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import math
from typing import Any, Callable, Literal
import torch
from torch import Tensor, nn
from transformers import Qwen2ForCausalLM
from transformers.masking_utils import create_causal_mask
from .outputs import RecurrentOutput, answer_margin
from .completion import CompletionHead
from .memory import PromptMemory
from .prefix import fixed_prefix_layer
from torch.utils.checkpoint import checkpoint
from .interfaces import PromptRouter, ReentryBridge, RecurrentController, AffineSuffixController, SharedNumberController, controller_head
from .number_reader import MAX_NUMBER_TOKENS, number_token_ids
def _answer_readout(hidden: Tensor, positions: list[int]) -> Tensor:
# Integer slices avoid MPS's nondeterministic scatter backward for advanced
# indexing/gather. Batches are deliberately tiny; positions sync once per forward.
return torch.stack([hidden[row, position] for row, position in enumerate(positions)])
class DecoderBlock(nn.Module):
"""An ordered group of original decoder objects, with no copies."""
def __init__(self, layers: list[nn.Module]) -> None:
super().__init__()
self.layers = nn.ModuleList(layers)
self.prefix_reuse = False
self.gradient_checkpointing = False
def forward(
self,
hidden: Tensor,
attention_mask: Tensor | None,
position_ids: Tensor,
position_embeddings: tuple[Tensor, Tensor],
memory: PromptMemory | None = None,
) -> Tensor:
for index, layer in enumerate(self.layers):
if memory is not None:
hidden = memory.layer_input(index, hidden)
if self.prefix_reuse and memory is not None:
hidden = fixed_prefix_layer(layer, hidden, position_embeddings, attention_mask,
memory, index, self.gradient_checkpointing)
continue
call = layer
if self.gradient_checkpointing and torch.is_grad_enabled():
def call(x, _layer=layer, **kwargs):
return checkpoint(_layer, x, use_reentrant=False, **kwargs)
hidden = call(
hidden,
attention_mask=attention_mask,
position_ids=position_ids,
position_embeddings=position_embeddings,
past_key_values=None,
use_cache=False,
)
return memory.block_output(hidden) if memory is not None else hidden
class RecurrentQwen(nn.Module):
"""P -> R repeated T times -> C, reusing the supplied model's weights.
Construction freezes and shares the supplied base model in place. Load a
fresh base for an independent reference after adapters have been attached.
Only full attention and default RoPE are supported in Stage 0, as used by
Qwen2.5-0.5B-Instruct. Device/dtype are inherited, never selected here.
"""
def __init__(
self, base: Qwen2ForCausalLM, recurrent_start: int = 6, recurrent_end: int = 18,
*, recurrence_mode: str = "full_sequence",
) -> None:
super().__init__()
if not isinstance(base, Qwen2ForCausalLM):
raise TypeError("Stage 0 requires Qwen2ForCausalLM")
if recurrence_mode not in ("full_sequence", "fixed_prompt"):
raise ValueError("recurrence_mode must be full_sequence or fixed_prompt")
self.recurrence_mode = recurrence_mode
layers = list(base.model.layers)
if not 0 <= recurrent_start < recurrent_end <= len(layers):
raise ValueError("Require 0 <= recurrent_start < recurrent_end <= layer count")
if any(kind != "full_attention" for kind in base.config.layer_types):
raise ValueError("Stage 0 supports full-attention Qwen layers only")
if base.config.rope_parameters["rope_type"] != "default":
raise ValueError("Stage 0 supports default RoPE only")
if getattr(base, "peft_config", None):
raise ValueError("Supply an unadapted base; attach LoRA to recurrence afterwards")
base.requires_grad_(False)
base.zero_grad(set_to_none=True)
base.config.use_cache = False
self.config = base.config
self.recurrent_start = recurrent_start
self.recurrent_end = recurrent_end
self.embed_tokens = base.model.embed_tokens
self.rotary_emb = base.model.rotary_emb
self.prelude = DecoderBlock(layers[:recurrent_start])
self.recurrent = DecoderBlock(layers[recurrent_start:recurrent_end])
self.coda = DecoderBlock(layers[recurrent_end:])
self.norm = base.model.norm
self.lm_head = base.lm_head
self.completion_head: CompletionHead | RecurrentController | None = None
self.router = None
self.bridge = None
self.state_head = None
self.train_scope = "lora"
self.prefix_reuse = False
self.gradient_checkpointing = False
self.train(base.training)
def enable_completion(self, intermediate: int = 128) -> None:
"""Attach a trainable stop readout without changing recurrent inputs."""
