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"""TAMELM model definitions: routed attention, depth attention, and dropless MoE.
This module stays self-contained for notebook execution and portable Hugging Face
exports. Definitions are ordered from configuration and low-level operations to
model components, cache/output interfaces, and the complete language model.
Routing uses shared GQA projections and original top-k softmax probabilities.
Tile routing and graph selection are causal; cached decoding retains tile state.
CUDA FlexAttention and Triton grouped GEMM are optional acceleration backends.
"""
from __future__ import annotations
import ast as _ast
import inspect as _inspect
import linecache as _linecache
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import (
AutoConfig,
AutoModelForCausalLM,
GenerationMixin,
PretrainedConfig,
PreTrainedModel,
)
from transformers.cache_utils import Cache
from transformers.utils import ModelOutput
try:
from torch.nn.attention.flex_attention import (
BlockMask,
create_block_mask,
flex_attention,
)
HAS_FLEX = True
except ImportError: # pragma: no cover
HAS_FLEX = False
try:
import triton # type: ignore
import triton.language as tl # type: ignore
HAS_TRITON = True
except ImportError:
HAS_TRITON = False
THIRD_PARTY_NOTICES = """Position adapters informed by zetascale 2.8.8, https://github.com/kyegomez/zeta.
Modified for causal rectangular reads, explicit offsets, portable initialization,
and efficient unique-distance evaluation. These are local adaptations, not direct Zeta imports.
RelativePositionBias source notice: Copyright (c) 2022 Agora. MIT License.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
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"""
# Notebook source capture and semantic fingerprints.
# Capture source independently of execution counts, paths, and timestamps.
_NOTEBOOK_SOURCES = globals().get("_NOTEBOOK_SOURCES", {})
# =============================================================================
# Source identity helpers
# =============================================================================
def _capture_cell_source(component):
caller = _inspect.currentframe().f_back
source = "".join(_linecache.getlines(caller.f_code.co_filename))
if not source:
try:
source = get_ipython().history_manager.input_hist_raw[-1]
except (NameError, AttributeError, IndexError):
raise RuntimeError(
"Cannot capture executed source. Run the model, tokenizer, and harness cells in Jupyter/Colab."
)
if not source.strip():
raise RuntimeError(
"Cannot capture model source; rerun the model definition cell."
)
_NOTEBOOK_SOURCES[component] = source
class _WithoutDocstrings(_ast.NodeTransformer):
def _strip(self, node):
self.generic_visit(node)
if (
node.body
and isinstance(node.body[0], _ast.Expr)
and isinstance(node.body[0].value, _ast.Constant)
and isinstance(node.body[0].value.value, str)
):
node.body = node.body[1:] or [_ast.Pass()]
return node
visit_Module = _strip
visit_ClassDef = _strip
visit_FunctionDef = _strip
visit_AsyncFunctionDef = _strip
def _semantic_source(source):
return _ast.dump(
_WithoutDocstrings().visit(_ast.parse(source)), include_attributes=False
)
# Routing mechanism IDs are part of the attention/cache contract.
GLOBAL, LOCAL, STRIDED, GRAPH = 0, 1, 2, 3
MECH_NAMES = ("global", "local", "strided", "graph")
# =============================================================================
# Configuration
# =============================================================================
@dataclass
class RoutedAttentionConfig:
d_model: int
n_q_heads: int = 16
n_kv_heads: int = 4
head_dim: Optional[int] = None
top_k: int = 2
tile: int = 128 # routing granularity == FlexAttention block size
local_window: int = 256 # tokens
strided_recent: int = 128 # tokens of dense recent context for 'strided'
strided_every: int = 4 # plus every S-th tile back (tile-relative)
graph_top_r: int = 0 # past tiles picked by 'graph'; 0 = legacy 3-way router
graph_recent: int = 128 # tokens of dense recent context for 'graph'
max_seq_len: int = 2048
router_init_std: float = 1e-2
balance_coef: float = 1e-2
backend: str = "flex" # "flex" | "dense"; decode/cached paths always dense
class TAMEConfig(PretrainedConfig):
"""Architecture settings grouped by backbone, attention, depth, and refinement.
Head widths must divide the model width; rotary widths must be even.
Defaults describe a fresh model. from_dict fills legacy checkpoint defaults.
Backend choices change execution; bank/head/refinement choices are experiments.
"""
model_type = "tamelm_two_axis"
attribute_map = {
"hidden_size": "embed_dim",
"num_hidden_layers": "num_layers",
"num_attention_heads": "n_q_heads",
"num_key_value_heads": "n_kv_heads",
"max_position_embeddings": "max_seq_len",
}
# Keys introduced after the alpha checkpoint, with the value that reproduces
# the older architecture when a saved config predates them.
_LEGACY_DEFAULTS = {
"graph_top_r": 0,
"init_scheme": "pilot",
"reasoning_mode": "none",
"reasoning_revisit_mode": "full",
"expert_banks": 1,
"expert_bank_top_k": 1,
"reasoning_position_bias": "none",
"add_sinusoidal_embeddings": False,
}
def __init__(
self,
# Backbone and expert capacity (see MODEL_CONFIGURATION.md).
vocab_size=44469,
embed_dim=768,
num_layers=12,
num_experts=8,
expert_top_k=2,
expert_hidden_dim=3072,
# Sequence attention and execution backends (see MODEL_CONFIGURATION.md).
n_q_heads=32,
n_kv_heads=4,
attention_top_k=2,
tile=128,
local_window=256,
strided_recent=128,
strided_every=4,
graph_top_r=2,
graph_recent=128,
attention_backend="flex",
expert_backend="grouped",
max_seq_len=2048,
# Same-token depth memory (see MODEL_CONFIGURATION.md).
depth_mode="attention",
depth_slots=4,
depth_dim=256,
depth_heads=4,
depth_gate_init=0.01,
# Loss, positions, and initialization (see MODEL_CONFIGURATION.md).
balance_coef=0.01,
router_z_loss_coef=0.0,
rope_type="warped",
router_init_std=0.01,
init_scheme="v1",
loss_chunk_size=256,
# Tokenizer and generation (see MODEL_CONFIGURATION.md).
pad_token_id=0,
bos_token_id=1,
eos_token_id=2,
use_cache=True,
# Refinement and bank capacity (see MODEL_CONFIGURATION.md).
reasoning_mode="fold_unfold", # "fold_unfold" | "none"
reasoning_slots=4,
reasoning_steps=3,
reasoning_heads=4,
reasoning_chunk_size=32,
reasoning_gate_init=0.1,
reasoning_initial_fold=True,
reasoning_direct_read=True,
reasoning_revisit_mode="full", # "full" | "local_only"
expert_banks=4,
expert_bank_top_k=2,
# Memory and optional position mechanisms (see MODEL_CONFIGURATION.md).
checkpoint_backbone=False,
reasoning_position_bias="none", # "none" | "relative" | "continuous"
add_sinusoidal_embeddings=False,
**kwargs,
):
kwargs.setdefault("tie_word_embeddings", True)
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
values = locals().copy()
for key, value in values.items():
if key not in {"self", "kwargs", "__class__", "values"}:
setattr(self, key, value)
if embed_dim % n_q_heads or (embed_dim // n_q_heads) % 2:
raise ValueError(
"Width must divide query heads with even rotary head dimension"
)
if depth_dim % depth_heads or depth_mode not in {"attention", "baseline"}:
raise ValueError("Invalid depth dimensions/mode")
if (
not 1 <= expert_top_k <= num_experts
or min(num_layers, depth_slots, loss_chunk_size) < 1
):
raise ValueError("Invalid expert/depth/loss dimensions")
if expert_backend not in {"reference", "grouped"} or rope_type not in {
"blackhole",
"standard",
"warped",
}:
raise ValueError("Unknown expert backend or position transform")
if not math.isfinite(router_z_loss_coef) or router_z_loss_coef < 0:
raise ValueError("router_z_loss_coef must be finite and nonnegative")
if graph_top_r < 0 or init_scheme not in {"v1", "pilot"} or router_init_std < 0:
raise ValueError("Invalid graph_top_r, init_scheme or router_init_std")
if not isinstance(reasoning_initial_fold, bool) or not isinstance(reasoning_direct_read, bool):
raise ValueError("Refinement branch switches must be booleans")
if reasoning_mode not in {"none", "fold_unfold"}:
raise ValueError("Unknown reasoning_mode")
if reasoning_revisit_mode not in {"full", "local_only"}:
raise ValueError("Unknown reasoning_revisit_mode")
if reasoning_position_bias not in {"none", "relative", "continuous"}:
raise ValueError("Unknown reasoning_position_bias")
if reasoning_mode == "none" and reasoning_position_bias != "none":
raise ValueError("reasoning_position_bias requires fold_unfold reasoning")
if (
min(reasoning_slots, reasoning_steps, reasoning_heads, reasoning_chunk_size)
< 1
or embed_dim % reasoning_heads
):
raise ValueError("Invalid reasoning dimensions/steps/chunk size")
if not 1 <= expert_bank_top_k <= expert_banks:
raise ValueError("expert_bank_top_k must be between 1 and expert_banks")
@classmethod
def from_dict(cls, config_dict, **kwargs):
config_dict = dict(config_dict)
for key, legacy in cls._LEGACY_DEFAULTS.items():
config_dict.setdefault(key, legacy)
return super().from_dict(config_dict, **kwargs)
# =============================================================================
# Shared routing precision
# =============================================================================
def router_logits_fp32(router, x):
"""Keep routing decisions out of AMP; casting rounded BF16 logits is too late."""
with torch.autocast(device_type=x.device.type, enabled=False):
return F.linear(
x.float(),
router.weight.float(),
None if router.bias is None else router.bias.float(),
)
# =============================================================================
# Position transforms
# =============================================================================
class ScaledSinusoidalEmbedding(nn.Module):
"""Zeta-style learned sinusoidal scale, with correct explicit/cache positions.
Adapted interface: frequencies are computed at use time for meta/HF loading;
supplied positions are respected rather than replaced. See bundled notices.
"""
def __init__(self, dim, theta=10000):
super().__init__()
if dim < 2 or dim % 2:
raise ValueError("Sinusoidal embedding width must be positive and even")
self.dim, self.theta = dim, theta
self.scale = nn.Parameter(torch.tensor(dim**-0.5))
def forward(self, x, pos=None, seq_start_pos=None, start_pos=0):
if pos is None:
pos = torch.arange(start_pos, start_pos + x.shape[1], device=x.device)
else:
pos = pos.to(x.device)
if seq_start_pos is not None:
pos = pos - seq_start_pos.to(x.device)[..., None]
inv = self.theta ** (
-torch.arange(self.dim // 2, device=x.device).float() / (self.dim // 2)
)
angles = pos.float()[..., None] * inv
return (torch.cat((angles.sin(), angles.cos()), dim=-1) * self.scale).to(
x.dtype
)
class RelativePositionBias(nn.Module):
"""Causal logarithmic distance buckets; rectangular cached reads supported.
Adapted from Zeta's RelativePositionBias (Copyright 2022 Agora, MIT).
