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d730d2e | 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 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Optional
from config import NexusConfig
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
rms = torch.rsqrt(x.float().pow(2).mean(-1, keepdim=True) + self.eps)
return (x.float() * rms * self.weight.float()).type_as(x)
def precompute_freqs_cis(config: NexusConfig) -> torch.Tensor:
dim = config.dim // config.num_heads
freqs = 1.0 / (config.rope_theta ** (torch.arange(0, dim, 2).float() / dim))
t = torch.arange(config.max_seq_len)
freqs = torch.outer(t, freqs)
return torch.polar(torch.ones_like(freqs), freqs)
class RotaryEmbedding(nn.Module):
def __init__(self, config: NexusConfig):
super().__init__()
self.freqs_cis = precompute_freqs_cis(config)
def forward(self, x: torch.Tensor, start_pos: int = 0):
_, seq_len, _, head_dim = x.shape
freqs_cis = self.freqs_cis[start_pos:start_pos+seq_len, :head_dim//2].to(x.device)
freqs_cis = freqs_cis.view(1, seq_len, 1, head_dim//2)
x_shaped = x.float().reshape(*x.shape[:-1], -1, 2)
x_complex = torch.complex(x_shaped[..., 0], x_shaped[..., 1])
x_rotated = x_complex * freqs_cis
x_out = torch.stack([x_rotated.real, x_rotated.imag], dim=-1).reshape_as(x_shaped)
return x_out.reshape_as(x).type_as(x)
class Attention(nn.Module):
def __init__(self, config: NexusConfig):
super().__init__()
self.num_heads = config.num_heads
self.num_kv_heads = config.num_kv_heads
if self.num_kv_heads is None:
self.num_kv_heads = config.num_heads
self.head_dim = config.dim // config.num_heads
self.num_kv_groups = config.num_heads // self.num_kv_heads
self.wq = nn.Linear(config.dim, config.dim, bias=False)
self.wk = nn.Linear(config.dim, self.head_dim * self.num_kv_heads, bias=False)
self.wv = nn.Linear(config.dim, self.head_dim * self.num_kv_heads, bias=False)
self.wo = nn.Linear(config.dim, config.dim, bias=False)
self.rotary = RotaryEmbedding(config)
def forward(self, x: torch.Tensor, start_pos: int = 0, mask: Optional[torch.Tensor] = None):
bsz, seqlen, _ = x.shape
q = self.wq(x).view(bsz, seqlen, self.num_heads, self.head_dim).transpose(1, 2)
k = self.wk(x).view(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
v = self.wv(x).view(bsz, seqlen, self.num_kv_heads, self.head_dim).transpose(1, 2)
q = self.rotary(q, start_pos)
k = self.rotary(k, start_pos)
if self.num_kv_groups > 1:
k = k[:, :, None, :, :].expand(bsz, self.num_kv_heads, self.num_kv_groups, seqlen, self.head_dim)
k = k.reshape(bsz, self.num_heads, seqlen, self.head_dim)
v = v[:, :, None, :, :].expand(bsz, self.num_kv_heads, self.num_kv_groups, seqlen, self.head_dim)
v = v.reshape(bsz, self.num_heads, seqlen, self.head_dim)
scale = 1.0 / math.sqrt(self.head_dim)
attn_weights = torch.matmul(q, k.transpose(-2, -1)) * scale
if mask is not None:
attn_weights = attn_weights + mask
attn_weights = F.softmax(attn_weights.float(), dim=-1).type_as(q)
attn_output = torch.matmul(attn_weights, v)
attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
return self.wo(attn_output)
class FeedForward(nn.Module):
def __init__(self, config: NexusConfig):
super().__init__()
hidden_dim = int(2 * config.ff_dim / 3)
hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
self.w1 = nn.Linear(config.dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, config.dim, bias=False)
self.w3 = nn.Linear(config.dim, hidden_dim, bias=False)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class TransformerBlock(nn.Module):
def __init__(self, config: NexusConfig):
super().__init__()
self.attention = Attention(config)
self.feed_forward = FeedForward(config)
self.attention_norm = RMSNorm(config.dim, config.norm_eps)
self.ff_norm = RMSNorm(config.dim, config.norm_eps)
def forward(self, x: torch.Tensor, start_pos: int = 0, mask: Optional[torch.Tensor] = None):
h = x + self.attention(self.attention_norm(x), start_pos, mask)
out = h + self.feed_forward(self.ff_norm(h))
return out
class Nexus(nn.Module):
def __init__(self, config: NexusConfig):
super().__init__()
self.config = config
self.token_embeddings = nn.Embedding(config.vocab_size, config.dim)
self.layers = nn.ModuleList([TransformerBlock(config) for _ in range(config.num_layers)])
self.norm = RMSNorm(config.dim, config.norm_eps)
self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
self.token_embeddings.weight = self.output.weight
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, input_ids: torch.Tensor, start_pos: int = 0):
_, seqlen = input_ids.shape
mask = torch.full((1, 1, seqlen, start_pos + seqlen), float('-inf'),
dtype=torch.float32, device=input_ids.device)
mask = torch.triu(mask, diagonal=start_pos + 1).type_as(input_ids)
x = self.token_embeddings(input_ids)
for layer in self.layers:
x = layer(x, start_pos, mask)
x = self.norm(x)
logits = self.output(x)
return logits
def generate(self, input_ids: torch.Tensor, max_new_tokens: int,
temperature: float = 0.7, top_k: int = 50, top_p: float = 0.9):
self.eval()
generated = []
for _ in range(max_new_tokens):
seq_len = input_ids.shape[1]
if seq_len > self.config.max_seq_len:
input_ids = input_ids[:, -self.config.max_seq_len:]
with torch.no_grad():
logits = self(input_ids, 0)
logits = logits[:, -1, :] / temperature
if top_k > 0:
top_k_values, _ = torch.topk(logits, top_k)
min_top_k = top_k_values[:, -1].unsqueeze(-1)
logits = torch.where(logits < min_top_k,
torch.full_like(logits, float('-inf')), logits)
if top_p > 0:
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[:, 0] = False
indices_to_remove = torch.zeros_like(logits, dtype=torch.bool)
indices_to_remove = indices_to_remove.scatter(1, sorted_indices,
sorted_indices_to_remove)
logits = torch.where(indices_to_remove,
torch.full_like(logits, float('-inf')), logits)
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
input_ids = torch.cat([input_ids, next_token], dim=-1)
generated.append(next_token.item())
return generated, input_ids
def create_nexus_model():
from config import nexus_config
config = nexus_config
model = Nexus(config)
total_params = sum(p.numel() for p in model.parameters())
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"[Nexus SmAll] Model created with {total_params/1e6:.1f}M parameters "
f"({trainable_params/1e6:.1f}M trainable)")
return model, config |