training/fable_router_hybrid.py

"""Trainable router shell for the frozen Fable host and frozen donor experts.

The production campaign keeps host and expert tensors immutable.  This module
adds an explicit host-only route, a bounded expert residual, deterministic
forced routes for counterfactual discovery, and state helpers for the small
router-only checkpoints.
"""
from __future__ import annotations

import contextlib
import math
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Iterator

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint


PROJECTIONS = ("gate_proj", "up_proj", "down_proj")


def inverse_softplus(value: float) -> float:
    if value <= 0:
        raise ValueError("softplus target must be positive")
    return math.log(math.expm1(value))


class FrozenSwiGLUExpert(nn.Module):
    def __init__(self, hidden_size: int = 2048, intermediate_size: int = 512):
        super().__init__()
        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False, device="meta")
        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False, device="meta")
        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False, device="meta")

    def materialize(self, weights: dict[str, torch.Tensor], device: torch.device, dtype: torch.dtype) -> None:
        for name in PROJECTIONS:
            tensor = weights[f"{name}.weight"].to(device=device, dtype=dtype, non_blocking=True)
            module = getattr(self, name)
            module.weight = nn.Parameter(tensor, requires_grad=False)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        if self.gate_proj.weight.device == hidden_states.device:
            return self.down_proj(F.silu(self.gate_proj(hidden_states)) * self.up_proj(hidden_states))

        # Free 16 GiB runtimes cannot safely keep the 6.04 GiB frozen donor
        # bank beside the 5.3 GiB host and long-context activations.  The
        # expert branch is detached by FrozenExpertRouterBlock, so immutable
        # projections can be staged one active expert at a time without
        # retaining a weight-gradient graph.  This preserves arithmetic: bank
        # tensors are cast to the same device/dtype used by resident experts.
        device, dtype = hidden_states.device, hidden_states.dtype
        gate_weight = self.gate_proj.weight.to(device=device, dtype=dtype)
        up_weight = self.up_proj.weight.to(device=device, dtype=dtype)
        activated = F.silu(F.linear(hidden_states, gate_weight)) * F.linear(hidden_states, up_weight)
        del gate_weight, up_weight
        down_weight = self.down_proj.weight.to(device=device, dtype=dtype)
        output = F.linear(activated, down_weight)
        del down_weight
        return output


class ExplicitOffRouter(nn.Module):
    """Expert logits plus a fixed zero-logit host-only class.

    The off class is explicit in the classification/ranking objective but adds
    no new trainable parameter beyond the frozen contract's router gate.
    """

    def __init__(self, hidden_size: int, num_experts: int):
        super().__init__()
        self.gate = nn.Linear(hidden_size, num_experts, bias=False)
        nn.init.normal_(self.gate.weight, mean=0.0, std=0.01)
        self.num_experts = num_experts

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        # Router and scale are the only trainable parameters.  Keep their
        # master precision at FP32 even when the frozen host and experts run in
        # FP16; casting this tiny trainable gate to FP16 caused its task-loss
        # gradient to underflow to exactly zero on T4.
        expert_logits = self.gate(hidden_states.to(self.gate.weight.dtype))
        off_logits = torch.zeros(
            (*expert_logits.shape[:-1], 1),
            dtype=expert_logits.dtype,
            device=expert_logits.device,
        )
        return torch.cat((expert_logits, off_logits), dim=-1)


@dataclass
class RouteTrace:
    logits: torch.Tensor
    selected_experts: torch.Tensor
    selected_weights: torch.Tensor
    off_probability: torch.Tensor
    active_scale: torch.Tensor
    host_std: torch.Tensor
    donor_input_std: torch.Tensor
    expert_std: torch.Tensor
    normalization_factor: torch.Tensor
    residual_std: torch.Tensor
    residual_max_abs: torch.Tensor
    post_add_changed: torch.Tensor


class FrozenExpertRouterBlock(nn.Module):
    """A sparse, bounded residual over one frozen host layer output."""