Here forward returns [1, heads, queries, keys], ready for SDPA masks.
"""
def __init__(self, heads, num_buckets=32, max_distance=128):
super().__init__()
if num_buckets < 4 or max_distance <= num_buckets // 2:
raise ValueError("Invalid relative position buckets/distance")
self.num_buckets, self.max_distance = num_buckets, max_distance
self.relative_attention_bias = nn.Embedding(num_buckets, heads)
nn.init.zeros_(self.relative_attention_bias.weight)
def forward(self, qlen, klen, start_pos=0):
device = self.relative_attention_bias.weight.device
distance = (
torch.arange(start_pos, start_pos + qlen, device=device)[:, None]
- torch.arange(klen, device=device)[None, :]
).clamp_min(0)
exact = self.num_buckets // 2
large = (
exact
+ (
torch.log(distance.float().clamp_min(exact) / exact)
/ math.log(self.max_distance / exact)
* (self.num_buckets - exact)
).long()
)
bucket = torch.where(
distance < exact, distance, large.clamp_max(self.num_buckets - 1)
)
return self.relative_attention_bias(bucket).permute(2, 0, 1).unsqueeze(0)
class ContinuousPositionBias(nn.Module):
"""Zeta-inspired 1-D relative-bias MLP evaluated once per unique distance.
Uses signed log distances and rectangular query offsets. It does not allocate
an MLP activation for every query/key pair or depend on future token content.
"""
def __init__(self, dim, heads):
super().__init__()
self.net = nn.Sequential(
nn.Linear(1, dim, bias=False), nn.SiLU(), nn.Linear(dim, heads, bias=False)
)
def forward(self, qlen, klen, start_pos=0):
device = self.net[0].weight.device
minimum = start_pos - klen + 1
offsets = torch.arange(minimum, start_pos + qlen, device=device)
feature = offsets.float().sign() * offsets.float().abs().log1p()
values = self.net(feature[:, None])
distance = (
torch.arange(start_pos, start_pos + qlen, device=device)[:, None]
- torch.arange(klen, device=device)[None, :]
)
return values[distance - minimum].permute(2, 0, 1).unsqueeze(0)
class RotaryPos(nn.Module):
"""Standard RoPE with interleaved pairs (same pairing convention as BlackHoleRoPE)."""
def __init__(self, head_dim: int, base: float = 10_000.0):
super().__init__()
half = head_dim // 2
self.register_buffer(
"inv", 1.0 / base ** (torch.arange(half).float() / half), persistent=False
)
def forward(self, x, start_pos: int = 0, **_): # x: [B, H, T, dh]
T = x.shape[2]
t = torch.arange(start_pos, start_pos + T, device=x.device, dtype=torch.float32)
ang = t[:, None] * self.inv[None]
cos, sin = ang.cos(), ang.sin()
xf = x.float()
x1, x2 = xf[..., 0::2], xf[..., 1::2]
return (
torch.stack([x1 * cos - x2 * sin, x1 * sin + x2 * cos], -1)
.flatten(-2)
.to(x.dtype)
)
class WarpedRoPE(nn.Module):
"""RoPE on a content-defined clock, one clock per KV group.
tau_t = softplus(w . x_t + b) > 0 clock rate of token t
T_t = sum_{s<t} tau_s token time (exclusive prefix sum)
angle = omega_f * T_t
q_m . k_n then depends on T_m - T_n = sum_{n<=s<m} tau_s: only the tokens between
them, so attention is relative in warped time and independent of where a document
sits in a packed window. Causal, cumsum-parallel, O(1) decode state (the running
time). At init (w = 0, softplus(b) = 1) it is exactly standard RoPE.
"""
def __init__(
self,
d_model: int,
head_dim: int,
n_q_heads: int,
n_kv_heads: int,
base: float = 10_000.0,
):
super().__init__()
if head_dim % 2 or n_q_heads % n_kv_heads:
raise ValueError(
"WarpedRoPE needs an even head_dim and grouped query heads"
)
self.half, self.base = head_dim // 2, float(base)
self.group = n_q_heads // n_kv_heads
self.rate = nn.Linear(d_model, n_kv_heads)
self.reset_parameters()
def reset_parameters(self):
nn.init.zeros_(self.rate.weight)
nn.init.constant_(self.rate.bias, math.log(math.e - 1.0)) # softplus(b) = 1
def clock(self, x, t0=None):
"""x [B,T,d] (normed). t0 [B,Hkv] time already elapsed, or None.
Returns token times [B,T,Hkv] and inclusive times [B,T,Hkv] (cache state)."""
tau = F.softplus(router_logits_fp32(self.rate, x)) # fp32
inclusive = tau.cumsum(1)
if t0 is not None:
inclusive = inclusive + t0[:, None]
return inclusive - tau, inclusive
def rotate(self, x, times): # x [B,h,T,dh]; times [B,h,T]
# Frequencies are recomputed rather than buffered: nothing here can be left
# uninitialized by meta-device / no_init_weights loading.
inv = 1.0 / self.base ** (
torch.arange(self.half, device=x.device, dtype=torch.float32) / self.half
)
ang = times[..., None] * inv
cos, sin = ang.cos(), ang.sin()
xf = x.float()
x1, x2 = xf[..., 0::2], xf[..., 1::2]
return (
torch.stack([x1 * cos - x2 * sin, x1 * sin + x2 * cos], -1)
.flatten(-2)
.to(x.dtype)
)
class BlackHoleRoPE(nn.Module):
def __init__(
self,
dim,
max_seq_len=2048,
base_vortex_strength=1.0,
energy_amplification=0.1,
theta_base=10_000.0,
adaptive_vortex=True,
discrepancy_modulation=True,
):
super().__init__()
if dim <= 0 or dim % 2 or max_seq_len < 1:
raise ValueError(
"BlackHoleRoPE requires an even dimension and positive max_seq_len"
)
self.dim = int(dim)
self.half_dim = dim // 2
self.max_seq_len = int(max_seq_len)
self.energy_amplification = float(energy_amplification)
self.discrepancy_modulation = bool(discrepancy_modulation)
freqs = 1.0 / (
theta_base
** (torch.arange(0, self.half_dim, dtype=torch.float32) / self.half_dim)
)
self.register_buffer("base_freqs", freqs)
self.vortex_strength = nn.Parameter(
torch.full((max_seq_len,), base_vortex_strength),
requires_grad=adaptive_vortex,
)
self.phase_shift = nn.Parameter(torch.randn(max_seq_len, self.half_dim) * 0.01)
self.radial_warp = nn.Parameter(torch.ones(max_seq_len, self.half_dim))
if discrepancy_modulation:
self.discrepancy_projector = nn.Sequential(
nn.Linear(dim, self.half_dim),
nn.Tanh(),
nn.Linear(self.half_dim, self.half_dim),
)
self.register_buffer("energy_clamp_min", torch.tensor(0.1))
self.register_buffer("energy_clamp_max", torch.tensor(10.0))
def _compute_positional_vortex(self, seq_len, device, dtype, start_pos=0):
positions = torch.arange(
start_pos, start_pos + seq_len, dtype=torch.float32, device=device
)
position_freqs = positions.unsqueeze(1) * self.base_freqs.unsqueeze(0)
base_phase = position_freqs + self.phase_shift[start_pos : start_pos + seq_len]
vortex_phase = self.vortex_strength[start_pos : start_pos + seq_len].unsqueeze(
1
) * torch.sin(base_phase + position_freqs * 0.1)
final_phase = base_phase + vortex_phase
rotation = torch.complex(torch.cos(final_phase), torch.sin(final_phase))
radial_factor = torch.sqrt(position_freqs + 1e-8)
energy = 1.0 + self.energy_amplification * torch.sin(
self.radial_warp[start_pos : start_pos + seq_len] * radial_factor
+ vortex_phase.abs()
)
energy = energy.clamp(self.energy_clamp_min, self.energy_clamp_max)
return rotation, energy.to(dtype=dtype)
def apply_discrepancy_modulation(self, x, symplectic_state):
if not self.discrepancy_modulation or not symplectic_state:
return None
bsz, seq_len, _ = x.shape
total = torch.zeros(bsz, seq_len, 1, device=x.device, dtype=x.dtype)
count = 0
for theta, p in symplectic_state.values():
if theta is None or p is None:
continue
ev = theta.float().var(dim=-1, keepdim=True, correction=0) + p.float().var(
dim=-1, keepdim=True, correction=0
)
ev = (
ev.unsqueeze(1).expand(-1, seq_len, -1)
if theta.dim() == 2
else ev[:, :seq_len]
)
total = total + ev.to(dtype=x.dtype)
count += 1
if count == 0:
return None
lifted = F.pad(total / count, (0, self.dim - 1))
return self.discrepancy_projector(lifted)
def forward(self, x, symplectic_state=None, start_pos=0):
bsz, seq_len, dim = x.shape
if dim != self.dim or start_pos < 0 or start_pos + seq_len > self.max_seq_len:
raise ValueError(
"BlackHoleRoPE dimension/position exceeds configured bounds"
)
input_dtype = x.dtype
xp = x.reshape(bsz, seq_len, self.half_dim, 2)
xc = torch.complex(xp[..., 0].float(), xp[..., 1].float())
rotation, energy = self._compute_positional_vortex(
seq_len, x.device, torch.float32, start_pos
)
rotation = rotation.unsqueeze(0)
energy = energy.unsqueeze(0)
disc = self.apply_discrepancy_modulation(x, symplectic_state)
if disc is not None:
phase_scale = 1.0 + 0.1 * torch.tanh(disc.float())
base_phase = torch.angle(rotation)
rotation = torch.polar(
torch.ones_like(base_phase), base_phase * phase_scale
)
amp = xc * rotation * energy.float()
return (
torch.stack([amp.real, amp.imag], dim=-1)
.reshape(bsz, seq_len, dim)
.to(input_dtype)
)
class BlackHolePos(nn.Module):
"""Adapter: folds heads into batch for BlackHoleRoPE and repeats the symplectic
state per head so discrepancy modulation keeps the right batch alignment."""
def __init__(self, bh: nn.Module):
super().__init__()
self.bh = bh
def forward(self, x, start_pos: int = 0, symplectic_state=None):
B, H, T, dh = x.shape
state = None
if symplectic_state:
state = {
n: (th.repeat_interleave(H, 0), p.repeat_interleave(H, 0))
for n, (th, p) in symplectic_state.items()
if th is not None and p is not None
}
y = self.bh(
x.reshape(B * H, T, dh), symplectic_state=state, start_pos=start_pos
)
return y.view(B, H, T, dh)
# FlexAttention compilation is lazy and shared by CUDA attention calls.
_COMPILED_FLEX = None
# =============================================================================
# Attention backends and visibility geometry
# =============================================================================
def _run_flex(q, k, v, block_mask):
global _COMPILED_FLEX
if q.is_cuda:
if min(q.shape[-1], k.shape[-1], v.shape[-1]) < 16:
raise ValueError(
"CUDA FlexAttention requires head_dim >= 16; use a supported GPU model configuration"
)
if _COMPILED_FLEX is None:
_COMPILED_FLEX = torch.compile(flex_attention, dynamic=False)
return _COMPILED_FLEX(q, k, v, block_mask=block_mask, enable_gqa=True)
return flex_attention(q, k, v, block_mask=block_mask, enable_gqa=True)
def geometry_allows(mech, q_idx, kv_idx, cfg: RoutedAttentionConfig, picked=None):
"""Boolean key-visibility for a given mechanism id. Pure tensor algebra so the
same function serves FlexAttention's mask_mod and the dense reference mask.