if self.completion_head is not None:
raise ValueError("Completion head is already attached")
self.completion_head = CompletionHead(self.config.hidden_size, intermediate).to(
device=self.embed_tokens.weight.device, dtype=self.embed_tokens.weight.dtype)
def configure_executor(self, tokenizer: Any, *, train_scope: str = "recurrent",
bridge: bool = True, direct_readout: bool = True,
controller_size: int = 128, isolated: bool = True,
prefix_reuse: bool = False, gradient_checkpointing: bool = False,
controller_kind: str = 'gru') -> None:
"""Opt-in architecture; legacy forwards/checkpoints keep their semantics."""
if train_scope not in ("lora", "recurrent", "full"):
raise ValueError("train_scope must be lora, recurrent, or full")
if self.recurrence_mode != "fixed_prompt":
raise ValueError("Executor interfaces require fixed_prompt recurrence")
if train_scope != "lora" and any(".lora_" in name for name, _ in self.named_parameters()):
raise ValueError("Full-weight scopes require an unadapted base, not an attached LoRA model")
if prefix_reuse and self.config.attention_dropout != 0:
raise ValueError("Prefix reuse requires zero attention dropout for equivalent execution")
self.train_scope = train_scope
if train_scope == "recurrent":
self.recurrent.requires_grad_(True)
elif train_scope == "full":
self.requires_grad_(True)
device, dtype = self.embed_tokens.weight.device, self.embed_tokens.weight.dtype
if isolated:
self.router = PromptRouter(tokenizer)
if controller_size:
self.completion_head = controller_head(self.config.hidden_size, controller_size, controller_kind).to(device=device, dtype=dtype)
if bridge:
self.bridge = ReentryBridge(self.config.hidden_size).to(device=device, dtype=dtype)
if direct_readout:
self.state_head = nn.Linear(self.config.hidden_size, 26).to(device=device, dtype=dtype)
self.prefix_reuse = prefix_reuse
self.gradient_checkpointing = gradient_checkpointing
for block in (self.prelude, self.recurrent, self.coda):
block.prefix_reuse = prefix_reuse
block.gradient_checkpointing = gradient_checkpointing
def controller_prompt_state(self, input_ids: Tensor, attention_mask: Tensor,
answer_positions: Tensor | None = None) -> Tensor:
"""Frozen native initializer features [B,H_context], without R/C execution.
This interface runs neither R nor C and consumes only token IDs/mask.
GRU uses full-prompt P's answer position; affine_suffix uses the final
eight frozen token embeddings. Shared-number uses routed field-token
embeddings and validity masks. No branch interprets a numeric value.
"""
if input_ids.ndim != 2 or attention_mask.shape != input_ids.shape or not attention_mask.any(-1).all():
raise ValueError('Need aligned nonempty [B,S] prompt IDs and mask')
if answer_positions is None:
answer_positions = (attention_mask.long() * torch.arange(1, input_ids.shape[1] + 1,
device=input_ids.device)).argmax(-1)
if isinstance(self.completion_head, SharedNumberController):
rows = []
for ids, mask in zip(input_ids.detach().cpu().tolist(), attention_mask.detach().cpu().tolist(), strict=True):
valid = tuple(t for t, active in zip(ids, mask, strict=True) if active)
rows.append(number_token_ids(self.router.tokenizer, valid))
with torch.no_grad():
context = self.embed_tokens.weight.new_zeros(len(rows), MAX_NUMBER_TOKENS, self.config.hidden_size + 1)
for i, tokens in enumerate(rows):
context[i, :len(tokens), :-1] = self.embed_tokens(torch.tensor(tokens, device=input_ids.device))
context[i, :len(tokens), -1] = 1
return context.flatten(1)
if isinstance(self.completion_head, AffineSuffixController):
k = self.completion_head.suffix_tokens
if (answer_positions < k - 1).any():
raise ValueError('Affine suffix initialization requires eight final prompt tokens')
with torch.no_grad():
embeddings = self.embed_tokens(input_ids)
return torch.stack([embeddings[i, end - k + 1:end + 1].flatten()
for i, end in enumerate(answer_positions.tolist())])
with torch.no_grad():
context = self.embed_tokens(input_ids)
positions = torch.arange(input_ids.shape[1], device=input_ids.device).unsqueeze(0)
mask = create_causal_mask(config=self.config, inputs_embeds=context,
attention_mask=attention_mask, past_key_values=None, position_ids=positions)
context = self.prelude(context, mask, positions, self.rotary_emb(context, positions))
return _answer_readout(context, answer_positions.tolist())
def forward(
self,
input_ids: Tensor,
attention_mask: Tensor | None = None,
*,
num_loops: int = 1,
position_ids: Tensor | None = None,
answer_positions: Tensor | None = None,
labels: Tensor | None = None,
allowed_token_ids: Tensor | None = None,
return_hidden_states: bool = False,
logits_mode: Literal["answer", "all"] = "answer",
stop_policy: Callable[[int, Tensor, Tensor], bool] | None = None,
completion_threshold: float | None = None,
readout_token_ids: list[int] | None = None,
loop_callback: Callable[[int, Tensor, Tensor | None, Tensor | None], None] | None = None,
) -> RecurrentOutput:
"""Run complete prompt states through shared recurrence, without KV cache.