    def __init__(
        self,
        expert_ids: list[int],
        *,
        hidden_size: int = 2048,
        intermediate_size: int = 512,
        top_k: int = 2,
        initial_scale: float = 0.005,
        maximum_scale: float = 0.1,
        donor_input_normalization: str = "none",
        donor_norm_eps: float = 1e-6,
    ):
        super().__init__()
        if len(expert_ids) != 32 or len(set(expert_ids)) != 32:
            raise ValueError("a frozen bank layer must contain 32 unique experts")
        if not 1 <= top_k <= len(expert_ids):
            raise ValueError("top_k is outside the expert bank")
        self.expert_ids = tuple(int(item) for item in expert_ids)
        self.router = ExplicitOffRouter(hidden_size, len(expert_ids))
        self.experts = nn.ModuleList(
            FrozenSwiGLUExpert(hidden_size, intermediate_size) for _ in expert_ids
        )
        self.expert_scale = nn.Parameter(torch.tensor(inverse_softplus(initial_scale)))
        self.top_k = top_k
        self.maximum_scale = float(maximum_scale)
        if donor_input_normalization not in {"none", "unit_rms", "donor_rms"}:
            raise ValueError("unsupported donor input normalization")
        self.donor_input_normalization = donor_input_normalization
        self.donor_norm_eps = float(donor_norm_eps)
        self.register_buffer("donor_norm_weight", torch.ones(hidden_size), persistent=False)
        self._donor_norm_loaded = donor_input_normalization != "donor_rms"
        self.donor_norm_weight_transform: str | None = None
        self.enabled = True
        self.checkpoint_enabled = False
        self._forced_expert: int | None = None
        self._forced_scale: float | None = None
        self.last_trace: RouteTrace | None = None

    @property
    def off_class_index(self) -> int:
        return len(self.expert_ids)

    @contextlib.contextmanager
    def forced_route(self, local_expert: int | None, scale: float = 0.025) -> Iterator[None]:
        if local_expert is not None and not 0 <= local_expert < len(self.expert_ids):
            raise ValueError("forced expert index is outside the local bank")
        old_expert, old_scale = self._forced_expert, self._forced_scale
        self._forced_expert, self._forced_scale = local_expert, float(scale)
        try:
            yield
        finally:
            self._forced_expert, self._forced_scale = old_expert, old_scale

    def load_router_warmstart(self, weight: torch.Tensor) -> None:
        if tuple(weight.shape) != tuple(self.router.gate.weight.shape):
            raise ValueError(
                f"router warm-start shape {tuple(weight.shape)} does not match "
                f"{tuple(self.router.gate.weight.shape)}"
            )
        with torch.no_grad():
            self.router.gate.weight.copy_(weight.to(self.router.gate.weight))

    def load_donor_norm(
        self,
        weight: torch.Tensor,
        *,
        device: torch.device,
        dtype: torch.dtype,
        stored_weight_transform: str,
    ) -> None:
        if tuple(weight.shape) != tuple(self.donor_norm_weight.shape):
            raise ValueError(f"donor RMSNorm shape mismatch: {tuple(weight.shape)}")
        if stored_weight_transform == "one_plus_stored_delta":
            effective_weight = 1.0 + weight.float()
        elif stored_weight_transform == "identity":
            effective_weight = weight.float()
        else:
            raise ValueError(f"unsupported donor RMSNorm stored-weight transform: {stored_weight_transform}")
        self.donor_norm_weight = effective_weight.to(device=device, dtype=dtype).contiguous()
        self.donor_norm_weight_transform = stored_weight_transform
        self._donor_norm_loaded = True

    def materialize_experts(
        self,
        bank_path: Path,
        layer: int,
        *,
        device: torch.device,
        dtype: torch.dtype,
    ) -> None:
        from safetensors import safe_open