`picked` is True where kv_idx lies in a graph-selected tile of q_idx's tile."""
d = q_idx - kv_idx
causal = d >= 0
local = d < cfg.local_window
tile_gap = torch.div(q_idx, cfg.tile, rounding_mode="floor") - torch.div(
kv_idx, cfg.tile, rounding_mode="floor"
)
strided = (d < cfg.strided_recent) | (
torch.remainder(tile_gap, cfg.strided_every) == 0
)
allowed = (
(mech == GLOBAL) | ((mech == LOCAL) & local) | ((mech == STRIDED) & strided)
)
if cfg.graph_top_r:
graph = d < cfg.graph_recent
if picked is not None:
graph = graph | picked
allowed = allowed | ((mech == GRAPH) & graph)
return causal & allowed
def geometry_block_mask(mech, length, cfg, mask_mod, picked=None):
"""Exact block classification in O(B * H * (T/tile)^2), never O(B*H*T^2).
A partial block uses the original elementwise predicate. Fully allowed blocks
skip that predicate. No attention probabilities or causal rules are approximated.
`picked` is the tile-level graph selection [B, H, n, n] (query tile, kv tile).
"""
c = cfg.tile
n = mech.shape[-1]
starts = torch.arange(n, device=mech.device) * c
ends = (starts + c - 1).clamp(max=length - 1)
dmin = starts[:, None] - ends[None, :]
dmax = ends[:, None] - starts[None, :]
gap = (
torch.arange(n, device=mech.device)[:, None]
- torch.arange(n, device=mech.device)[None, :]
)
periodic = gap.remainder(cfg.strided_every) == 0
mechanism = mech[..., None]
any_geometry = (
(mechanism == GLOBAL)
| ((mechanism == LOCAL) & (dmin < cfg.local_window))
| ((mechanism == STRIDED) & ((dmin < cfg.strided_recent) | periodic))
)
full_geometry = (
(mechanism == GLOBAL)
| ((mechanism == LOCAL) & (dmax < cfg.local_window))
| ((mechanism == STRIDED) & ((dmax < cfg.strided_recent) | periodic))
)
if cfg.graph_top_r:
g_any, g_full = dmin < cfg.graph_recent, dmax < cfg.graph_recent
if picked is not None:
# A picked tile is a strictly earlier, complete tile: every pair in the
# block is allowed, so it is safe for the full-block path.
g_any, g_full = g_any | picked, g_full | picked
any_geometry = any_geometry | ((mechanism == GRAPH) & g_any)
full_geometry = full_geometry | ((mechanism == GRAPH) & g_full)
any_block = (dmax >= 0) & any_geometry
# Out-of-range positions in tail tiles are masked. Such blocks must not
# enter the full-block path, which skips mask_mod in the CUDA kernel.
complete_tiles = starts + c <= length
full = (
(dmin >= 0) & full_geometry & complete_tiles[:, None] & complete_tiles[None, :]
)
partial = any_block & ~full
def encode(mask):
counts = mask.sum(-1).to(
dtype=torch.int32, memory_format=torch.contiguous_format
)
# Router permutes tile/head axes; argsort preserves that nonstandard
# layout. CUDA Flex flattens B/H using the H stride and requires
# contiguous metadata (the CPU eager path does not enforce this).
indices = (
mask.to(torch.int32)
.argsort(dim=-1, descending=True, stable=True)
.to(dtype=torch.int32, memory_format=torch.contiguous_format)
)
return counts, indices
pnum, pidx = encode(partial)
fnum, fidx = encode(full)
return BlockMask.from_kv_blocks(
pnum,
pidx,
fnum,
fidx,
BLOCK_SIZE=c,
mask_mod=mask_mod,
seq_lengths=(length, length),
)
# TAMELM routed sequence attention, v2.
#
# One shared GQA projection/cache; a tiny tile-level router picks, per (tile, q-head),
# top-k attention geometries from {global, local, strided, graph}; all geometries run
# through ONE attention call (FlexAttention block mask, or an equivalent dense mask).
#
# Invariants:
# * K/V are projected, position-encoded and cached BEFORE routing.
# * Tile routing is causal: tile c is routed, and its graph neighbours are chosen,
# from its first token only, so training == chunked prefill == token-by-token decode.
# * Top-k outputs are weighted by the ORIGINAL softmax probabilities (no
# renormalization), so top-1 still trains the router.
# * Graph geometry: a query tile sees its recent window plus the top-r COMPLETED
# past tiles ranked by (first-token query) . (tile-mean key). Ranking uses
# un-rotated q/k, so it is content similarity rather than position. The tile choice
# is hard (no gradient); the router weight on 'graph' is what trains. This is the
# causal, tile-level repair of the learned k-NN adjacency from the graph router.
#
# Positional transform is pluggable: RotaryPos (standard RoPE), WarpedRoPE (RoPE on a
# content-defined clock; relative in warped time), or BlackHolePos (legacy adapter;
# note it depends on absolute position through per-position phase/vortex/energy).
# =============================================================================
# Routed sequence attention
# =============================================================================
class RoutedSequenceAttention(nn.Module):
def __init__(self, cfg: RoutedAttentionConfig, pos: Optional[nn.Module] = None):
super().__init__()
H, Hkv, K = cfg.n_q_heads, cfg.n_kv_heads, cfg.top_k
dh = cfg.head_dim or cfg.d_model // H
if cfg.graph_top_r < 0:
raise ValueError("graph_top_r must be nonnegative")
self.M = 4 if cfg.graph_top_r else 3 # number of mechanisms
if (
min(
H,
Hkv,
dh,
cfg.tile,
cfg.local_window,
cfg.strided_recent,
cfg.strided_every,
cfg.graph_recent,
cfg.max_seq_len,
)
<= 0
):
raise ValueError("Attention dimensions/windows must be positive")
if H % Hkv or dh % 2 or not 1 <= K <= self.M:
raise ValueError("Invalid GQA heads, odd rotary dimension, or top_k")
if cfg.backend not in {"flex", "dense"}:
raise ValueError("backend must be flex or dense")
self.cfg, self.H, self.Hkv, self.K, self.dh = cfg, H, Hkv, K, dh
self.graph = bool(cfg.graph_top_r)
self.n_max = -(-cfg.max_seq_len // cfg.tile) # tiles in a full window
self._force_flex = False # test hook: Flex on CPU (eager)
self.q_proj = nn.Linear(cfg.d_model, H * dh, bias=False)
self.k_proj = nn.Linear(cfg.d_model, Hkv * dh, bias=False)
self.v_proj = nn.Linear(cfg.d_model, Hkv * dh, bias=False)
self.o_proj = nn.Linear(H * dh, cfg.d_model, bias=False)
self.router = nn.Linear(cfg.d_model, H * self.M, bias=False)
nn.init.normal_(self.router.weight, std=cfg.router_init_std)
# [H, M] mask: this revision has no mandatory anchor heads.
# The run contract records actual constraints; anchored variants are separate experiments.
self.register_buffer("allowed", torch.ones(H, self.M, dtype=torch.bool))
self.pos = pos if pos is not None else RotaryPos(dh)
# --------------------------------------------------------------------- routing
def _route(self, x_starts): # [B, n, d]
B, n, _ = x_starts.shape
logits = router_logits_fp32(self.router, x_starts).view(B, n, self.H, self.M)
logits = logits.masked_fill(~self.allowed, float("-inf"))
P = logits.softmax(-1)
w, idx = P.topk(self.K, dim=-1) # un-renormalized weights
return P, w, idx
def _balance(self, P, idx):
M = self.M
f = F.one_hot(idx, M).float().sum(-2).reshape(-1, M).mean(0) / self.K
Pbar = P.reshape(-1, M).mean(0)
loss = self.cfg.balance_coef * M * (f * Pbar).sum()
ent = -(P.clamp_min(1e-9).log() * P).sum(-1).mean()
return loss, f.detach(), ent.detach()
@torch.no_grad()
def _select_tiles(self, q_first, k_raw, tiles):
"""q_first [B,H,n_new,dh]: un-rotated query of each new tile's first token.
k_raw [B,Hkv,S,dh]: every un-rotated key known so far. tiles [n_new].
Returns [B, n_new, H, n_max] bool: past complete tiles picked per query tile."""
C, r = self.cfg.tile, self.cfg.graph_top_r
B, H, n_new, dh = q_first.shape
sel = torch.zeros(
B, n_new, H, self.n_max, dtype=torch.bool, device=q_first.device
)
n_full = min(k_raw.shape[2] // C, self.n_max)
if n_full == 0 or n_new == 0:
return sel
with torch.autocast(device_type=q_first.device.type, enabled=False):
kbar = k_raw[:, :, : n_full * C].float().unflatten(2, (n_full, C)).mean(3)
kbar = kbar.repeat_interleave(H // self.Hkv, dim=1) # [B,H,n_full,dh]
scores = torch.einsum("bhnd,bhtd->bnht", q_first.float(), kbar)
# Tile t is a candidate for query tile c iff t < c (strictly earlier, hence
# complete by the time tile c's first token exists).
past = torch.arange(n_full, device=sel.device)[None, :] < tiles[:, None]
scores = scores.masked_fill(~past[None, :, None, :], float("-inf"))
vals, idx = scores.topk(min(r, n_full), dim=-1)
part = torch.zeros(B, n_new, H, n_full, dtype=torch.bool, device=sel.device)
part.scatter_(-1, idx, torch.isfinite(vals))
sel[..., :n_full] = part
return sel
# --------------------------------------------------------------------- forward
def forward(
self, x, cache: Optional[Dict] = None, start_pos: int = 0, symplectic_state=None
):
"""x: [B, T, d]. With `cache` (a dict, mutated in place) supports chunked
prefill and decode; `start_pos` is the absolute position of x[:, 0]."""
