"""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. Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. 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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 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")