        with safe_open(str(bank_path), framework="pt", device="cpu") as bank:
            for local_index, global_expert in enumerate(self.expert_ids):
                prefix = f"model.layers.{layer}.mlp.experts.{global_expert}"
                weights = {
                    f"{projection}.weight": bank.get_tensor(f"{prefix}.{projection}.weight")
                    for projection in PROJECTIONS
                }
                self.experts[local_index].materialize(weights, device, dtype)
        for parameter in self.experts.parameters():
            parameter.requires_grad_(False)

    def _expert_output(
        self,
        flat: torch.Tensor,
        selected: torch.Tensor,
        weights: torch.Tensor,
    ) -> torch.Tensor:
        output = torch.zeros_like(flat)
        detached = flat.detach()
        for local_index, expert in enumerate(self.experts):
            positions = (selected == local_index).nonzero(as_tuple=False)
            if positions.numel() == 0:
                continue
            token_indices, rank_indices = positions.unbind(dim=1)
            expert_values = expert(detached.index_select(0, token_indices)).detach()
            weighted = expert_values * weights[token_indices, rank_indices].unsqueeze(-1)
            output = output.index_add(0, token_indices, weighted)
        return output

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        if not self.enabled:
            self.last_trace = None
            return hidden_states
        original_shape = hidden_states.shape
        flat = hidden_states.reshape(-1, original_shape[-1])
        donor_input = flat
        if self.donor_input_normalization in {"unit_rms", "donor_rms"}:
            if not self._donor_norm_loaded:
                raise RuntimeError("exact donor RMSNorm weight was not loaded")
            norm_weight = self.donor_norm_weight.float() if self.donor_input_normalization == "donor_rms" else None
            donor_input = F.rms_norm(
                flat.float(), (flat.shape[-1],), weight=norm_weight, eps=self.donor_norm_eps
            ).to(flat.dtype)
        logits = self.router(donor_input)

        if self._forced_expert is not None:
            selected = torch.full(
                (flat.shape[0], 1), self._forced_expert, dtype=torch.long, device=flat.device
            )
            weights = torch.ones((flat.shape[0], 1), dtype=flat.dtype, device=flat.device)
            off_probability = torch.zeros(flat.shape[0], dtype=flat.dtype, device=flat.device)
            scale = torch.as_tensor(self._forced_scale, dtype=flat.dtype, device=flat.device)
        else:
            probabilities = torch.softmax(logits.float(), dim=-1).to(flat.dtype)
            expert_probabilities = probabilities[:, :-1]
            weights, selected = expert_probabilities.topk(self.top_k, dim=-1)
            off_probability = probabilities[:, -1]
            scale = torch.clamp(F.softplus(self.expert_scale), max=self.maximum_scale).to(flat.dtype)

        expert_output = self._expert_output(donor_input, selected, weights)
        host_std = flat.float().std().detach().clamp_min(1e-6)
        donor_input_std = donor_input.float().std().detach().clamp_min(1e-6)
        expert_std = expert_output.float().std().detach().clamp_min(1e-6)
        normalization = torch.clamp(host_std / expert_std, max=2.0).to(flat.dtype)
        expert_output = expert_output * normalization
        residual = scale * expert_output
        routed_flat = flat + residual
        self.last_trace = RouteTrace(
            logits,
            selected,
            weights,
            off_probability,
            scale,
            host_std,
            donor_input_std,
            expert_std,
            normalization.detach(),
            residual.float().std().detach(),
            residual.float().abs().max().detach(),
            torch.count_nonzero((routed_flat - flat).detach()),
        )
        return routed_flat.reshape(original_shape)


class AugmentedHostLayer(nn.Module):
    def __init__(self, host_layer: nn.Module, expert_block: FrozenExpertRouterBlock):
        super().__init__()
        self.host_layer = host_layer
        self.expert_block = expert_block
        self.is_attention_layer = getattr(host_layer, "is_attention_layer", False)

    def forward(self, hidden_states: torch.Tensor, *args: Any, **kwargs: Any) -> torch.Tensor:
        output = self.host_layer(hidden_states, *args, **kwargs)
        if not isinstance(output, torch.Tensor):
            raise TypeError(f"unsupported LFM2 layer output type: {type(output)!r}")
        if self.expert_block.checkpoint_enabled and self.training and torch.is_grad_enabled():
            return checkpoint(
                self.expert_block,
                output,
                use_reentrant=False,
                preserve_rng_state=False,
            )
        return self.expert_block(output)