cfg, H, Hkv, K, dh, C = (
self.cfg,
self.H,
self.Hkv,
self.K,
self.dh,
self.cfg.tile,
)
B, T, _ = x.shape
if T < 1 or start_pos < 0:
raise ValueError("Nonempty input and nonnegative start_pos required")
if cache is not None and torch.is_grad_enabled():
raise ValueError("KV caching is inference-only; use torch.no_grad()")
if cache is None and start_pos != 0:
raise ValueError("A nonzero start_pos requires the prior KV cache")
if cache is not None and "k" not in cache and start_pos != 0:
raise ValueError("An empty cache must begin at position zero")
q_raw = self.q_proj(x).view(B, T, H, dh).transpose(1, 2)
k_raw = self.k_proj(x).view(B, T, Hkv, dh).transpose(1, 2)
v = self.v_proj(x).view(B, T, Hkv, dh).transpose(1, 2)
clock_stats = {}
if isinstance(self.pos, WarpedRoPE):
t0 = (
cache["clock"][:, -1]
if (cache is not None and "clock" in cache)
else None
)
times, clock = self.pos.clock(x, t0) # [B, T, Hkv]
rates = (clock - times).detach()
clock_stats = {
"clock_rate_mean": rates.mean(),
"clock_rate_min": rates.amin(),
"clock_rate_max": rates.amax(),
}
if cache is not None:
cache["clock"] = (
torch.cat([cache["clock"], clock], 1) if "clock" in cache else clock
)
times = times.transpose(1, 2) # [B, Hkv, T]
q = self.pos.rotate(q_raw, times.repeat_interleave(H // Hkv, dim=1))
k = self.pos.rotate(k_raw, times)
else:
# Compute the positional transform once for the shared Q/K position range.
qk = self.pos(
torch.cat((q_raw, k_raw), dim=1),
start_pos=start_pos,
symplectic_state=symplectic_state,
)
q, k = qk.split((H, Hkv), dim=1)
# 1) write sequence memory before any routing decision
k_raw_all = k_raw
if cache is not None:
if "k" in cache:
assert (
cache["k"].shape[2] == start_pos
), "cache length must equal start_pos"
k = torch.cat([cache["k"], k], 2)
v = torch.cat([cache["v"], v], 2)
if self.graph:
k_raw_all = torch.cat([cache["k_raw"], k_raw], 2)
cache["k"], cache["v"] = k, v
if self.graph:
cache["k_raw"] = k_raw_all
# 2) causal tile routing: route every tile whose FIRST token lies in this chunk
first_tile, last_tile = start_pos // C, (start_pos + T - 1) // C
n_known = cache["w"].shape[1] if (cache is not None and "w" in cache) else 0
new_tiles = torch.arange(n_known, last_tile + 1, device=x.device)
offsets = new_tiles * C - start_pos
if n_known <= last_tile and n_known * C < start_pos:
raise ValueError("A tile first token was skipped; routes out of sync")
aux = dict(clock_stats)
sel_new = None
if len(new_tiles):
P_new, w_new, idx_new = self._route(x[:, offsets])
loss, f, ent = self._balance(P_new, idx_new)
aux.update(balance_loss=loss, mech_freq=f, router_entropy=ent)
with torch.no_grad():
selected = F.one_hot(idx_new, self.M).float()
selected_weight = torch.zeros_like(P_new).scatter_add_(
-1, idx_new, w_new
)
aux.update(
query_tile_indices=new_tiles.detach(),
mech_frequency_by_query_tile=selected.mean(dim=(0, 2, 3)),
mech_probability_by_query_tile=P_new.detach().mean(dim=(0, 2)),
mech_selected_weight_by_query_tile=selected_weight.mean(dim=(0, 2)),
)
if self.graph:
sel_new = self._select_tiles(q_raw[:, :, offsets], k_raw_all, new_tiles)
else:
w_new = x.new_zeros(B, 0, H, K, dtype=torch.float32)
idx_new = torch.zeros(B, 0, H, K, dtype=torch.long, device=x.device)
if self.graph and sel_new is None:
sel_new = torch.zeros(
B, 0, H, self.n_max, dtype=torch.bool, device=x.device
)
if cache is not None and n_known:
w_all = torch.cat([cache["w"], w_new], 1)
idx_all = torch.cat([cache["idx"], idx_new], 1)
sel_all = torch.cat([cache["sel"], sel_new], 1) if self.graph else None
else:
w_all, idx_all, sel_all = w_new, idx_new, sel_new
if cache is not None:
cache["w"], cache["idx"] = w_all.detach(), idx_all
if self.graph:
cache["sel"] = sel_all
# slot s = h*K + j (maps to kv head s // (H*K/Hkv) == h // (H/Hkv))
w_t = w_all[:, first_tile : last_tile + 1] # [B, n, H, K]
i_t = idx_all[:, first_tile : last_tile + 1]
n = w_t.shape[1]
mech = i_t.permute(0, 2, 3, 1).reshape(B, H * K, n) # [B, HK, n]
wslot = w_t.permute(0, 2, 3, 1).reshape(B, H * K, n)
picked_slots = None
if self.graph: # [B, HK, n, n_max]
picked_slots = (
sel_all[:, first_tile : last_tile + 1]
.permute(0, 2, 1, 3)
.repeat_interleave(K, dim=1)
)
q_slots = q.repeat_interleave(K, dim=1) # [B, HK, T, dh]
q_pos = torch.arange(start_pos, start_pos + T, device=x.device)
q_tile = q_pos // C - first_tile # [T]
if cfg.backend == "flex" and not HAS_FLEX:
raise RuntimeError(
"FlexAttention unavailable; install compatible torch or explicitly select dense"
)
use_flex = (
cfg.backend == "flex"
and (x.is_cuda or self._force_flex)
and cache is None
and start_pos == 0
)
if use_flex:
n_max = self.n_max
if picked_slots is not None:
def mask_mod(b, h, q_idx, kv_idx):
qt = (q_idx // C).clamp(max=n - 1)
picked = picked_slots[b, h, qt, (kv_idx // C).clamp(max=n_max - 1)]
return (
(q_idx < T)
& (kv_idx < T)
& geometry_allows(mech[b, h, qt], q_idx, kv_idx, cfg, picked)
)
else:
def mask_mod(b, h, q_idx, kv_idx):
return (
(q_idx < T)
& (kv_idx < T)
& geometry_allows(
mech[b, h, (q_idx // C).clamp(max=n - 1)],
q_idx,
kv_idx,
cfg,
)
)
if C == 128:
bm = geometry_block_mask(
mech,
T,
cfg,
mask_mod,
None if picked_slots is None else picked_slots[..., :n],
)
else:
bm = create_block_mask(
mask_mod,
B,
H * K,
T,
T,
device=x.device,
BLOCK_SIZE=C,
_compile=x.is_cuda,
)
out = _run_flex(q_slots, k, v, bm)
else:
S = k.shape[2]
kv_pos = torch.arange(S, device=x.device)
m = mech[:, :, q_tile] # [B, HK, T]
picked = None
if picked_slots is not None:
picked = picked_slots[:, :, q_tile][..., kv_pos // C] # [B, HK, T, S]
allow = geometry_allows(
m[..., None],
q_pos[None, None, :, None],
kv_pos[None, None, None, :],
cfg,
picked,
) # [B, HK, T, S]
out = F.scaled_dot_product_attention(
q_slots, k, v, attn_mask=allow, enable_gqa=True
)
# 3) merge: o_h = sum_j P_{h,m_j} * A_{m_j}
out = out * wslot[:, :, q_tile, None].to(out.dtype)
out = out.view(B, H, K, T, dh).sum(2).transpose(1, 2).reshape(B, T, H * dh)
return self.o_proj(out), aux
# =============================================================================
# Grouped GEMM acceleration
# =============================================================================
# Ampere-compatible dropless grouped GEMM with explicit autograd.
#
# No Hopper TMA instructions, token dropping, padding capacity, or CPU count reads.
# A is [sum(tokens), K], W is [experts,K,N], offsets are cumulative token counts.
# Persistent forward/input-gradient kernels use tensor-core dot products; each
# weight-gradient tile has a unique writer and accumulates in FP32.
if HAS_TRITON:
@triton.jit
def _grouped_forward(
A,
W,
O,
Y,
K: tl.constexpr,
N: tl.constexpr,
AS0: tl.constexpr,
AS1: tl.constexpr,
WS0: tl.constexpr,
WS1: tl.constexpr,
WS2: tl.constexpr,
E: tl.constexpr,
GRID: tl.constexpr,
BM: tl.constexpr = 32,
BN: tl.constexpr = 64,
BK: tl.constexpr = 32,
):
tile_id = tl.program_id(0)
prior_tiles = 0
start = 0
for expert in range(E):
end = tl.load(O + expert)
count = end - start
nt = tl.cdiv(N, BN)
tiles = tl.cdiv(count, BM) * nt
while tile_id >= prior_tiles and tile_id < prior_tiles + tiles:
local = tile_id - prior_tiles
mi = (local // nt) * BM + tl.arange(0, BM)
ni = (local % nt) * BN + tl.arange(0, BN)
ki = tl.arange(0, BK)
acc = tl.zeros((BM, BN), tl.float32)
for kk in range(tl.cdiv(K, BK)):
kval = kk * BK + ki
av = tl.load(
A + (start + mi[:, None]) * AS0 + kval[None, :] * AS1,
(mi[:, None] < count) & (kval[None, :] < K),
other=0,
)
wv = tl.load(
W + expert * WS0 + kval[:, None] * WS1 + ni[None, :] * WS2,
(kval[:, None] < K) & (ni[None, :] < N),
other=0,
)
acc += tl.dot(av, wv)
tl.store(
Y + (start + mi[:, None]) * N + ni[None, :],
acc,
(mi[:, None] < count) & (ni[None, :] < N),
)
tile_id += GRID
prior_tiles += tiles
start = end
@triton.jit
def _grouped_weight_grad(
A,
DY,
O,
DW,
K: tl.constexpr,
N: tl.constexpr,
AS0: tl.constexpr,
AS1: tl.constexpr,
DS0: tl.constexpr,
DS1: tl.constexpr,
BM: tl.constexpr = 32,
BN: tl.constexpr = 64,
BT: tl.constexpr = 32,
):
expert = tl.program_id(0)
ki = tl.program_id(1) * BM + tl.arange(0, BM)
ni = tl.program_id(2) * BN + tl.arange(0, BN)
end = tl.load(O + expert)
start = 0
if expert > 0:
start = tl.load(O + expert - 1)
ti = tl.arange(0, BT)
acc = tl.zeros((BM, BN), tl.float32)
for block in range(tl.cdiv(end - start, BT)):
token = start + block * BT + ti
av = tl.load(
A + token[None, :] * AS0 + ki[:, None] * AS1,
(token[None, :] < end) & (ki[:, None] < K),
other=0,
)
dy = tl.load(
DY + token[:, None] * DS0 + ni[None, :] * DS1,
(token[:, None] < end) & (ni[None, :] < N),
other=0,
)
acc += tl.dot(av, dy)
tl.store(
DW + expert * K * N + ki[:, None] * N + ni[None, :],
acc,
(ki[:, None] < K) & (ni[None, :] < N),
)
def _forward(a, w, offsets):
if not HAS_TRITON or not a.is_cuda:
raise RuntimeError("Ampere grouped GEMM requires CUDA and Triton")
if a.dtype not in (torch.bfloat16, torch.float16) or w.dtype != a.dtype:
raise ValueError("Grouped GEMM expects matching BF16/FP16 operands")
if (
a.ndim != 2
or w.ndim != 3
or a.shape[1] != w.shape[1]
or offsets.numel() != w.shape[0]
):
raise ValueError("Grouped GEMM shape mismatch")
out = torch.empty((a.shape[0], w.shape[2]), device=a.device, dtype=a.dtype)
grid = torch.cuda.get_device_properties(a.device).multi_processor_count
with torch.cuda.device(a.device):
_grouped_forward[(grid,)](
a,
w,
offsets,
out,
a.shape[1],
w.shape[2],
*a.stride(),
*w.stride(),
w.shape[0],
grid,
num_warps=4,
num_stages=3,
)
return out
class _GroupedGEMM(torch.autograd.Function):
@staticmethod
def forward(ctx, a, w, offsets):
ctx.save_for_backward(a, w, offsets)
return _forward(a, w, offsets)
@staticmethod
def backward(ctx, grad):
a, w, offsets = ctx.saved_tensors
da = (
_forward(grad, w.transpose(-1, -2), offsets)
if ctx.needs_input_grad[0]
else None
)
dw = None
if ctx.needs_input_grad[1]:
dw = torch.empty(w.shape, device=w.device, dtype=w.dtype)
grid = (
w.shape[0],
triton.cdiv(w.shape[1], 32),
triton.cdiv(w.shape[2], 64),
)
with torch.cuda.device(a.device):
_grouped_weight_grad[grid](
a,
grad,
offsets,
dw,
w.shape[1],
w.shape[2],
*a.stride(),
*grad.stride(),
num_warps=4,
num_stages=3,
)
return da, dw, None
def ampere_grouped_mm(a, w, offsets):
return _GroupedGEMM.apply(a, w, offsets)
def grouped_mm(a, b, offsets):
return ampere_grouped_mm(a, b, offsets)
# =============================================================================
# Model building blocks
# =============================================================================
class RMSNorm(nn.Module):
def __init__(self, dim):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
return (
x.float() * torch.rsqrt(x.float().square().mean(-1, keepdim=True) + 1e-6)
).to(x.dtype) * self.weight
class DepthAttention(nn.Module):
"""Bank entries [B,T,H,d] preserve token index exactly; never reduce over T.
