File size: 8,373 Bytes
919fd68 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 | """Tensor-native hard-knowledge surface — trauma as definitive learn targets.
**Trauma = hard knowledge (positive + negative).** Every hot-path API here
returns ``torch.Tensor`` fields only. JSON / dict receipts live in
``trauma_system.hard_knowledge_surface_receipt`` (explicit boundary adapter).
Schema: nnf.resynthesis.hard_knowledge_surface.v1
"""
from __future__ import annotations
from dataclasses import dataclass
import torch
from torch import Tensor
from resynthesis.trauma_system import (
DEFAULT_HARD_KNOWLEDGE_FAIL_THRESHOLD,
DEFAULT_HARD_WON_EMA_THRESHOLD,
DEFAULT_TRAUMA_EPS,
DEFAULT_TRAUMA_POSITIVE_SCALE,
DEFAULT_TRAUMA_PRESSURE_SCALE,
TensorTraumaState,
)
HARD_KNOWLEDGE_SURFACE_SCHEMA = "nnf.resynthesis.hard_knowledge_surface.v1"
@dataclass(frozen=True)
class HardKnowledgeSurfacePacket:
"""Definitive hard-knowledge surface — all fields are tensors.
Negative pole = gaps still missing from weights/pages (must learn).
Positive pole = hard-won capability after struggle (must preserve).
``definitive_surface_t`` is the combined router bias (repel gaps, attract wins).
"""
schema: str
num_arms: int
negative_gap_mask_t: Tensor
positive_hard_won_mask_t: Tensor
negative_gap_signal_t: Tensor
positive_hard_won_signal_t: Tensor
definitive_surface_t: Tensor
empirical_learnability_t: Tensor
learning_progress_t: Tensor
negative_arm_indices_t: Tensor
positive_arm_indices_t: Tensor
negative_count_t: Tensor
positive_count_t: Tensor
def _threshold_t(value: float, *, ref: Tensor) -> Tensor:
return ref.new_tensor(float(value))
def _top_k_arm_indices_t(
scores_t: Tensor,
mask_t: Tensor,
*,
top_k: int,
) -> Tensor:
"""Ranked arm indices with strictly positive masked score (tensor-only)."""
if scores_t.numel() == 0:
return scores_t.new_empty(0, dtype=torch.long)
masked = scores_t * mask_t.to(dtype=scores_t.dtype)
order = masked.argsort(descending=True, stable=True)
capped = order[: max(0, int(top_k))]
if capped.numel() == 0:
return capped.to(dtype=torch.long)
keep = masked[capped].gt(0.0)
return capped[keep].to(dtype=torch.long)
def hard_knowledge_surface_packet_t(
state: TensorTraumaState,
*,
top_k: int = 64,
negative_threshold: float = DEFAULT_HARD_KNOWLEDGE_FAIL_THRESHOLD,
positive_threshold: float = DEFAULT_HARD_WON_EMA_THRESHOLD,
negative_scale: float = DEFAULT_TRAUMA_PRESSURE_SCALE,
positive_scale: float = DEFAULT_TRAUMA_POSITIVE_SCALE,
device: torch.device | None = None,
) -> HardKnowledgeSurfacePacket:
"""Build the definitive hard-knowledge surface (tensor-native, no host lists)."""
ref = state.fail_ema
if device is not None:
ref = ref.to(device=device)
fails = state.fail_ema.to(device=ref.device, dtype=torch.float32).clamp_min(0.0)
peaks = state.peak_fail_ema.to(device=ref.device, dtype=torch.float32).clamp_min(0.0)
hard_won = state.hard_won_ema.to(device=ref.device, dtype=torch.float32).clamp_min(0.0)
successes = state.success_ema.to(device=ref.device, dtype=torch.float32).clamp_min(0.0)
neg_thr = _threshold_t(negative_threshold, ref=ref)
pos_thr = _threshold_t(positive_threshold, ref=ref)
negative_gap_mask_t = (
(fails >= neg_thr) | (peaks >= neg_thr)
).to(dtype=torch.float32)
positive_hard_won_mask_t = (hard_won >= pos_thr).to(dtype=torch.float32)
negative_gap_signal_t = (fails + peaks) * negative_gap_mask_t
positive_hard_won_signal_t = hard_won * positive_hard_won_mask_t
neg_norm = negative_gap_signal_t.max().clamp_min(DEFAULT_TRAUMA_EPS)
pos_norm = positive_hard_won_signal_t.max().clamp_min(DEFAULT_TRAUMA_EPS)
neg_unit = negative_gap_signal_t / neg_norm
pos_unit = positive_hard_won_signal_t / pos_norm
neg_scaled = torch.where(
negative_gap_mask_t.gt(0.0),
neg_unit * float(negative_scale),
neg_unit.new_zeros(()),
)
pos_scaled = torch.where(
positive_hard_won_mask_t.gt(0.0),
pos_unit * float(positive_scale),
pos_unit.new_zeros(()),
)
