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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from transformers import PreTrainedModel
try:
from .configuration_prism import PRISMConfig
except ImportError:
from configuration_prism import PRISMConfig
try:
from x_transformers import Encoder
except ImportError:
raise ImportError("To use PRISM, you must run: pip install x-transformers")
# --- UTILS ---
class ComplexDropout(nn.Module):
def __init__(self, p=0.5):
super().__init__()
self.p = p
def forward(self, z):
if not self.training or self.p == 0.0: return z
mask = torch.ones_like(z.real)
mask = F.dropout(mask, self.p, self.training, inplace=False)
return z * mask
class RobustPhaseNorm(nn.Module):
def __init__(self, d_model, eps=1e-5):
super().__init__()
self.scale = nn.Parameter(torch.ones(d_model))
self.eps = eps
def forward(self, x):
mag = torch.abs(x)
rms = torch.sqrt(torch.mean(mag**2, dim=-1, keepdim=True) + self.eps)
return (x / rms) * self.scale
class ModReLU(nn.Module):
def __init__(self, features):
super().__init__()
self.b = nn.Parameter(torch.zeros(features))
def forward(self, z):
mag = torch.abs(z)
new_mag = F.relu(mag + self.b)
phase = z / (mag + 1e-6)
return new_mag * phase
class ComplexToRealBridge(nn.Module):
def __init__(self, d_model):
super().__init__()
self.proj = nn.Linear(d_model * 2, d_model)
self.norm = nn.LayerNorm(d_model)
def forward(self, x_complex):
cat = torch.cat([x_complex.real, x_complex.imag], dim=-1)
return self.norm(self.proj(cat))
# --- COMPONENTS ---
class DynamicRoSE(nn.Module):
def __init__(self, num_embeddings, embedding_dim, max_period=10000.0):
super().__init__()
self.embedding_dim = embedding_dim
self.raw_embedding = nn.Embedding(num_embeddings, embedding_dim)
self.adapter = nn.Linear(embedding_dim, embedding_dim * 2)
freqs = torch.exp(torch.arange(0, embedding_dim, dtype=torch.float32) * -(math.log(max_period) / embedding_dim))
self.register_buffer('freqs', freqs)
self.rotation_predictor = nn.Linear(embedding_dim, embedding_dim * 2)
def forward(self, input_ids):
real_base = self.raw_embedding(input_ids)
B, L, D = real_base.shape
complex_params = self.adapter(real_base)
z_t = torch.complex(complex_params[..., :D], complex_params[..., D:])
rot_raw = self.rotation_predictor(real_base)
rot_x, rot_y = rot_raw.chunk(2, dim=-1)
rot_mag = torch.sqrt(rot_x**2 + rot_y**2 + 1e-6)
dynamic_rot = torch.complex(rot_x / rot_mag, rot_y / rot_mag)
pos = torch.arange(L, device=input_ids.device).float()
static_angles = torch.outer(pos, self.freqs)
static_rot = torch.polar(torch.ones_like(static_angles), static_angles)
z_final = z_t * static_rot.unsqueeze(0) * dynamic_rot
return z_final, real_base
class HyenaNeuralFilter(nn.Module):
def __init__(self, d_model, max_len=1024, hidden_dim=64):
super().__init__()
self.d_model = d_model
freqs = torch.exp(torch.arange(0, hidden_dim, 2, dtype=torch.float32) * -(math.log(10000.0) / hidden_dim))
self.register_buffer("freqs", freqs)
self.mlp = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim), nn.SiLU(),
nn.Linear(hidden_dim, hidden_dim), nn.SiLU(),
nn.Linear(hidden_dim, d_model * 2)
)
def forward(self, L, device):
t = torch.linspace(0, 1, steps=L, device=device).unsqueeze(-1)
emb = torch.cat([torch.sin(t * self.freqs), torch.cos(t * self.freqs)], dim=-1)
out = self.mlp(emb).view(L, self.d_model, 2)
return torch.complex(out[..., 0], out[..., 1])
class GatedHarmonicConvolution(nn.Module):
def __init__(self, d_model, max_len=1024, dropout=0.1, hidden_dim=64):
super().__init__()
self.d_model = d_model
