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AETHER Hugging Face Pretrained Model Implementation
Post-Transformer Foundation Model: Unitary Phasor Gauge-Scan + Symplectic Leapfrog Deliberator
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple, Union
class UnitaryPhasorAssociativeFlow(nn.Module):
def __init__(self, d_model: int, d_mem: int):
super().__init__()
self.d_model = d_model
self.d_mem = d_mem
self.w_theta = nn.Linear(d_model, d_mem, bias=False)
self.w_v = nn.Linear(d_model, d_mem, bias=False)
self.w_gate_forget = nn.Linear(d_model, d_mem, bias=True)
self.w_gate_input = nn.Linear(d_model, d_mem, bias=True)
self.w_q = nn.Linear(d_model, d_mem, bias=False)
self.w_out = nn.Linear(d_mem, d_model, bias=False)
def forward(self, x: torch.Tensor, prev_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
B, T, D = x.shape
thetas = 2.0 * math.pi * torch.sigmoid(self.w_theta(x))
k_cos = torch.cos(thetas)
k_sin = torch.sin(thetas)
v = self.w_v(x)
alpha = torch.sigmoid(self.w_gate_forget(x))
beta = torch.sigmoid(self.w_gate_input(x))
q = self.w_q(x)
if prev_state is None:
z_r = torch.zeros(B, self.d_mem, device=x.device, dtype=x.dtype)
z_i = torch.zeros(B, self.d_mem, device=x.device, dtype=x.dtype)
else:
z_r, z_i = prev_state
outputs = []
for t in range(T):
u_r = v[:, t, :] * k_cos[:, t, :]
u_i = -v[:, t, :] * k_sin[:, t, :]
z_r = alpha[:, t, :] * z_r + beta[:, t, :] * u_r
z_i = alpha[:, t, :] * z_i + beta[:, t, :] * u_i
q_t = q[:, t, :]
y_t = z_r * q_t
outputs.append(y_t.unsqueeze(1))
y_seq = torch.cat(outputs, dim=1)
out = self.w_out(y_seq)
return out, (z_r, z_i)
class SymplecticHamiltonianDeliberator(nn.Module):
def __init__(self, d_model: int, d_latent: int, dt_init: float = 0.05, beta_init: float = 2.0):
super().__init__()
self.d_model = d_model
self.d_latent = d_latent
self.proj_in = nn.Linear(d_model, d_latent)
self.w_attractors = nn.Parameter(torch.randn(32, d_latent) / math.sqrt(d_latent))
self.w_h_coupling = nn.Linear(d_latent, d_latent, bias=False)
self.log_dt = nn.Parameter(torch.tensor(math.log(dt_init)))
self.log_beta = nn.Parameter(torch.tensor(math.log(beta_init)))
self.proj_out = nn.Linear(d_latent, d_model)
def _compute_potential_force(self, q: torch.Tensor, h: torch.Tensor) -> torch.Tensor:
beta = torch.exp(self.log_beta)
logits = torch.matmul(q, self.w_attractors.T) * beta
weights = F.softmax(logits, dim=-1)
grad_v_attractors = -torch.matmul(weights, self.w_attractors)
grad_v_coupling = q - self.w_h_coupling(h)
return -(grad_v_attractors + grad_v_coupling)
def forward(self, x: torch.Tensor, steps: int = 4):
h = self.proj_in(x)
q = h.clone()
p = torch.zeros_like(q)
dt = torch.exp(self.log_dt).clamp(1e-3, 0.5)
for _ in range(steps):
f_q = self._compute_potential_force(q, h)
p_half = p + 0.5 * dt * f_q
q_next = q + dt * p_half
f_q_next = self._compute_potential_force(q_next, h)
p = p_half + 0.5 * dt * f_q_next
q = q_next
out = self.proj_out(q)
return out
class AETHERBlock(nn.Module):
def __init__(self, d_model: int, d_mem: int, d_latent: int, d_ff: int, dt_init: float = 0.05, beta_init: float = 2.0):
super().__init__()
self.flow = UnitaryPhasorAssociativeFlow(d_model, d_mem)
self.deliberator = SymplecticHamiltonianDeliberator(d_model, d_latent, dt_init, beta_init)
self.ffn = nn.Sequential(
nn.Linear(d_model, d_ff),
nn.GELU(),
nn.Linear(d_ff, d_model)
)
self.norm1 = nn.LayerNorm(d_model)
self.norm2 = nn.LayerNorm(d_model)
self.norm3 = nn.LayerNorm(d_model)
def forward(self, x: torch.Tensor, steps: int = 4, prev_state: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
flow_out, state = self.flow(self.norm1(x), prev_state=prev_state)
x = x + flow_out
delib_out = self.deliberator(self.norm2(x), steps=steps)
x = x + delib_out
ff_out = self.ffn(self.norm3(x))
x = x + ff_out
return x, state
class AetherForCausalLM(nn.Module):
"""
Standard Hugging Face compatible causal language model for AETHER.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.token_embeddings = nn.Embedding(config.vocab_size, config.d_model)
self.blocks = nn.ModuleList([
AETHERBlock(
d_model=config.d_model,
d_mem=config.d_mem,
d_latent=config.d_latent,
d_ff=config.d_ff,
dt_init=config.dt_init,
beta_init=config.beta_init
)
for _ in range(config.n_layers)
])
self.final_norm = nn.LayerNorm(config.d_model)
self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)
self.lm_head.weight = self.token_embeddings.weight
def forward(
self,
input_ids: torch.Tensor,
labels: Optional[torch.Tensor] = None,
steps: Optional[int] = None,
**kwargs
):
num_steps = steps if steps is not None else self.config.default_steps
x = self.token_embeddings(input_ids)
for block in self.blocks:
x, _ = block(x, steps=num_steps)
x = self.final_norm(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
loss = F.cross_entropy(
logits.view(-1, self.config.vocab_size),
labels.view(-1),
ignore_index=self.config.pad_token_id
)
return {"loss": loss, "logits": logits}
@torch.no_grad()
def generate(
self,
input_ids: torch.Tensor,
max_new_tokens: int = 50,
temperature: float = 0.7,
top_k: int = 40,
**kwargs
) -> torch.Tensor:
curr_ids = input_ids.clone()
for _ in range(max_new_tokens):
out = self.forward(curr_ids)
logits = out["logits"][:, -1, :] / max(1e-5, 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_token = torch.multinomial(probs, num_samples=1)
curr_ids = torch.cat([curr_ids, next_token], dim=1)
return curr_ids
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