Keep the input embedding plus the most recent layer states. Keys and values
are projected once when written, with autograd retained through the bank.
"""
def __init__(self, cfg):
super().__init__()
self.heads = cfg.depth_heads
self.dim = cfg.depth_dim
self.q_proj = nn.Linear(cfg.embed_dim, cfg.depth_dim, bias=False)
self.out_proj = nn.Linear(cfg.depth_dim, cfg.embed_dim, bias=False)
self.gate = nn.Parameter(torch.tensor(cfg.depth_gate_init))
def forward(self, x, bank):
b, t, _ = x.shape
q = self.q_proj(x).view(b, t, self.heads, self.dim // self.heads)
keys = torch.stack([kv[0] for kv in bank], dim=-2)
vals = torch.stack([kv[1] for kv in bank], dim=-2)
scores = (q.unsqueeze(-2).float() * keys.float()).sum(-1) / math.sqrt(
q.shape[-1]
)
weights = scores.softmax(-1).to(vals.dtype)
y = (weights.unsqueeze(-1) * vals).sum(-2).reshape(b, t, self.dim)
return self.gate.tanh() * self.out_proj(y)
class FoldUnfoldReasoning(nn.Module):
"""Prefix-specific full-width slots, shared refinement, and residual readout.
Each token owns its workspace; slots never mix across token positions. Fold
and, by default, every refinement step read the full causal token bank.
Experimental local_only refinement omits revisits but retains slot operations.
A separate direct token read bypasses the slots. Query chunking bounds temporary activations,
not context or information access. Only immutable prefix K/V are cached;
slots are recomputed for new queries, so chunked decoding has the same graph
as a full forward. No extra token positions are consumed by latent steps.
"""
def __init__(self, cfg):
super().__init__()
self.width, self.heads = cfg.embed_dim, cfg.reasoning_heads
self.slots, self.steps = cfg.reasoning_slots, cfg.reasoning_steps
self.chunk_size = cfg.reasoning_chunk_size
self.initial_fold = cfg.reasoning_initial_fold
self.direct_read = cfg.reasoning_direct_read
self.revisit_mode = cfg.reasoning_revisit_mode
self.position_bias = (
RelativePositionBias(self.heads)
if cfg.reasoning_position_bias == "relative"
else (
ContinuousPositionBias(32, self.heads)
if cfg.reasoning_position_bias == "continuous"
else None
)
)
self.slot_seed = nn.Parameter(torch.randn(self.slots, self.width) * 0.02)
self.context_norm = RMSNorm(self.width)
self.slot_norm = RMSNorm(self.width)
self.ff_norm = RMSNorm(self.width)
self.context_kv = nn.Linear(self.width, 2 * self.width, bias=False)
self.fold_q = nn.Linear(self.width, self.width, bias=False)
self.fold_out = nn.Linear(self.width, self.width, bias=False)
self.revisit_q = nn.Linear(self.width, self.width, bias=False)
self.revisit_out = nn.Linear(self.width, self.width, bias=False)
self.slot_qkv = nn.Linear(self.width, 3 * self.width, bias=False)
self.slot_out = nn.Linear(self.width, self.width, bias=False)
self.ff_up = nn.Linear(self.width, 4 * self.width, bias=False)
self.ff_down = nn.Linear(2 * self.width, self.width, bias=False)
self.unfold_q = nn.Linear(self.width, self.width, bias=False)
self.unfold_kv = nn.Linear(self.width, 2 * self.width, bias=False)
self.unfold_out = nn.Linear(self.width, self.width, bias=False)
self.direct_q = nn.Linear(self.width, self.width, bias=False)
self.direct_out = nn.Linear(self.width, self.width, bias=False)
self.unfold_gate = nn.Parameter(torch.tensor(float(cfg.reasoning_gate_init)))
self.direct_gate = nn.Parameter(torch.tensor(float(cfg.reasoning_gate_init)))
def _heads(self, x):
return x.unflatten(-1, (self.heads, self.width // self.heads)).transpose(-3, -2)
def _merge(self, x):
return x.transpose(-3, -2).flatten(-2)
def _chunk(self, x, k, v, start_pos, steps):
b, t, d = x.shape
seed = self.context_norm(x)
z = seed[:, :, None, :] + self.slot_seed[None, None]
# A query's slots share exactly that query's causal prefix.
allow = (
torch.arange(k.shape[2], device=x.device)[None, :]
<= (start_pos + torch.arange(t, device=x.device))[:, None]
)
direct_mask = allow[None, None]
if self.position_bias is not None:
# Explicit causality is retained after adding learned score biases.
direct_mask = self.position_bias(t, k.shape[2], start_pos).to(k.dtype)
direct_mask = direct_mask.masked_fill(~allow[None, None], float("-inf"))
slot_mask = direct_mask.repeat_interleave(self.slots, dim=-2)
def read_slots(query):
q = self._heads(query.reshape(b, t * self.slots, d))
return self._merge(
F.scaled_dot_product_attention(q, k, v, attn_mask=slot_mask)
).reshape(b, t, self.slots, d)
if self.initial_fold:
z = z + self.fold_out(read_slots(self.fold_q(self.slot_norm(z))))
initial = z
for _ in range(steps):
if self.revisit_mode == "full":
z = z + self.revisit_out(read_slots(self.revisit_q(self.slot_norm(z))))
q, sk, sv = self.slot_qkv(self.slot_norm(z)).chunk(3, dim=-1)
# [B,T,H,slots,head_dim]: no attention between different prefixes.
z = z + self.slot_out(
self._merge(
F.scaled_dot_product_attention(
self._heads(q), self._heads(sk), self._heads(sv)
)
)
)
gate, value = self.ff_up(self.ff_norm(z)).chunk(2, dim=-1)
z = z + self.ff_down(F.silu(gate) * value)
uk, uv = self.unfold_kv(self.slot_norm(z)).chunk(2, dim=-1)
uq = self._heads(self.unfold_q(seed)[:, :, None, :])
unfolded = self._merge(
F.scaled_dot_product_attention(uq, self._heads(uk), self._heads(uv))
).squeeze(-2)
delta = self.unfold_gate.tanh() * self.unfold_out(unfolded)
if self.direct_read:
direct = self._merge(
F.scaled_dot_product_attention(
self._heads(self.direct_q(seed)), k, v, attn_mask=direct_mask
)
)
delta = delta + self.direct_gate.tanh() * self.direct_out(direct)
# Detached sums allow correct aggregation across execution chunks.
stats = torch.stack(
(
(z - initial).detach().float().square().sum(),
delta.detach().float().square().sum(),
x.detach().float().square().sum(),
)
)
return x + delta, stats
def forward(self, x, cache=None, start_pos=0, steps=None):
steps = self.steps if steps is None else steps
if not isinstance(steps, int) or isinstance(steps, bool) or steps < 1:
raise ValueError("reasoning_steps must be a positive integer")
k, v = self.context_kv(self.context_norm(x)).chunk(2, dim=-1)
k, v = self._heads(k), self._heads(v)
if cache is not None:
if "reasoning_k" in cache:
k = torch.cat((cache["reasoning_k"], k), dim=2)
v = torch.cat((cache["reasoning_v"], v), dim=2)
cache["reasoning_k"], cache["reasoning_v"] = k, v
chunks, stats = [], []
for offset in range(0, x.shape[1], self.chunk_size):
query = x[:, offset : offset + self.chunk_size]
if torch.is_grad_enabled():
from torch.utils.checkpoint import checkpoint
out, info = checkpoint(
self._chunk,
query,
k,
v,
start_pos + offset,
steps,
use_reentrant=False,
)
else:
out, info = self._chunk(query, k, v, start_pos + offset, steps)
chunks.append(out)
stats.append(info)
sums = torch.stack(stats).sum(0)
return torch.cat(chunks, dim=1), {
"steps": steps,
"slots": self.slots,
"refinement_delta_rms": (sums[0] / (x.numel() * self.slots)).sqrt(),
"output_delta_rms": (sums[1] / x.numel()).sqrt(),
"input_rms": (sums[2] / x.numel()).sqrt(),
}
class DroplessMoE(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.router = nn.Linear(cfg.embed_dim, cfg.num_experts, bias=False)
# Flat [expert * bank + bank_id] storage keeps grouped GEMM unchanged.
self.bank_router = (
nn.Parameter(torch.empty(cfg.num_experts, cfg.embed_dim, cfg.expert_banks))
if cfg.expert_banks > 1
else None
)
if self.bank_router is not None:
nn.init.normal_(self.bank_router, std=0.02)
self.up = nn.Parameter(
torch.empty(
cfg.num_experts * cfg.expert_banks,
cfg.embed_dim,
cfg.expert_hidden_dim * 2,
)
)
self.down = nn.Parameter(
torch.empty(
cfg.num_experts * cfg.expert_banks, cfg.expert_hidden_dim, cfg.embed_dim
)
)
nn.init.normal_(self.up, std=0.02)
nn.init.normal_(self.down, std=0.02 / math.sqrt(2 * cfg.num_layers))
def forward(self, x):
cfg = self.cfg
if (
self.training
and torch.is_grad_enabled()
and cfg.expert_top_k == 1
and cfg.num_experts > 1
):
raise ValueError(
"Training with expert_top_k=1 removes the task gradient through "
"normalized expert weights. Use top-2 or more; top-1 inference remains supported."