self.filter_len = max_len
self.neural_filter = HyenaNeuralFilter(d_model, max_len=max_len, hidden_dim=hidden_dim)
self.gate_proj = nn.Linear(d_model * 2, d_model * 2)
self.mix_real = nn.Linear(d_model, d_model)
self.mix_imag = nn.Linear(d_model, d_model)
self.out_real = nn.Linear(d_model, d_model)
self.out_imag = nn.Linear(d_model, d_model)
self.activation = ModReLU(d_model)
self.norm = RobustPhaseNorm(d_model)
self.dropout = ComplexDropout(dropout)
def forward(self, x, src_mask=None):
residual = x
x_norm = self.norm(x)
if src_mask is not None:
x_norm = x_norm.masked_fill(src_mask.unsqueeze(-1), 0.0)
B, L, D = x_norm.shape
eff_L = min(L, self.filter_len)
x_freq = torch.fft.fft(x_norm, n=eff_L, dim=1, norm='ortho')
h = self.neural_filter(eff_L, x.device).unsqueeze(0)
x_filtered = x_freq * h
x_time = torch.fft.ifft(x_filtered, n=eff_L, dim=1, norm='ortho')
if L > eff_L: x_time = F.pad(x_time, (0,0,0,L-eff_L))
else: x_time = x_time[:, :L, :]
gates = torch.sigmoid(self.gate_proj(torch.cat([x_norm.real, x_norm.imag], dim=-1)))
g_r, g_i = gates.chunk(2, dim=-1)
x_gated = torch.complex(x_time.real * g_r, x_time.imag * g_i)
mr, mi = self.mix_real, self.mix_imag
x_mixed = torch.complex(mr(x_gated.real) - mi(x_gated.imag), mr(x_gated.imag) + mi(x_gated.real))
x_act = self.activation(x_mixed)
or_, oi = self.out_real, self.out_imag
out = torch.complex(or_(x_act.real) - oi(x_act.imag), or_(x_act.imag) + oi(x_act.real))
return self.dropout(out) + residual
class PRISMEncoder(nn.Module):
def __init__(self, num_layers, d_model, max_len, dropout=0.1, hidden_dim=64):
super().__init__()
self.layers = nn.ModuleList([
GatedHarmonicConvolution(d_model, max_len, dropout, hidden_dim)
for _ in range(num_layers)
])
self.final_norm = RobustPhaseNorm(d_model)
def forward(self, x, src_mask=None):
for layer in self.layers:
if self.training: x = torch.utils.checkpoint.checkpoint(layer, x, src_mask, use_reentrant=False)
else: x = layer(x, src_mask)
return self.final_norm(x)
# --- MAIN MODEL ---
class PRISM_WikiText_Model(PreTrainedModel):
config_class = PRISMConfig
def __init__(self, config):
super().__init__(config)
self.config = config
self.d_model = config.d_model
# 1. PRISM Core
self.rose = DynamicRoSE(config.vocab_size, config.d_model)
self.prism_encoder = PRISMEncoder(
config.prism_depth,
config.d_model,
max_len=config.seq_len,
dropout=config.dropout,
hidden_dim=config.fft_dim
)
self.bridge = ComplexToRealBridge(config.d_model)
self.periscope_proj = nn.Sequential(
nn.Linear(config.d_model * 2, config.d_model),
nn.LayerNorm(config.d_model),
nn.GELU()
)
# 2. Refiner (Standard Transformer + RoPE)
if config.trans_depth > 0:
self.refiner = Encoder(
dim=config.d_model,
depth=config.trans_depth,
heads=8,
rotary_pos_emb=True,
attn_flash=True,
attn_dropout=config.dropout,
ff_dropout=config.dropout,
)
else:
self.refiner = None
# 3. Output
self.lm_head = nn.Linear(config.d_model, config.vocab_size)
self.lm_head.weight = self.rose.raw_embedding.weight
def forward(self, input_ids, labels=None):
# A. Wave Physics
wave_src, particle_src = self.rose(input_ids)
wave_out = self.prism_encoder(wave_src)
wave_real = self.bridge(wave_out)
# B. Interface
mixed_memory = self.periscope_proj(torch.cat([wave_real, particle_src], dim=-1))
# C. Digital Refinement
if self.refiner:
out = self.refiner(mixed_memory)
else:
out = mixed_memory
logits = self.lm_head(out)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
return {"loss": loss, "logits": logits}
return logits
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