# This tensor is added directly to route logits. Positive definitive
# knowledge therefore has positive sign and negative definitive knowledge
# has negative sign: learned_score + pro - anti.
definitive_surface_t = pos_scaled - neg_scaled
current_gap_t = fails + peaks
empirical_learnability_t = (
successes
/ (successes + current_gap_t).clamp_min(DEFAULT_TRAUMA_EPS)
).clamp(0.0, 1.0)
ownership_mass_t = successes + hard_won
learning_progress_t = (
ownership_mass_t
/ (ownership_mass_t + current_gap_t).clamp_min(DEFAULT_TRAUMA_EPS)
).clamp(0.0, 1.0)
mastered_t = positive_hard_won_mask_t.gt(0.0) & negative_gap_mask_t.eq(0.0)
learning_progress_t = torch.where(
mastered_t,
torch.ones_like(learning_progress_t),
learning_progress_t,
)
negative_arm_indices_t = _top_k_arm_indices_t(
negative_gap_signal_t,
negative_gap_mask_t,
top_k=top_k,
)
positive_arm_indices_t = _top_k_arm_indices_t(
positive_hard_won_signal_t,
positive_hard_won_mask_t,
top_k=top_k,
)
negative_count_t = negative_gap_mask_t.sum().to(dtype=torch.long)
positive_count_t = positive_hard_won_mask_t.sum().to(dtype=torch.long)
return HardKnowledgeSurfacePacket(
schema=HARD_KNOWLEDGE_SURFACE_SCHEMA,
num_arms=int(state.num_arms),
negative_gap_mask_t=negative_gap_mask_t,
positive_hard_won_mask_t=positive_hard_won_mask_t,
negative_gap_signal_t=negative_gap_signal_t,
positive_hard_won_signal_t=positive_hard_won_signal_t,
definitive_surface_t=definitive_surface_t,
empirical_learnability_t=empirical_learnability_t,
learning_progress_t=learning_progress_t,
negative_arm_indices_t=negative_arm_indices_t,
positive_arm_indices_t=positive_arm_indices_t,
negative_count_t=negative_count_t,
positive_count_t=positive_count_t,
)
def hard_knowledge_router_bias_t(
state: TensorTraumaState,
*,
device: torch.device | None = None,
) -> Tensor:
"""Per-arm router bias from definitive hard knowledge (tensor-only)."""
return hard_knowledge_surface_packet_t(state, device=device).definitive_surface_t
def hard_knowledge_mitm_target_levels_t(
packet: HardKnowledgeSurfacePacket,
*,
coverage_floor_t: Tensor | None = None,
) -> Tensor:
"""Per-arm hardness levels for MITM targeting (negative + coverage floor)."""
levels = packet.negative_gap_signal_t.clone()
if coverage_floor_t is not None:
floor = coverage_floor_t.to(device=levels.device, dtype=levels.dtype).reshape(
-1
)
if floor.numel() == levels.numel():
levels = torch.maximum(levels, floor * packet.negative_gap_mask_t)
return levels
def hard_knowledge_bell_curve_depth_prior_t(
*,
max_depth: int,
center_depth_t: Tensor,
scale_depth_t: Tensor,
magnitude_t: Tensor,
anneal_t: Tensor,
device: torch.device | None = None,
) -> Tensor:
"""Bell-curve depth prior over ``[1, max_depth]`` — tensor-native MITM scaffold."""
if max_depth < 1:
dev = device or center_depth_t.device
return torch.empty(0, dtype=torch.float32, device=dev)
dev = device or center_depth_t.device
axis = torch.arange(1, int(max_depth) + 1, dtype=torch.float32, device=dev)
center = center_depth_t.reshape(()).to(device=dev, dtype=torch.float32)
scale = scale_depth_t.reshape(()).to(device=dev, dtype=torch.float32).clamp_min(
DEFAULT_TRAUMA_EPS
)
magnitude = magnitude_t.reshape(()).to(device=dev, dtype=torch.float32).clamp_min(0.0)
anneal = anneal_t.reshape(()).to(device=dev, dtype=torch.float32).clamp(0.0, 1.0)
z = (axis - center) / scale
bell = magnitude * torch.exp(-0.5 * z * z)
return torch.nan_to_num(bell * anneal, nan=0.0, posinf=0.0, neginf=0.0)
__all__ = [
"HARD_KNOWLEDGE_SURFACE_SCHEMA",
"HardKnowledgeSurfacePacket",
"hard_knowledge_bell_curve_depth_prior_t",
"hard_knowledge_mitm_target_levels_t",
"hard_knowledge_router_bias_t",
"hard_knowledge_surface_packet_t",
]
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