Inputs/mask: [B,S]; positions: [1,S] or [B,S], default arange(S),
matching ordinary Qwen forward (including padding). Answer positions:
[B], default last unmasked token. Labels: [B,T] *token IDs*, one
target per loop, with an explicit allowed answer set [A]. No CE loss
or causal label shifting is performed. Pass repeated final labels
explicitly if final-answer margins, rather than step margins, are wanted.
Optional stop_policy(t, h_t, answer_logits_t) is inference-only, batch 1,
and may return True to end after loop t. It never sees labels.
completion_threshold enables the checkpoint's hidden-state-only head,
independent of task depth, under the num_loops safety cap (batch 1).
fixed_prompt keeps only the final unmasked position writable; returned
full-shaped states contain fixed first-pass prefix outputs for diagnostics.
loop_callback receives one-based loop, C logits [1,A], optional stop
logits [1], and optional R logits [1,26] after each readout, including
the stopping loop. Detached copies protect inference from observer edits.
It is inference-only; its return value cannot choose when to stop.
"""
if type(num_loops) is not int or num_loops < 1:
raise ValueError("num_loops must be a positive integer")
if loop_callback is not None and (self.training or torch.is_grad_enabled() or
input_ids.shape[0] != 1 or labels is not None or logits_mode != "answer"):
raise ValueError("Loop observation requires eval/no_grad, batch 1, no labels, and answer logits")
if stop_policy is not None and (self.training or torch.is_grad_enabled() or input_ids.shape[0] != 1 or labels is not None or logits_mode != "answer"):
raise ValueError("Stopping requires eval/no_grad, batch 1, no labels, and answer logits")
if completion_threshold is not None:
if (not math.isfinite(completion_threshold) or not 0 < completion_threshold < 1 or
self.completion_head is None or stop_policy is not None):
raise ValueError("Completion stopping requires a head, threshold in (0,1), and no other stop policy")
if self.training or torch.is_grad_enabled() or input_ids.shape[0] != 1 or labels is not None or logits_mode != "answer":
raise ValueError("Completion stopping requires eval/no_grad, batch 1, no labels, and answer logits")
if logits_mode not in ("answer", "all"):
raise ValueError("logits_mode must be 'answer' or 'all'")
if input_ids.ndim != 2 or input_ids.dtype != torch.long or 0 in input_ids.shape:
raise ValueError("input_ids must be a nonempty torch.long [batch, sequence] tensor")
if input_ids.device != self.embed_tokens.weight.device:
raise ValueError("Input IDs and model must share a device")
if (input_ids < 0).any() or (input_ids >= self.config.vocab_size).any():
raise ValueError("Input IDs must be within the model vocabulary")
batch, sequence = input_ids.shape
device = input_ids.device
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
if attention_mask.shape != input_ids.shape or attention_mask.device != device:
raise ValueError("attention_mask must match input_ids shape and device")
if not ((attention_mask == 0) | (attention_mask == 1)).all():
raise ValueError("attention_mask must contain only 0 and 1")
if not attention_mask.bool().any(dim=-1).all():
raise ValueError("Each example must have at least one unmasked token")
if position_ids is None:
position_ids = torch.arange(sequence, device=device).unsqueeze(0)
if (
position_ids.shape not in ((1, sequence), (batch, sequence))
or position_ids.dtype != torch.long
or position_ids.device != device
or (position_ids < 0).any()
):
raise ValueError("position_ids must be nonnegative long [1,S] or [B,S] on the input device")
if answer_positions is None:
indices = torch.arange(sequence, device=device).expand(batch, -1)
answer_positions = indices.masked_fill(~attention_mask.bool(), -1).max(-1).values
if (
answer_positions.shape != (batch,)
or answer_positions.dtype != torch.long
or answer_positions.device != device
or (answer_positions < 0).any()
or (answer_positions >= sequence).any()
):
raise ValueError("answer_positions must be long [batch] valid sequence indices on the input device")
rows = torch.arange(batch, device=device)
if not attention_mask[rows, answer_positions].bool().all():
raise ValueError("Answer positions cannot point to padding")
if self.recurrence_mode == "fixed_prompt":
last = torch.arange(sequence, device=device).expand(batch, -1).masked_fill(~attention_mask.bool(), -1).max(-1).values
if not torch.equal(answer_positions, last):
raise ValueError("fixed_prompt requires the final unmasked position as its working state")
readout_positions = answer_positions.tolist()
if (labels is None) != (allowed_token_ids is None):
raise ValueError("Provide both labels and allowed_token_ids to compute margins")
if labels is not None and labels.shape != (batch, num_loops):
raise ValueError("labels must have shape [batch, num_loops]")
controller_initial = None
if self.router is not None:
if logits_mode != "answer":
raise ValueError("Isolated execution supports answer-position logits only")
