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from typing import List, Tuple
from dataclasses import dataclass
import torch
import torch.nn as nn
import torch.nn.functional as F
# ---------------------------------------------------------------------------
# Utilities
# ---------------------------------------------------------------------------
def _trunc_normal_init(tensor: torch.Tensor, std: float = 1.0,
lower: float = -2.0, upper: float = 2.0) -> torch.Tensor:
with torch.no_grad():
if std == 0:
tensor.zero_()
else:
sqrt2 = math.sqrt(2)
a = math.erf(lower / sqrt2)
b = math.erf(upper / sqrt2)
z = (b - a) / 2
c = (2 * math.pi) ** -0.5
pdf_u = c * math.exp(-0.5 * lower ** 2)
pdf_l = c * math.exp(-0.5 * upper ** 2)
comp_std = std / math.sqrt(
1 - (upper * pdf_u - lower * pdf_l) / z
- ((pdf_u - pdf_l) / z) ** 2
)
tensor.uniform_(a, b)
tensor.erfinv_()
tensor.mul_(sqrt2 * comp_std)
tensor.clip_(lower * comp_std, upper * comp_std)
return tensor
def _find_multiple(a: int, b: int) -> int:
return (-(a // -b)) * b
def rms_norm(hidden_states: torch.Tensor, variance_epsilon: float) -> torch.Tensor:
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.square().mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + variance_epsilon)
return hidden_states.to(input_dtype)
class CastedLinear(nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool):
super().__init__()
self.weight = nn.Parameter(
_trunc_normal_init(torch.empty(out_features, in_features),
std=1.0 / (in_features ** 0.5))
)
self.bias = nn.Parameter(torch.zeros(out_features)) if bias else None
def forward(self, x: torch.Tensor) -> torch.Tensor:
return F.linear(
x, self.weight.to(x.dtype),
bias=self.bias.to(x.dtype) if self.bias is not None else None,
)
class SwiGLU(nn.Module):
def __init__(self, hidden_size: int, expansion: float):
super().__init__()
inter = _find_multiple(round(expansion * hidden_size * 2 / 3), 256)
self.gate_up_proj = CastedLinear(hidden_size, inter * 2, bias=False)
self.down_proj = CastedLinear(inter, hidden_size, bias=False)
def forward(self, x):
gate, up = self.gate_up_proj(x).chunk(2, dim=-1)
return self.down_proj(F.silu(gate) * up)
# ---------------------------------------------------------------------------
# RTM block: token-mixing MLP + channel-mixing MLP with RMSNorm residuals
# ---------------------------------------------------------------------------
class RTMBlock(nn.Module):
def __init__(self, hidden_size: int, seq_len: int, expansion: float,
rms_norm_eps: float = 1e-5):
super().__init__()
self.mlp_t = SwiGLU(hidden_size=seq_len, expansion=expansion)
self.mlp = SwiGLU(hidden_size=hidden_size, expansion=expansion)
self.norm_eps = rms_norm_eps
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = hidden_states.transpose(1, 2)
hidden_states = rms_norm(
hidden_states + self.mlp_t(hidden_states),
variance_epsilon=self.norm_eps,
)
hidden_states = hidden_states.transpose(1, 2)
hidden_states = rms_norm(
hidden_states + self.mlp(hidden_states),
variance_epsilon=self.norm_eps,
)
return hidden_states
class RTMReasoningModule(nn.Module):
def __init__(self, layers: List[RTMBlock]):
super().__init__()
self.layers = nn.ModuleList(layers)
def forward(self, hidden_states: torch.Tensor,
input_injection: torch.Tensor) -> torch.Tensor:
hidden_states = hidden_states + input_injection
for layer in self.layers:
hidden_states = layer(hidden_states)
return hidden_states
def _make_level(num_layers: int, hidden_size: int, seq_len: int,
expansion: float, rms_norm_eps: float) -> RTMReasoningModule:
return RTMReasoningModule([
RTMBlock(hidden_size, seq_len, expansion, rms_norm_eps)
for _ in range(num_layers)
])
# ---------------------------------------------------------------------------
# Recursive H/L cycle loop
# ---------------------------------------------------------------------------
@dataclass
class RTMCarry:
z_H: torch.Tensor
z_L: torch.Tensor
class RTMInner(nn.Module):
def __init__(self, hidden_size: int, expansion: float,
H_cycles: int, L_cycles: int, H_layers: int, L_layers: int,
num_tokens: int,
with_grad: bool = False,
cycle_noise_std: float = 0.0,
rms_norm_eps: float = 1e-5,
forward_dtype: str = "float32"):
super().__init__()
self.hidden_size = hidden_size
self.H_cycles = H_cycles
self.L_cycles = L_cycles
self.with_grad = with_grad
self.cycle_noise_std = max(0.0, float(cycle_noise_std))
self.forward_dtype = getattr(torch, forward_dtype)
self.total_seq_len = max(1, int(num_tokens))
self.L_level = _make_level(L_layers, hidden_size, self.total_seq_len,
expansion, rms_norm_eps)
self.H_init = nn.Buffer(
_trunc_normal_init(torch.empty(hidden_size, dtype=self.forward_dtype), std=1),
persistent=True,
)
self.L_init = nn.Parameter(
_trunc_normal_init(torch.empty(hidden_size, dtype=self.forward_dtype), std=1)
)
def empty_carry(self, batch_size: int, device=None) -> RTMCarry:
if device is None:
device = self.H_init.device
return RTMCarry(
z_H=self.H_init.unsqueeze(0).unsqueeze(0).expand(
batch_size, self.total_seq_len, -1),
z_L=self.L_init.unsqueeze(0).unsqueeze(0).expand(
batch_size, self.total_seq_len, -1),
)
def forward(self, carry: RTMCarry, z_H_init: torch.Tensor
) -> Tuple[RTMCarry, torch.Tensor, List[torch.Tensor]]:
z_H, z_L = carry.z_H, carry.z_L
intermediates: List[torch.Tensor] = []
if self.with_grad:
for _ in range(self.H_cycles):
for _ in range(self.L_cycles):
z_L = self.L_level(z_L, z_H + z_H_init)
z_H = self.L_level(z_H, z_L)
if self.training and self.cycle_noise_std > 0:
z_H = z_H + torch.randn_like(z_H) * self.cycle_noise_std
intermediates.append(z_H)
else:
with torch.no_grad():
for _ in range(self.H_cycles - 1):
for _ in range(self.L_cycles):
z_L = self.L_level(z_L, z_H + z_H_init)
z_H = self.L_level(z_H, z_L)
if self.training and self.cycle_noise_std > 0:
z_H = z_H + torch.randn_like(z_H) * self.cycle_noise_std
intermediates.append(z_H)
for _ in range(self.L_cycles):
z_L = self.L_level(z_L, z_H + z_H_init)
z_H = self.L_level(z_H, z_L)
if self.training and self.cycle_noise_std > 0:
z_H = z_H + torch.randn_like(z_H) * self.cycle_noise_std
intermediates.append(z_H)
new_carry = RTMCarry(z_H=z_H.detach(), z_L=z_L.detach())
return new_carry, z_H, intermediates
class _PixelNorm(nn.Module):
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x * (x.square().mean(dim=1, keepdim=True) + 1e-8).rsqrt()
class _EqualLinear(nn.Module):
def __init__(self, in_dim: int, out_dim: int):
super().__init__()
self.linear = nn.Linear(in_dim, out_dim)
self.linear.bias.data.zero_()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.linear(x)
def _coerce_noise_2d(input_tensor: torch.Tensor, code_dim: int, mapper_name: str) -> torch.Tensor:
"""Accept [B, D] or [B, 1, D] and return [B, D]."""
