File size: 10,012 Bytes
92edcfa | 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 | """Training-only proxy codes for hard-forward ternary optimization."""
from __future__ import annotations
import torch
import torch.nn as nn
import torch.nn.functional as F
def soft_ternary_proxy(proxy: torch.Tensor, temperature: float) -> torch.Tensor:
"""Smooth three-level staircase with transitions at -0.5 and +0.5."""
tau = max(float(temperature), 1e-4)
return torch.sigmoid((proxy - 0.5) / tau) - torch.sigmoid(
(-proxy - 0.5) / tau
)
def soft_ternary_proxy_derivative(
proxy: torch.Tensor, temperature: float
) -> torch.Tensor:
"""Analytic derivative of :func:`soft_ternary_proxy` with respect to proxy."""
tau = max(float(temperature), 1e-4)
positive = torch.sigmoid((proxy - 0.5) / tau)
negative = torch.sigmoid((-proxy - 0.5) / tau)
return (
positive * (1.0 - positive) + negative * (1.0 - negative)
) / tau
class ProxyTernaryMatrix(nn.Module):
"""Optimize a scalar proxy per weight while always executing hard codes."""
def __init__(
self,
codes: torch.Tensor,
scales: torch.Tensor,
*,
compute_dtype: torch.dtype,
temperature: float = 0.35,
committed_mask: torch.Tensor | None = None,
master_weight: torch.Tensor | None = None,
fake_fp16_scale: bool = False,
initial_proxy_magnitude: float = 1.0,
initial_zero_proxy_boundary: float | None = None,
):
super().__init__()
if codes.ndim != 3 or scales.shape != codes.shape[:2]:
raise ValueError("codes must be [out, groups, group_size] with matching scales")
if not torch.all((codes >= -1) & (codes <= 1)):
raise ValueError("codes must be ternary")
if committed_mask is None:
committed_mask = torch.ones(codes.shape[:2], dtype=torch.bool, device=codes.device)
if committed_mask.shape != codes.shape[:2] or committed_mask.dtype != torch.bool:
raise ValueError("committed_mask must match the code group shape")
if not 0.5 <= initial_proxy_magnitude <= 1.5:
raise ValueError("initial_proxy_magnitude must be in [0.5, 1.5]")
if initial_zero_proxy_boundary is not None:
if not 0.0 < initial_zero_proxy_boundary < 0.5:
raise ValueError(
"initial_zero_proxy_boundary must be strictly inside (0, 0.5)"
)
if master_weight is None:
raise ValueError(
"initial_zero_proxy_boundary requires master_weight for "
"deterministic activation directions"
)
full_in_features = codes.shape[1] * codes.shape[2]
if master_weight is None:
if not committed_mask.all():
raise ValueError("partial proxy matrices require master_weight")
base_weight = torch.zeros_like(codes, dtype=torch.float32)
in_features = full_in_features
else:
if master_weight.ndim != 2 or master_weight.shape[0] != codes.shape[0]:
raise ValueError("master_weight must match the code output dimension")
if not 0 < master_weight.shape[1] <= full_in_features:
raise ValueError("master_weight has an invalid input dimension")
if full_in_features - master_weight.shape[1] >= codes.shape[2]:
raise ValueError("master_weight padding must be smaller than one group")
in_features = int(master_weight.shape[1])
padded = F.pad(
master_weight.detach().float(), (0, full_in_features - in_features)
)
base_weight = padded.view_as(codes)
initial_proxy = codes.detach().float().clone() * float(
initial_proxy_magnitude
)
if initial_zero_proxy_boundary is not None:
# Keep the deployed code exactly zero while placing its continuous
# training proxy close to the nearest hard boundary. The sign of
# the original high-precision weight supplies a deterministic
# direction for possible 0 -> +/-1 transitions. Exact zeros use
# +1 so that initialization never introduces an ambiguous sign.
source_direction = torch.where(
base_weight >= 0,
torch.ones_like(base_weight),
-torch.ones_like(base_weight),
)
eligible_zero = (codes == 0) & committed_mask.unsqueeze(-1)
boundary_proxy = source_direction * float(initial_zero_proxy_boundary)
initial_proxy = torch.where(
eligible_zero, boundary_proxy, initial_proxy
)
self.proxy_code = nn.Parameter(initial_proxy)
self.group_scale = nn.Parameter(scales.detach().float().clone())
self.register_buffer("initial_codes", codes.detach().to(torch.int8).clone())
self.register_buffer("committed_mask", committed_mask.detach().clone())
self.register_buffer("base_weight", base_weight.detach().clone())
self._in_features = in_features
self.compute_dtype = compute_dtype
self.temperature = float(temperature)
self.fake_fp16_scale = bool(fake_fp16_scale)
@property
def out_features(self) -> int:
return self.proxy_code.shape[0]
@property
def in_features(self) -> int:
return self._in_features
@property
def group_size(self) -> int:
return self.proxy_code.shape[2]
def hard_codes(self) -> torch.Tensor:
return self.proxy_code.detach().round().clamp(-1, 1).to(torch.int8)
def code_churn(self) -> float:
changed = self.hard_codes() != self.initial_codes
return float(changed[self.committed_mask].float().mean().item())
@torch.no_grad()
def deployment_statistics(self, boundary_epsilon: float = 0.05) -> dict:
"""Summarize hard-code stability and scale health on deployed groups."""
if boundary_epsilon < 0:
raise ValueError("boundary_epsilon must be non-negative")
active_proxy = self.proxy_code[self.committed_mask]
active_codes = self.hard_codes()[self.committed_mask]
active_initial = self.initial_codes[self.committed_mask]
active_scales = self.group_scale.detach().abs()[self.committed_mask]
if active_proxy.numel() == 0:
raise ValueError("deployment statistics require committed groups")
boundary_distance = torch.minimum(
(active_proxy - 0.5).abs(), (active_proxy + 0.5).abs()
)
counts = {
str(code): int((active_codes == code).sum().item())
for code in (-1, 0, 1)
}
probabilities = torch.tensor(
list(counts.values()), dtype=torch.float64, device=active_proxy.device
)
probabilities /= probabilities.sum().clamp_min(1)
nonzero = probabilities > 0
entropy = -(probabilities[nonzero] * probabilities[nonzero].log2()).sum()
scale_quantiles = torch.quantile(
active_scales.float(),
torch.tensor([0.0, 0.5, 0.95, 1.0], device=active_scales.device),
)
return {
"code_counts": counts,
"zero_fraction": float((active_codes == 0).float().mean().item()),
"code_entropy_bits": float(entropy.item()),
"code_churn": float((active_codes != active_initial).float().mean().item()),
"proxy_abs_displacement_mean": float(
(active_proxy - active_initial.float()).abs().mean().item()
),
"boundary_epsilon": float(boundary_epsilon),
"near_boundary_fraction": float(
(boundary_distance <= boundary_epsilon).float().mean().item()
),
"boundary_distance_min": float(boundary_distance.min().item()),
"boundary_distance_p01": float(
torch.quantile(boundary_distance.float(), 0.01).item()
),
"scale_min": float(scale_quantiles[0].item()),
"scale_median": float(scale_quantiles[1].item()),
"scale_p95": float(scale_quantiles[2].item()),
"scale_max": float(scale_quantiles[3].item()),
"scale_at_clamp_fraction": float(
(active_scales <= 1.00001e-5).float().mean().item()
),
}
def proxy_anchor_loss(self) -> torch.Tensor:
"""Squared proxy displacement over deployed ternary groups only."""
delta = (self.proxy_code - self.initial_codes.float()).square()
return delta[self.committed_mask].mean()
def effective_weight(self) -> torch.Tensor:
soft = soft_ternary_proxy(self.proxy_code, self.temperature)
hard = self.proxy_code.round().clamp(-1, 1)
# Exact hard forward with the smooth staircase supplying the gradient.
code = hard.detach() + soft - soft.detach()
scale = self.group_scale.abs().clamp_min(1e-5)
if self.fake_fp16_scale:
rounded = scale.half().float()
scale = scale + (rounded - scale).detach()
value = code * scale.unsqueeze(-1)
mixed = torch.where(self.committed_mask.unsqueeze(-1), value, self.base_weight)
return mixed.reshape(self.out_features, -1)[:, : self.in_features].to(
self.compute_dtype
)
@torch.no_grad()
def constrain_(self) -> None:
self.proxy_code.clamp_(-1.5, 1.5)
self.group_scale.clamp_(min=1e-5)
class ProxyTernaryLinear(nn.Module):
def __init__(self, matrix: ProxyTernaryMatrix, bias=None):
super().__init__()
self.matrix = matrix
self.bias = None if bias is None else nn.Parameter(
bias.detach().clone(), requires_grad=False
)
self.in_features = matrix.in_features
self.out_features = matrix.out_features
def forward(self, value: torch.Tensor) -> torch.Tensor:
return F.linear(value, self.matrix.effective_weight().to(value.dtype), self.bias)
|