# Controller prompt encoding is isolated even for full-model training.
controller_initial = self.controller_prompt_state(input_ids, attention_mask, answer_positions)
input_ids, attention_mask, position_ids = self.router(input_ids, attention_mask)
hidden = self.embed_tokens(input_ids)
causal_mask = create_causal_mask(
config=self.config,
inputs_embeds=hidden,
attention_mask=attention_mask,
past_key_values=None,
position_ids=position_ids,
)
position_embeddings = self.rotary_emb(hidden, position_ids)
block_args = (causal_mask, position_ids, position_embeddings)
hidden = self.prelude(hidden, *block_args)
initial_hidden = hidden if return_hidden_states else None
bridge_initial = _answer_readout(hidden, readout_positions)
controller_memory = (self.completion_head.initialize(controller_initial)
if isinstance(self.completion_head, RecurrentController) else None)
recurrent_memory = coda_memory = None
if self.recurrence_mode == "fixed_prompt":
write_mask = (torch.arange(sequence, device=device)[None, :] == answer_positions[:, None]).unsqueeze(-1)
recurrent_memory, coda_memory = PromptMemory(write_mask), PromptMemory(write_mask)
states, logits, margins, stop_logits, state_logits = [], [], [], [], []
for loop in range(num_loops):
if loop and self.bridge is not None:
working = self.bridge(_answer_readout(hidden, readout_positions), bridge_initial)
hidden = torch.where(write_mask, working[:, None, :], hidden)
hidden = (self.recurrent(hidden, *block_args, memory=recurrent_memory)
if recurrent_memory is not None else self.recurrent(hidden, *block_args))
working = _answer_readout(hidden, readout_positions)
if isinstance(self.completion_head, RecurrentController):
stop, controller_memory = self.completion_head.advance(working, controller_memory)
stop_logits.append(stop)
elif self.completion_head is not None:
stop_logits.append(self.completion_head(working))
if self.state_head is not None:
state_logits.append(self.state_head(working))
if return_hidden_states:
states.append(hidden)
# C stays differentiable even when frozen: readout loss must reach R.
decoded = self.norm(self.coda(hidden, *block_args, memory=coda_memory)
if coda_memory is not None else self.coda(hidden, *block_args))
readout = decoded if logits_mode == "all" else _answer_readout(decoded, readout_positions)
if readout_token_ids is None:
scores = self.lm_head(readout)
else:
if labels is not None:
raise ValueError("Use full-vocabulary readout when requesting token-ID margins")
ids = torch.tensor(readout_token_ids, device=device)
scores = nn.functional.linear(readout, self.lm_head.weight.index_select(0, ids))
logits.append(scores)
if loop_callback is not None:
loop_callback(loop + 1, scores.detach().clone(),
stop_logits[-1].detach().clone() if stop_logits else None,
state_logits[-1].detach().clone() if state_logits else None)
if labels is not None:
answer_scores = _answer_readout(scores, readout_positions) if logits_mode == "all" else scores
margins.append(answer_margin(answer_scores, labels[:, loop], allowed_token_ids))
if stop_policy is not None and stop_policy(loop + 1, hidden, scores):
break
if completion_threshold is not None:
probability = stop_logits[-1].sigmoid().item()
if not math.isfinite(probability):
raise FloatingPointError("Nonfinite completion probability")
if probability >= completion_threshold:
break
return RecurrentOutput(
loop_logits=tuple(logits),
state_logits=torch.stack(state_logits, dim=1) if state_logits else None,
hidden_states=tuple(states) if return_hidden_states else None,
initial_hidden_state=initial_hidden,
margins=torch.stack(margins, dim=1) if margins else None,
stop_logits=torch.stack(stop_logits, dim=1) if stop_logits else None,
)
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