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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 | """True binary quantization primitives for selective WAL-TAT conversion."""
from __future__ import annotations
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .quantization import padded_grouped
def q1_g128_physical_bpw(group_size: int = 128, scale_bits: int = 16) -> float:
"""Physical bpw for one sign bit plus one group scale."""
if group_size <= 0:
raise ValueError("group_size must be positive")
return 1.0 + scale_bits / group_size
@torch.no_grad()
def weighted_binary_project(
weight: torch.Tensor,
input_second_moment: torch.Tensor,
*,
group_size: int = 128,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""Diagonal activation-weighted projection to ``{-scale, +scale}``."""
if input_second_moment.ndim != 1 or input_second_moment.numel() != weight.shape[1]:
raise ValueError("input_second_moment must match weight input features")
grouped, padding, size = padded_grouped(weight.detach(), group_size)
moment = input_second_moment.detach().float().clamp_min(0)
if padding:
moment = F.pad(moment, (0, padding))
moment = moment.view(1, -1, size).expand_as(grouped)
codes = torch.where(grouped < 0, -1, 1).to(torch.int8)
denominator = moment.sum(-1)
numerator = (moment * grouped.abs()).sum(-1)
fallback = grouped.abs().mean(-1)
scales = torch.where(
denominator > 0,
numerator / denominator.clamp_min(1e-12),
fallback,
).clamp_min(1e-5)
error = (
moment * (grouped - codes.float() * scales.unsqueeze(-1)).square()
).sum(-1)
return codes, scales, error
def soft_binary_proxy(proxy: torch.Tensor, temperature: float) -> torch.Tensor:
tau = max(float(temperature), 1e-4)
return torch.tanh(proxy / tau)
class ProxyBinaryMatrix(nn.Module):
"""Hard-forward binary codes with a smooth training-only gradient path."""
def __init__(
self,
codes: torch.Tensor,
scales: torch.Tensor,
*,
compute_dtype: torch.dtype,
temperature: float = 0.35,
initial_proxy_magnitude: float = 0.25,
fake_fp16_scale: bool = False,
):
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("binary codes must be in {-1, +1}")
if initial_proxy_magnitude <= 0:
raise ValueError("initial_proxy_magnitude must be positive")
self.proxy_code = nn.Parameter(
codes.detach().float().clone() * float(initial_proxy_magnitude)
)
self.group_scale = nn.Parameter(scales.detach().float().clone())
self.register_buffer("initial_codes", codes.detach().to(torch.int8).clone())
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.proxy_code.shape[1] * self.proxy_code.shape[2]
@property
def group_size(self) -> int:
return self.proxy_code.shape[2]
def hard_codes(self) -> torch.Tensor:
return torch.where(self.proxy_code.detach() < 0, -1, 1).to(torch.int8)
def effective_weight(self) -> torch.Tensor:
soft = soft_binary_proxy(self.proxy_code, self.temperature)
hard = torch.where(self.proxy_code < 0, -1.0, 1.0)
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()
return (code * scale.unsqueeze(-1)).reshape(
self.out_features, self.in_features
).to(self.compute_dtype)
def code_churn(self) -> float:
return float((self.hard_codes() != self.initial_codes).float().mean().item())
@torch.no_grad()
def constrain_(self) -> None:
self.proxy_code.clamp_(-1.5, 1.5)
self.group_scale.clamp_(min=1e-5)
class ProxyBinaryLinear(nn.Module):
def __init__(self, matrix: ProxyBinaryMatrix, 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)
def pack_binary_codes(codes: torch.Tensor) -> torch.Tensor:
"""Pack eight binary signs per byte, little-endian within each byte."""
values = codes.detach().to(torch.int8).contiguous().cpu().reshape(-1)
if values.numel() == 0:
return torch.empty(0, dtype=torch.uint8)
if not torch.all((values == -1) | (values == 1)):
raise ValueError("binary codes must be in {-1, +1}")
bits = (values > 0).to(torch.uint8)
padding = (-bits.numel()) % 8
if padding:
bits = F.pad(bits, (0, padding), value=0)
lanes = bits.view(-1, 8)
packed = torch.zeros(lanes.shape[0], dtype=torch.uint8)
for bit in range(8):
packed |= lanes[:, bit] << bit
return packed
def unpack_binary_codes(packed: torch.Tensor, count: int) -> torch.Tensor:
if count < 0:
raise ValueError("count must be non-negative")
if packed.dtype != torch.uint8:
raise TypeError("packed must use torch.uint8")
value = packed.detach().contiguous().cpu().reshape(-1)
required = (count + 7) // 8
if value.numel() != required:
raise ValueError(f"packed code length is {value.numel()}, expected {required}")
if count == 0:
return torch.empty(0, dtype=torch.int8)
lanes = torch.stack(tuple((value >> bit) & 1 for bit in range(8)), dim=1)
return lanes.reshape(-1)[:count].to(torch.int8).mul(2).sub(1).contiguous()
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