def attach_router_block(model: nn.Module, layer: int, block: FrozenExpertRouterBlock) -> AugmentedHostLayer:
    layers = model.model.layers
    if not 0 <= layer < len(layers):
        raise ValueError(f"layer {layer} outside host model with {len(layers)} layers")
    wrapper = AugmentedHostLayer(layers[layer], block)
    layers[layer] = wrapper
    return wrapper


def freeze_except_routers(model: nn.Module) -> dict[str, int]:
    counts = {"hostAndExperts": 0, "router": 0, "expertScale": 0, "trainable": 0}
    for name, parameter in model.named_parameters():
        if ".expert_block.router.gate.weight" in name:
            parameter.requires_grad_(True)
            counts["router"] += parameter.numel()
        elif name.endswith(".expert_block.expert_scale"):
            parameter.requires_grad_(True)
            counts["expertScale"] += parameter.numel()
        else:
            parameter.requires_grad_(False)
            counts["hostAndExperts"] += parameter.numel()
        if parameter.requires_grad:
            counts["trainable"] += parameter.numel()
    assert_trainable_isolation(model)
    return counts


def assert_trainable_isolation(model: nn.Module) -> list[str]:
    names = [name for name, parameter in model.named_parameters() if parameter.requires_grad]
    invalid = [
        name
        for name in names
        if ".expert_block.router.gate.weight" not in name
        and not name.endswith(".expert_block.expert_scale")
    ]
    if invalid:
        raise RuntimeError(f"trainable-parameter isolation failed: {invalid[:5]}")
    if not names:
        raise RuntimeError("trainable-parameter isolation found no router parameters")
    return names


def router_state_dict(model: nn.Module) -> dict[str, torch.Tensor]:
    return {
        name: tensor.detach().cpu().contiguous()
        for name, tensor in model.state_dict().items()
        if ".expert_block.router.gate.weight" in name
        or name.endswith(".expert_block.expert_scale")
    }


def load_router_state_dict(model: nn.Module, state: dict[str, torch.Tensor]) -> None:
    expected = set(router_state_dict(model))
    if set(state) != expected:
        raise RuntimeError(
            f"router checkpoint identity mismatch: missing={sorted(expected-set(state))}, "
            f"extra={sorted(set(state)-expected)}"
        )
    current = model.state_dict()
    with torch.no_grad():
        for name, tensor in state.items():
            current[name].copy_(tensor.to(current[name]))


def benefit_targets(host_nll: torch.Tensor, candidate_nll: torch.Tensor, margin: float) -> torch.Tensor:
    """Return the best expert index, or the explicit off class if none earns its cost.

    ``candidate_nll`` is shaped ``[tokens, candidates]`` and must contain
    exact detached counterfactual losses generated outside the served path.
    """
    if host_nll.ndim != 1 or candidate_nll.ndim != 2 or candidate_nll.shape[0] != host_nll.shape[0]:
        raise ValueError("counterfactual NLL shapes are incompatible")
    best_nll, best_index = candidate_nll.min(dim=-1)
    off_index = candidate_nll.shape[-1]
    off = torch.full_like(best_index, off_index)
    return torch.where(best_nll + margin < host_nll, best_index, off)


def benefit_weighted_router_loss(
    logits: torch.Tensor,
    targets: torch.Tensor,
    host_nll: torch.Tensor,
    candidate_nll: torch.Tensor,
) -> torch.Tensor:
    best_nll = candidate_nll.min(dim=-1).values
    benefit = (host_nll - best_nll).clamp_min(0).detach()
    weights = torch.where(targets == logits.shape[-1] - 1, torch.ones_like(benefit), 1 + benefit)
    return (F.cross_entropy(logits.float(), targets, reduction="none") * weights.float()).mean()