)
flat = x.reshape(-1, x.shape[-1])
expert_logits = router_logits_fp32(self.router, flat)
prob = expert_logits.softmax(-1)
z_loss = (
cfg.router_z_loss_coef * expert_logits.logsumexp(-1).square().mean()
if cfg.router_z_loss_coef
else expert_logits.new_zeros(())
)
weights, experts = prob.topk(cfg.expert_top_k, -1)
weights = weights / weights.sum(-1, keepdim=True)
if cfg.expert_banks > 1:
mixed, balance, stats = self._bank_forward(x, flat, prob, weights, experts)
z_loss = z_loss + stats["router_z_loss"]
stats["router_z_loss"] = z_loss.detach()
stats["expert_router_entropy"] = (
-(prob.detach() * prob.detach().clamp_min(1e-9).log()).sum(-1).mean()
)
return mixed, balance + z_loss, stats
order = experts.flatten().argsort(stable=True)
token_ids = order // cfg.expert_top_k
dispatched = flat[token_ids]
counts = torch.zeros(cfg.num_experts, device=x.device, dtype=torch.int32)
counts.scatter_add_(
0, experts.flatten(), torch.ones_like(experts.flatten(), dtype=torch.int32)
)
if cfg.expert_backend == "grouped" and x.is_cuda:
# Explicit casts are differentiable and avoid ambiguous autocast support.
offsets = counts.cumsum(0, dtype=torch.int32)
h = grouped_mm(
dispatched.to(torch.bfloat16), self.up.to(torch.bfloat16), offsets
)
gate, value = h.chunk(2, -1)
h = (F.silu(gate) * value).contiguous()
result = grouped_mm(h, self.down.to(torch.bfloat16), offsets)
else:
# One synchronization for all counts, instead of nonzero()+sync per expert.
sizes = counts.cpu().tolist()
chunks = dispatched.split(sizes, 0)
# Empty assignments do not execute an FFN; indexed parameter grads
# still scatter zeros to unselected expert slices.
result = torch.cat(
[
self._expert(chunk, i)
for i, chunk in enumerate(chunks)
if chunk.shape[0]
],
0,
)
# Autocast produces BF16 expert outputs while the residual may be FP32.
# Both backends must mix in the residual dtype; index_add does not promote.
result = result.to(flat.dtype)
weighted = result * weights.flatten()[order, None].to(result.dtype)
mixed = torch.zeros_like(flat).index_add(0, token_ids, weighted)
frequency = counts.float() / experts.numel()
balance = cfg.balance_coef * cfg.num_experts * (frequency * prob.mean(0)).sum()
return (
mixed.view_as(x),
balance + z_loss,
{
"expert_frequency": frequency.detach(),
"router_z_loss": z_loss.detach(),
"expert_router_entropy": -(
prob.detach() * prob.detach().clamp_min(1e-9).log()
)
.sum(-1)
.mean(),
},
)
def _expert(self, x, i):
gate, value = (x @ self.up[i]).chunk(2, -1)
return (F.silu(gate) * value) @ self.down[i]
def _bank_forward(self, x, flat, prob, weights, experts):
cfg = self.cfg
# Compute tiny router logits, not unselected FFN banks. Avoid gathering
# a [tokens,chosen_experts,width,banks] copy of router parameters.
with torch.autocast(device_type=x.device.type, enabled=False):
logits = torch.einsum("nd,edp->nep", flat.float(), self.bank_router.float())
selected_logits = logits.gather(
1, experts[..., None].expand(-1, -1, cfg.expert_banks)
)
bank_prob = selected_logits.softmax(-1)
bank_weights, banks = bank_prob.topk(cfg.expert_bank_top_k, dim=-1)
# Original mass keeps a task-loss gradient when only one bank is chosen.
# Mean scale is one at a uniform bank router, including top-1.
bank_weights = bank_weights * (cfg.expert_banks / cfg.expert_bank_top_k)
paths = experts[..., None] * cfg.expert_banks + banks
path_weights = weights[..., None] * bank_weights
paths_per_token = cfg.expert_top_k * cfg.expert_bank_top_k
order = paths.flatten().argsort(stable=True)
token_ids = order // paths_per_token
dispatched = flat[token_ids]
count = cfg.num_experts * cfg.expert_banks
counts = torch.zeros(count, device=x.device, dtype=torch.int32)
counts.scatter_add_(
0, paths.flatten(), torch.ones_like(paths.flatten(), dtype=torch.int32)
)
if cfg.expert_backend == "grouped" and x.is_cuda:
offsets = counts.cumsum(0, dtype=torch.int32)
h = grouped_mm(
dispatched.to(torch.bfloat16), self.up.to(torch.bfloat16), offsets
)
gate, value = h.chunk(2, dim=-1)
result = grouped_mm(
(F.silu(gate) * value).contiguous(),
self.down.to(torch.bfloat16),
offsets,
)
else:
chunks = dispatched.split(counts.cpu().tolist(), dim=0)
# Do not invoke even an empty FFN for an unselected expert-bank.
result = torch.cat(
[
self._expert(chunk, i)
for i, chunk in enumerate(chunks)
if chunk.shape[0]
],
dim=0,
)
weighted = result.to(flat.dtype) * path_weights.flatten()[order, None].to(
flat.dtype
)
mixed = torch.zeros_like(flat).index_add(0, token_ids, weighted)
frequency = F.one_hot(experts, cfg.num_experts).float().mean(dim=(0, 1))
balance = cfg.balance_coef * cfg.num_experts * (frequency * prob.mean(0)).sum()
# Conditional bank balance for each expert, weighted by its assignments.
expert_ids = experts.flatten()
bank_sum = bank_prob.new_zeros(cfg.num_experts, cfg.expert_banks).index_add(
0, expert_ids, bank_prob.flatten(0, 1)
)
assignments = torch.bincount(expert_ids, minlength=cfg.num_experts).float()
bank_frequency = counts.reshape(cfg.num_experts, cfg.expert_banks).float()
bank_frequency = bank_frequency / (
assignments[:, None] * cfg.expert_bank_top_k
).clamp_min(1)
balance = (
balance
+ cfg.balance_coef
* cfg.expert_banks
* (bank_frequency * bank_sum).sum()
/ expert_ids.numel()
)
stats = {
"router_z_loss": (
cfg.router_z_loss_coef * selected_logits.logsumexp(-1).square().mean()
if cfg.router_z_loss_coef
else selected_logits.new_zeros(())
),
"expert_frequency": frequency.detach(),
"expert_bank_frequency": bank_frequency.detach(),
"expert_bank_probability": (
bank_sum / assignments[:, None].clamp_min(1)
).detach(),
"expert_bank_selected_mass": bank_prob.topk(cfg.expert_bank_top_k, dim=-1)
.values.sum(-1)
.mean()
.detach(),
}
return mixed.view_as(x), balance, stats
class AttentionBlock(nn.Module):
def __init__(self, cfg):
super().__init__()
self.hnorm, self.dnorm, self.mnorm = (RMSNorm(cfg.embed_dim) for _ in range(3))
ac = RoutedAttentionConfig(
d_model=cfg.embed_dim,
n_q_heads=cfg.n_q_heads,
n_kv_heads=cfg.n_kv_heads,
top_k=cfg.attention_top_k,
tile=cfg.tile,
local_window=cfg.local_window,
strided_recent=cfg.strided_recent,
strided_every=cfg.strided_every,
graph_top_r=cfg.graph_top_r,
graph_recent=cfg.graph_recent,
max_seq_len=cfg.max_seq_len,
router_init_std=cfg.router_init_std,
balance_coef=cfg.balance_coef,
backend=cfg.attention_backend,
)
head_dim = cfg.embed_dim // cfg.n_q_heads
if cfg.rope_type == "warped":
pos = WarpedRoPE(cfg.embed_dim, head_dim, cfg.n_q_heads, cfg.n_kv_heads)
elif cfg.rope_type == "blackhole":
pos = BlackHolePos(
BlackHoleRoPE(head_dim, cfg.max_seq_len, discrepancy_modulation=False)
)
else:
pos = None
self.horizontal = RoutedSequenceAttention(ac, pos)
self.vertical = DepthAttention(cfg)
self.moe = DroplessMoE(cfg)
self.depth_mode = cfg.depth_mode
def forward(self, x, bank, cache=None, start_pos=0):
h, aux = self.horizontal(self.hnorm(x), cache=cache, start_pos=start_pos)
x = x + h
if self.depth_mode == "attention":
x = x + self.vertical(self.dnorm(x), bank)
m, mloss, freq = self.moe(self.mnorm(x))
if isinstance(freq, dict):
aux.update(freq)
else:
aux["expert_frequency"] = freq
aux["depth_gate"] = self.vertical.gate.detach().tanh()
return x + m, aux.get("balance_loss", x.new_zeros(())) + mloss, aux
# =============================================================================
# Hugging Face cache and output interfaces
# =============================================================================
class TAMECache(Cache):
"""Per-sequence KV and tile-route state with HF beam and rollback operations.
Physical token count includes padding; each row's rotary position counts only
real tokens. Depth attention is same-token across layers, so its bank is
reconstructed for new tokens and needs no cross-token cache.
"""
def __init__(self, num_layers, tile):
super().__init__(layers=[])
self.num_layers, self.tile = num_layers, tile
self.rows = []
self.mask = None
def get_seq_length(self, layer_idx=0):
return 0 if self.mask is None else self.mask.shape[1]
def get_max_cache_shape(self, layer_idx=0):
return -1
def get_mask_sizes(self, query_length, layer_idx=0):
return self.get_seq_length() + query_length, 0
@property
def is_compileable(self):
return False
@property
def is_initialized(self):
return bool(self.rows)
@property
def is_sliding(self):
return [False] * self.num_layers
@property
def max_batch_size(self):
return len(self.rows)
def reset(self):
self.rows = []
self.mask = None
def row_length(self, index):
row = self.rows[index]
return row[0]["k"].shape[2] if row and "k" in row[0] else 0
def reorder_cache(self, beam_idx):
indices = beam_idx.tolist()
self.rows = [[dict(layer) for layer in self.rows[i]] for i in indices]
if self.mask is not None:
self.mask = self.mask.index_select(0, beam_idx.to(self.mask.device))
def batch_select_indices(self, indices):
self.reorder_cache(indices)
def batch_repeat_interleave(self, repeats):
if self.mask is not None:
indices = torch.arange(
len(self.rows), device=self.mask.device
).repeat_interleave(repeats)
self.reorder_cache(indices)
def crop(self, max_length):
seen = self.get_seq_length()
end = max(0, seen + max_length) if max_length < 0 else min(seen, max_length)
if self.mask is None:
return
self.mask = self.mask[:, :end]
for i, row in enumerate(self.rows):
length = int(self.mask[i].sum())
tiles = (length + self.tile - 1) // self.tile
for layer in row:
if length == 0:
layer.clear()
continue
for key in ("k", "v", "k_raw", "reasoning_k", "reasoning_v"):
if key in layer:
layer[key] = layer[key][:, :, :length]
if "clock" in layer: # [B, S, Hkv] warped time
layer["clock"] = layer["clock"][:, :length]
for key in ("w", "idx", "sel"): # [B, tiles, ...]