if input_tensor.dim() == 2 and input_tensor.shape[1] == code_dim:
return input_tensor
if input_tensor.dim() == 3 and input_tensor.shape[1] == 1 and input_tensor.shape[2] == code_dim:
return input_tensor.squeeze(1)
raise ValueError(
f"{mapper_name} expected input shape [B, {code_dim}] or [B, 1, {code_dim}], "
f"got {tuple(input_tensor.shape)}"
)
class RTMMappingNetwork(nn.Module):
def __init__(self, code_dim: int,
num_tokens: int = 1,
H_cycles: int = 1, L_cycles: int = 1,
H_layers: int = 2, L_layers: int = 2,
hidden_size: int = 256,
expansion: float = 4.0, refinement_steps: int = 1,
with_grad: bool = False,
cycle_noise_std: float = 0.0,
rms_norm_eps: float = 1e-5,
forward_dtype: str = "float32"):
super().__init__()
self.code_dim = code_dim
self.refinement_steps = max(1, refinement_steps)
total_seq_len = max(1, int(num_tokens))
if hidden_size <= 0:
assert code_dim % total_seq_len == 0, (
f"code_dim={code_dim} must be divisible by num_tokens={total_seq_len} "
f"when hidden_size is auto"
)
hidden_size = code_dim // total_seq_len
self.hidden_size = hidden_size
self.trm = RTMInner(
hidden_size=hidden_size,
expansion=expansion,
H_cycles=H_cycles,
L_cycles=L_cycles,
H_layers=H_layers,
L_layers=L_layers,
num_tokens=num_tokens,
with_grad=with_grad,
cycle_noise_std=cycle_noise_std,
rms_norm_eps=rms_norm_eps,
forward_dtype=forward_dtype,
)
seq_len = self.trm.total_seq_len
capacity = seq_len * hidden_size
self.pixel_norm = _PixelNorm()
self.mapper_direct = (capacity == code_dim)
if not self.mapper_direct:
self.z_to_seq = _EqualLinear(code_dim, capacity)
self.seq_to_w = _EqualLinear(capacity, code_dim)
def _z_H_to_w(self, z_H: torch.Tensor) -> torch.Tensor:
flat = z_H.flatten(start_dim=1)
if self.mapper_direct:
return flat
return self.seq_to_w(flat)
def forward(self, input):
if isinstance(input, (list, tuple)):
input = input[0]
input = _coerce_noise_2d(input, self.code_dim, self.__class__.__name__)
B = input.shape[0]
seq_len = self.trm.total_seq_len
z_norm = self.pixel_norm(input)
if self.mapper_direct:
z_seq = z_norm.view(B, seq_len, self.hidden_size)
else:
z_seq = self.z_to_seq(z_norm).view(B, seq_len, self.hidden_size)
carry = self.trm.empty_carry(B, device=input.device)
for _ in range(self.refinement_steps):
carry, z_H_out, _ = self.trm(carry, z_H_init=z_seq)
return [self._z_H_to_w(z_H_out)]
def forward_w_trajectory(self, input):
"""Decode every intermediate H state into a style vector.
Returns ``[w_proj, w_after_cycle_1, ...]`` so callers can visualize
how w is refined across the H/L cycles.
"""
if isinstance(input, (list, tuple)):
input = input[0]
input = _coerce_noise_2d(input, self.code_dim, self.__class__.__name__)
B = input.shape[0]
seq_len = self.trm.total_seq_len
z_norm = self.pixel_norm(input)
if self.mapper_direct:
z_seq = z_norm.view(B, seq_len, self.hidden_size)
else:
z_seq = self.z_to_seq(z_norm).view(B, seq_len, self.hidden_size)
trajectory = [self._z_H_to_w(z_seq)]
carry = self.trm.empty_carry(B, device=input.device)
for _ in range(self.refinement_steps):
carry, _z_H_out, intermediates = self.trm(carry, z_H_init=z_seq)
for zh in intermediates:
trajectory.append(self._z_H_to_w(zh))
return trajectory
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