if key in layer:
layer[key] = layer[key][:, :tiles]
@dataclass
class TAMEOutput(ModelOutput):
loss: torch.Tensor | None = None
logits: torch.Tensor | None = None
past_key_values: TAMECache | list | None = None
aux_losses: dict | None = None
attention_stats: list | None = None
reasoning_stats: dict | list | None = None
# =============================================================================
# Complete language model
# =============================================================================
class TAMELM(PreTrainedModel, GenerationMixin):
config_class = TAMEConfig
base_model_prefix = "tamelm"
_tied_weights_keys = {"lm_head.weight": "token_embedding.weight"}
# Construction and checkpoint compatibility
def _init_weights(self, module):
# Initialization below is the existing TAMELM scheme. HF post_init
# records model metadata but must not draw extra random numbers or
# change the seeded backbone used by the pilot and matched controls.
return
def __init__(self, config):
super().__init__(config)
self.token_embedding = nn.Embedding(config.vocab_size, config.embed_dim)
self.blocks = nn.ModuleList(
[AttentionBlock(config) for _ in range(config.num_layers)]
)
self.final_norm = RMSNorm(config.embed_dim)
self.depth_norm = RMSNorm(config.embed_dim)
self.depth_kv = nn.Linear(config.embed_dim, 2 * config.depth_dim, bias=False)
self.lm_head = nn.Linear(config.embed_dim, config.vocab_size, bias=False)
self.lm_head.weight = self.token_embedding.weight
self.post_init()
nn.init.normal_(self.token_embedding.weight, std=0.02)
for module in self.modules():
if isinstance(module, nn.Linear) and module is not self.lm_head:
nn.init.normal_(module.weight, std=0.02)
# HF loads under no_init_weights(); torch.ones there may leave this
# nonpersistent routing mask uninitialized. It is fixed config state.
for block in self.blocks:
block.horizontal.allowed.fill_(True)
if config.init_scheme == "v1":
# The generic Linear loop above overwrites module-specific schemes.
# Restore them so the run config describes the network that trains.
out_std = 0.02 / math.sqrt(2 * config.num_layers)
for block in self.blocks:
nn.init.normal_(
block.horizontal.router.weight, std=config.router_init_std
)
nn.init.normal_(block.horizontal.o_proj.weight, std=out_std)
nn.init.normal_(block.vertical.out_proj.weight, std=out_std)
for block in self.blocks:
if isinstance(block.horizontal.pos, WarpedRoPE):
block.horizontal.pos.reset_parameters() # exactly standard RoPE at step 0
# Construct after backbone initialization, preserving legacy seeded weights
# when reasoning is disabled. Old saved configs explicitly resolve to none.
self.reasoning = (
FoldUnfoldReasoning(config)
if config.reasoning_mode == "fold_unfold"
else None
)
self.sinusoidal = (
ScaledSinusoidalEmbedding(config.embed_dim)
if config.add_sinusoidal_embeddings
else None
)
if self.reasoning is not None:
for module in self.reasoning.modules():
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, std=0.02)
self.tokenizer = None
@classmethod
def from_baseline(
cls, baseline, bank_router_noise=1e-4, expansion_seed=0, **capacity_config
):
"""Expand a single-bank checkpoint, preserving its initial function to O(noise).
Replicate expert weights into banks and zero the new reasoning readout gates.
The bank router starts near-uniform, NOT exactly zero: replicated banks with a
zero router are a symmetric fixed point (tied banks get identical gradients
and Adam updates forever, so 4 banks/top-2 trains as 2 banks/top-1). Tiny
seeded noise sends different tokens to different bank pairs; because the
banks are still identical, the output changes only by O(noise).
Train with a fresh optimizer; its moments are not compatible with expanded
tensors. The original model is untouched. Only capacity settings may change.
"""
if not (math.isfinite(bank_router_noise) and bank_router_noise > 0):
raise ValueError(
"bank_router_noise must be positive: zero leaves banks tied"
)
allowed = {
"reasoning_mode",
"reasoning_slots",
"reasoning_steps",
"reasoning_heads",
"reasoning_chunk_size",
"reasoning_gate_init",
"expert_banks",
"expert_bank_top_k",
}
if set(capacity_config) - allowed:
raise ValueError(
"from_baseline accepts only reasoning and bank capacity settings"
)
if (
getattr(baseline.config, "expert_banks", 1) != 1
or getattr(baseline, "reasoning", None) is not None
):
raise ValueError(
"from_baseline requires a single-bank model without reasoning"
)
values = baseline.config.to_dict()
values.update(reasoning_mode="fold_unfold", expert_banks=4, expert_bank_top_k=2)
values.update(capacity_config)
config = TAMEConfig.from_dict(values)
# New modules (reasoning) are drawn from a fixed seed, so the expansion is a
# pure function of (baseline, config, expansion_seed) and the caller's RNG is untouched.
with torch.random.fork_rng(devices=[]):
torch.random.default_generator.manual_seed(expansion_seed)
target = cls(config)
target = target.to(
device=baseline.token_embedding.weight.device,
dtype=baseline.token_embedding.weight.dtype,
)
source = baseline.state_dict()
target_state = target.state_dict()
with torch.no_grad():
for name, value in source.items():
if name.endswith((".moe.up", ".moe.down")):
value = value.repeat_interleave(config.expert_banks, dim=0)
target_state[name].copy_(value)
generator = torch.Generator(device="cpu").manual_seed(expansion_seed)
for block in target.blocks:
if block.moe.bank_router is not None:
noise = torch.randn(
block.moe.bank_router.shape, generator=generator
)
block.moe.bank_router.copy_(noise * bank_router_noise)
if target.reasoning is not None:
target.reasoning.unfold_gate.zero_()
target.reasoning.direct_gate.zero_()
target.tokenizer = baseline.tokenizer
import copy
target.generation_config = copy.deepcopy(baseline.generation_config)
return target.train(baseline.training)
# Embedding and tokenizer interfaces
def get_input_embeddings(self):
return self.token_embedding
def load_state_dict(self, state_dict, strict=True, assign=False):
# Pilot checkpoints predate serialization of this fixed routing mask.
# Fill only missing masks; leave every learned tensor untouched.
missing = [
f"blocks.{i}.horizontal.allowed"
for i in range(len(self.blocks))
if f"blocks.{i}.horizontal.allowed" not in state_dict
]
if missing:
state_dict = dict(state_dict)
for key in missing:
state_dict[key] = torch.ones_like(self.get_buffer(key))
return super().load_state_dict(state_dict, strict=strict, assign=assign)
def set_input_embeddings(self, value):
self.token_embedding = value
self.lm_head.weight = value.weight
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, value):
self.lm_head = value
self.token_embedding.weight = value.weight
def attach_tokenizer(self, tokenizer):
import hashlib
import json
if len(tokenizer) != self.config.vocab_size:
raise ValueError("Tokenizer vocabulary does not match model")
for name in ("pad", "bos", "eos"):
if getattr(tokenizer, name + "_token_id") != getattr(
self.config, name + "_token_id"
):
raise ValueError(f"Tokenizer {name} ID does not match model")
backend = json.loads(tokenizer.backend_tokenizer.to_str())
backend.pop("truncation", None)
backend.pop("padding", None)
identity = {
"backend": backend,
"special_ids": {
name: getattr(tokenizer, name + "_token_id")
for name in ("pad", "bos", "eos")
},
}
digest = hashlib.sha256(
json.dumps(identity, sort_keys=True, separators=(",", ":")).encode()
).hexdigest()
if getattr(self.config, "tokenizer_sha256", digest) != digest:
raise ValueError(
"Tokenizer identity differs from the checkpoint vocabulary/policy"
)
self.config.tokenizer_sha256 = digest
self.tokenizer = tokenizer
# Depth state and packed forward path
def _write_depth(self, x):
b, t, _ = x.shape
k, v = self.depth_kv(self.depth_norm(x)).chunk(2, -1)
shape = (
b,
t,
self.config.depth_heads,
self.config.depth_dim // self.config.depth_heads,
)
return k.reshape(shape), v.reshape(shape)
def _forward_unpadded(
self,
input_ids,
labels=None,
attention_mask=None,
use_cache=False,
past_key_values=None,
start_pos=0,
return_logits=None,
reasoning_steps=None,
logit_positions=None,
**kwargs,
):
if (
input_ids.ndim != 2
or not 0 < input_ids.shape[1] <= self.config.max_seq_len - start_pos
):
raise ValueError("Input must be [batch, tokens] within max_seq_len")
# Packed training has no padding. Do not accept a mask and silently ignore it.
if attention_mask is not None:
raise ValueError(
"Use packed unpadded input_ids; padding masks are not supported"
)
if (use_cache or past_key_values is not None) and (
labels is not None or torch.is_grad_enabled()
):
raise ValueError(
"KV cache requires inference without labels under torch.no_grad()"
)
caches = (
past_key_values
if past_key_values is not None
else ([{} for _ in self.blocks] if use_cache else None)
)
if caches is not None and len(caches) != len(self.blocks):
raise ValueError("One cache per layer required")
x = self.token_embedding(input_ids)
if self.sinusoidal is not None:
x = x + self.sinusoidal(x, start_pos=start_pos)
bank = [self._write_depth(x)] if self.config.depth_mode == "attention" else []
losses, stats = [], []
for i, block in enumerate(self.blocks):
if (
self.config.checkpoint_backbone
and self.training
and torch.is_grad_enabled()
):
from torch.utils.checkpoint import checkpoint
# Freeze bank membership: later layer writes must not change the
# inputs replayed during backward. Tensor gradients stay intact.
x, loss, info = checkpoint(block, x, tuple(bank), use_reentrant=False)
else:
x, loss, info = block(
x, bank, None if caches is None else caches[i], start_pos
)
losses.append(loss)
stats.append({k: v.detach() for k, v in info.items()})
if self.config.depth_mode == "attention" and i + 1 < len(self.blocks):
bank.append(self._write_depth(x))
if len(bank) > self.config.depth_slots:
bank = (
[bank[0]] + bank[-(self.config.depth_slots - 1) :]
if self.config.depth_slots > 1
else bank[-1:]
)
reasoning_stats = None
if self.reasoning is not None:
x, reasoning_stats = self.reasoning(
x,
cache=None if caches is None else caches[-1],
start_pos=start_pos,
steps=reasoning_steps,
)
x = self.final_norm(x)
aux = torch.stack(losses).mean()
z_loss = torch.stack([info["router_z_loss"] for info in stats]).mean()
primary, total = None, None
if labels is not None:
# Checkpoint the vocabulary projection/CE chunks: no retained [B,T,V]
# logits. Recompute only this head in backward, not the full model.
from torch.utils.checkpoint import checkpoint
states, targets = x[:, :-1].reshape(-1, x.shape[-1]), labels[:, 1:].reshape(
-1
)
if targets.numel() == 0:
raise ValueError("Language-model loss requires at least two tokens")
parts = []
def chunk_loss(h, y):
return F.cross_entropy(
F.linear(h, self.lm_head.weight).float(),
y,
reduction="sum",
ignore_index=-100,
)
for offset in range(0, targets.numel(), self.config.loss_chunk_size):
h, y = (
states[offset : offset + self.config.loss_chunk_size],
targets[offset : offset + self.config.loss_chunk_size],
)
parts.append(
checkpoint(chunk_loss, h, y, use_reentrant=False)
if torch.is_grad_enabled()
else chunk_loss(h, y)
)
primary = torch.stack(parts).sum() / (targets != -100).sum().clamp_min(1)
total = primary + aux
if return_logits is None:
return_logits = labels is None
# Loss above still uses every target. Inference only projects requested
# positions, avoiding the temporary [batch, full_prompt, vocabulary].
logit_states = x if logit_positions is None else x[:, logit_positions]
logits = self.lm_head(logit_states) if return_logits else None
return TAMEOutput(
loss=total,
logits=logits,
past_key_values=caches,
aux_losses={
"primary": None if primary is None else primary.detach(),
"router_balance": aux.detach() - z_loss,
"router_z_loss": z_loss,
"auxiliary_total": aux.detach(),
},
attention_stats=stats,
reasoning_stats=reasoning_stats,
)
# Generation integration
@classmethod
def _supports_default_dynamic_cache(cls):
# A plain DynamicCache has no tile-routing state.
return False
def _prepare_cache_for_generation(
self, generation_config, model_kwargs, *args, **kwargs
):
implementation = generation_config.cache_implementation
if implementation not in (None, "dynamic"):
raise ValueError(
"TAMELM supports its dynamic KV/routing cache; static, offloaded and quantized caches are unsupported"
)
cache = model_kwargs.get("past_key_values")
if cache is not None and not isinstance(cache, TAMECache):
raise TypeError("TAMELM requires TAMECache to preserve tile-routing state")
def _validate_generated_length(
self, generation_config, input_ids_length, has_default_max_length
):
super()._validate_generated_length(
generation_config, input_ids_length, has_default_max_length
)
if generation_config.max_length > self.config.max_seq_len:
raise ValueError(
f"Requested generation exceeds max_seq_len={self.config.max_seq_len}; shorten the prompt or max_new_tokens"
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
use_cache=True,
next_sequence_length=None,
position_ids=None,
**kwargs,
):
seen = past_key_values.get_seq_length() if past_key_values is not None else 0
if seen:
if input_ids.shape[1] > seen:
input_ids = input_ids[:, seen:]
elif next_sequence_length is not None:
input_ids = input_ids[:, -next_sequence_length:]
if position_ids is not None:
position_ids = position_ids[:, -input_ids.shape[1] :]
return {
"input_ids": input_ids.contiguous(),
"past_key_values": past_key_values,
"attention_mask": attention_mask,
"position_ids": position_ids,
"use_cache": use_cache,
"logits_to_keep": 1,
"reasoning_steps": kwargs.get("reasoning_steps"),
}
# Public forward path with padding and cache handling
def forward(
self,
input_ids,
labels=None,
attention_mask=None,
use_cache=False,
past_key_values=None,
start_pos=None,
return_logits=None,
return_dict=None,
position_ids=None,
logits_to_keep=0,
token_type_ids=None,
reasoning_steps=None,
**kwargs,
):
if input_ids.ndim != 2 or input_ids.shape[1] < 1:
raise ValueError("input_ids must be a nonempty [batch, tokens] tensor")
if (
isinstance(logits_to_keep, bool)
or not isinstance(logits_to_keep, int)
or logits_to_keep < 0
):
raise ValueError("logits_to_keep must be a nonnegative integer")
if reasoning_steps is not None:
if self.reasoning is None:
raise ValueError(
"reasoning_steps requires reasoning_mode='fold_unfold'"
)
if (
not isinstance(reasoning_steps, int)
or isinstance(reasoning_steps, bool)
or reasoning_steps < 1
):
raise ValueError("reasoning_steps must be a positive integer")
if token_type_ids is not None and bool(token_type_ids.any()):
raise ValueError("TAMELM does not use token type embeddings")
if kwargs.get("output_attentions") or kwargs.get("output_hidden_states"):
raise NotImplementedError(
"Attention/hidden-state collection is not implemented"
)
if (use_cache or past_key_values is not None) and (
labels is not None or torch.is_grad_enabled()
):
raise ValueError(
"KV cache requires inference without labels under torch.no_grad()"
)
if past_key_values is not None and not isinstance(past_key_values, TAMECache):
raise TypeError("past_key_values must be a TAMECache")
if past_key_values is not None and not use_cache:
raise ValueError("Set use_cache=True when providing past_key_values")
cache = past_key_values
if use_cache and cache is None:
cache = TAMECache(len(self.blocks), self.config.tile)
seen = cache.get_seq_length() if cache is not None else 0
if start_pos is not None and start_pos != seen:
raise ValueError("start_pos disagrees with the cache token count")
batch, length = input_ids.shape
kept_length = min(logits_to_keep, length) if logits_to_keep else length
kept_start = length - kept_length
if labels is not None and labels.shape != input_ids.shape:
raise ValueError("labels must match input_ids")
if attention_mask is None:
current_mask = torch.ones_like(input_ids, dtype=torch.bool)
mask = (
torch.cat((cache.mask, current_mask), dim=1) if seen else current_mask
)
else:
if attention_mask.shape != (batch, seen + length):
raise ValueError("attention_mask must cover cached and new tokens")
if not bool(((attention_mask == 0) | (attention_mask == 1)).all()):
raise ValueError("attention_mask must contain only zeros and ones")
mask = attention_mask.to(device=input_ids.device, dtype=torch.bool)
if seen and not torch.equal(mask[:, :seen], cache.mask):
raise ValueError("attention_mask changes already cached tokens")
current_mask = mask[:, seen:]
if cache is not None:
if not cache.rows:
cache.rows = [[{} for _ in self.blocks] for _ in range(batch)]
if len(cache.rows) != batch:
raise ValueError(
"Cache batch size differs; reorder or expand it explicitly"
)
if (
attention_mask is None
and cache is None
and length > self.config.max_seq_len
) or (
(attention_mask is not None or cache is not None)
and bool((mask.sum(1) > self.config.max_seq_len).any())
):
raise ValueError("Real token count exceeds max_seq_len")
if position_ids is not None:
expected = mask.long().cumsum(-1)[:, seen:] - 1
if position_ids.shape != input_ids.shape or not torch.equal(
position_ids[current_mask], expected[current_mask]
):
raise ValueError("position_ids must follow unpadded token positions")
# Packed training and unpadded full forwards retain the vectorized path.
if cache is None and (attention_mask is None or bool(current_mask.all())):
out = self._forward_unpadded(
input_ids,
labels=labels,
return_logits=return_logits,
reasoning_steps=reasoning_steps,
logit_positions=slice(kept_start, None),
)
else:
if return_logits is None:
return_logits = labels is None
logits = None
weighted_loss, weighted_primary, weighted_aux, counts = [], [], [], []
weighted_z = []
stats, reasoning_stats = [], []
for i in range(batch):
positions = current_mask[i].nonzero(as_tuple=True)[0]
if not positions.numel():
raise ValueError(
"Each sequence must provide at least one real token"
)
ids = input_ids[i : i + 1].index_select(1, positions)
target = (
labels[i : i + 1].index_select(1, positions)
if labels is not None
else None
)
offset = cache.row_length(i) if cache is not None else 0
# Map physical output positions to this row's unpadded states.
retained = positions >= kept_start
native_positions = retained.nonzero(as_tuple=True)[0]
native = self._forward_unpadded(
ids,
labels=target,
use_cache=cache is not None,
past_key_values=cache.rows[i] if cache is not None else None,
start_pos=offset,
return_logits=return_logits,
reasoning_steps=reasoning_steps,
logit_positions=native_positions,
)
if native.logits is not None:
if logits is None:
logits = native.logits.new_zeros(
batch, kept_length, self.config.vocab_size
)
logits[i, positions[retained] - kept_start] = native.logits[0]
if labels is not None:
count = (target[:, 1:] != -100).sum()
counts.append(count)
weighted_loss.append(native.loss * count)
weighted_primary.append(native.aux_losses["primary"] * count)
weighted_aux.append(native.aux_losses["router_balance"] * count)
weighted_z.append(native.aux_losses["router_z_loss"] * count)
stats = native.attention_stats
reasoning_stats.append(native.reasoning_stats)
if cache is not None:
cache.mask = mask.detach().clone()
denom = torch.stack(counts).sum().clamp_min(1) if counts else None
out = TAMEOutput(
loss=torch.stack(weighted_loss).sum() / denom if counts else None,
logits=logits,
past_key_values=cache,
aux_losses={
"router_z_loss": (
torch.stack(weighted_z).sum() / denom
if counts
else input_ids.new_zeros((), dtype=torch.float32)
),
"auxiliary_total": (
(
torch.stack(weighted_aux).sum()
+ torch.stack(weighted_z).sum()
)
/ denom
if counts
else input_ids.new_zeros((), dtype=torch.float32)
),
"primary": (
torch.stack(weighted_primary).sum() / denom if counts else None
),
"router_balance": (
torch.stack(weighted_aux).sum() / denom
if counts
else input_ids.new_zeros((), dtype=torch.float32)
),
},
attention_stats=None,
reasoning_stats=reasoning_stats if self.reasoning is not None else None,
) # per-layer routing summaries are defined for packed batches
return out if return_dict is not False else out.to_tuple()
# Portable Hugging Face export
def export_hf_code(self, directory):
from pathlib import Path
import inspect
import sys
directory = Path(directory)
directory.mkdir(parents=True, exist_ok=True)
source = _NOTEBOOK_SOURCES.get("model")
if source is None:
source = inspect.getsource(sys.modules[type(self).__module__])
# Registration is notebook-local; portable loading uses auto_map.
source = source.split("\nAutoConfig.register(TAMEConfig.model_type")[0]
(directory / "modeling_tamelm.py").write_text(source, encoding="utf-8")
(directory / "THIRD_PARTY_NOTICES.txt").write_text(
THIRD_PARTY_NOTICES, encoding="utf-8"
)
self.config.auto_map = {
"AutoConfig": "modeling_tamelm.TAMEConfig",
"AutoModelForCausalLM": "modeling_tamelm.TAMELM",
}
self.config.architectures = ["TAMELM"]
self.config.save_pretrained(directory)
self.generation_config.save_pretrained(directory)
def save_pretrained(self, save_directory, *args, **kwargs):
self.export_hf_code(save_directory)
result = super().save_pretrained(save_directory, *args, **kwargs)
if self.tokenizer is not None:
self.tokenizer.save_pretrained(save_directory)
return result
_capture_cell_source("model")