File size: 7,995 Bytes
9c41926 | 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 | import torch
import torch.nn as nn
from dataclasses import dataclass
from typing import Dict, Optional, List, Iterator, Tuple, Any
from pathlib import Path
import os
import numpy as np
from .gguf_writer import GGUFWriter, GGML_TYPES
from .quantization import dequantize_factor
@dataclass
class Sub1BitConfig:
codebook_dim: int = 128
energy_threshold: float = 0.95
rank: int = 16
U_bits: float = 0.5
S_bits: float = 2.0
Vt_bits: float = 0.5
model_name: str = "llama-2-7b-sub1bit"
architecture: str = "llama"
class LowRankFactor(torch.nn.Module):
def __init__(self, U: torch.Tensor, S: torch.Tensor, Vt: torch.Tensor):
super().__init__()
self.register_buffer('U', U.half())
self.register_buffer('S', S.half())
self.register_buffer('Vt', Vt.half())
self.rank = U.shape[1]
def forward(self) -> torch.Tensor:
return torch.matmul(self.U * self.S.unsqueeze(0), self.Vt)
def forward_lowrank(self, x: torch.Tensor) -> torch.Tensor:
return torch.matmul(torch.matmul(x, self.Vt.T) * self.S.unsqueeze(0), self.U.T)
class TernaryQuantizedFactor(torch.nn.Module):
def __init__(self, data: torch.Tensor, scale: torch.Tensor, rank: int):
super().__init__()
self.register_buffer('data', data)
self.register_buffer('scale', scale)
self.rank = rank
def forward(self) -> torch.Tensor:
return self.data.float() * self.scale
class Sub1BitLLM(torch.nn.Module):
def __init__(
self,
model_path: str,
config: Optional[Sub1BitConfig] = None,
device: str = "cuda" if torch.cuda.is_available() else "cpu"
):
super().__init__()
self.model_path = model_path
self.config = config or Sub1BitConfig()
self.device = device
self.layers: Dict[int, LowRankFactor] = {}
self.metadata: Dict = {}
@classmethod
def from_fp16(
cls,
model_path: str,
config: Optional[Sub1BitConfig] = None,
checkpoint_dir: Optional[str] = None,
device: str = "cuda" if torch.cuda.is_available() else "cpu"
) -> "Sub1BitLLM":
instance = cls(model_path, config, device)
if checkpoint_dir is None:
checkpoint_dir = Path(model_path).parent / "checkpoints"
else:
checkpoint_dir = Path(checkpoint_dir)
if not checkpoint_dir.exists():
raise FileNotFoundError(f"Checkpoint directory not found: {checkpoint_dir}")
for ckpt_file in sorted(checkpoint_dir.glob("layer_*.pt")):
layer_idx = int(ckpt_file.stem.split("_")[1])
factor = torch.load(ckpt_file, weights_only=False, map_location=device)
U = torch.from_numpy(factor['U']).to(device)
S = torch.from_numpy(factor['S']).to(device)
Vt = torch.from_numpy(factor['Vt']).to(device)
instance.layers[layer_idx] = LowRankFactor(U, S, Vt)
instance.metadata = {
'num_layers': len(instance.layers),
'rank': instance.config.rank,
'energy_threshold': instance.config.energy_threshold
}
return instance
def load_checkpoint(self, checkpoint_path: str) -> "Sub1BitLLM":
checkpoint = torch.load(checkpoint_path, map_location=self.device, weights_only=False)
if 'layers' in checkpoint:
for layer_idx, factor_data in checkpoint['layers'].items():
self.layers[int(layer_idx)] = LowRankFactor(
torch.from_numpy(factor_data['U']).to(self.device),
torch.from_numpy(factor_data['S']).to(self.device),
torch.from_numpy(factor_data['Vt']).to(self.device),
)
return self
def state_dict(self) -> Dict[str, torch.Tensor]:
state = {}
for layer_idx, layer in self.layers.items():
state[f'layers.{layer_idx}.U'] = layer.U
state[f'layers.{layer_idx}.S'] = layer.S
state[f'layers.{layer_idx}.Vt'] = layer.Vt
return state
def forward(self, x: torch.Tensor, layer_indices: Optional[List[int]] = None) -> Dict[int, torch.Tensor]:
outputs = {}
indices = layer_indices if layer_indices is not None else list(self.layers.keys())
for idx in indices:
if idx in self.layers:
outputs[idx] = self.layers[idx].forward_lowrank(x)
return outputs
def get_weight(self, layer_idx: int) -> torch.Tensor:
if layer_idx not in self.layers:
raise KeyError(f"Layer {layer_idx} not found")
return self.layers[layer_idx]()
def iter_layers(self) -> Iterator[Tuple[int, LowRankFactor]]:
for idx in sorted(self.layers.keys()):
yield idx, self.layers[idx]
def compression_stats(self) -> Dict[str, float]:
total_original = 0
total_factor = 0
for _, layer in self.iter_layers():
orig_size = layer.U.shape[0] * layer.Vt.shape[1]
factor_size = layer.U.numel() + layer.S.numel() + layer.Vt.numel()
total_original += orig_size
total_factor += factor_size
return {
'compression_ratio': total_original / total_factor if total_factor > 0 else 0,
'avg_rank': sum(l.rank for _, l in self.iter_layers()) / max(len(self.layers), 1)
}
def to_gguf(self, output_path: str, metadata: Optional[Dict] = None):
writer = GGUFWriter(output_path)
writer.add_key_value("general.architecture", self.config.architecture)
writer.add_key_value("general.name", self.config.model_name)
writer.add_key_value("quantization.type", "sub1bit_lowrank")
writer.add_key_value("quantization.U_bits", self.config.U_bits)
writer.add_key_value("quantization.S_bits", self.config.S_bits)
writer.add_key_value("quantization.Vt_bits", self.config.Vt_bits)
if metadata:
for key, value in metadata.items():
writer.add_key_value(key, value)
for layer_idx, layer in self.iter_layers():
writer.add_tensor(
f"model.layers.{layer_idx}.U",
layer.U.cpu().numpy().astype(np.float16),
GGML_TYPES['float16']
)
writer.add_tensor(
f"model.layers.{layer_idx}.S",
layer.S.cpu().numpy().astype(np.float16),
GGML_TYPES['float16']
)
writer.add_tensor(
f"model.layers.{layer_idx}.Vt",
layer.Vt.cpu().numpy().astype(np.float16),
GGML_TYPES['float16']
)
writer.add_tensor(
f"model.layers.{layer_idx}.rank",
np.array([layer.rank], dtype=np.int32),
GGML_TYPES['int32']
)
writer.write()
return os.path.getsize(output_path)
def from_fp16(
model_path: str,
config: Optional[Sub1BitConfig] = None,
checkpoint_dir: Optional[str] = None,
device: str = "cuda" if torch.cuda.is_available() else "cpu"
) -> Sub1BitLLM:
return Sub1BitLLM.from_fp16(model_path, config, checkpoint_dir, device)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Sub1BitLLM API Demo")
parser.add_argument("--model", type=str, required=True, help="Path to model weights")
parser.add_argument("--checkpoint-dir", type=str, default=None, help="Path to checkpoint directory")
parser.add_argument("--device", type=str, default="cuda", help="Device")
args = parser.parse_args()
config = Sub1BitConfig(
codebook_dim=128,
energy_threshold=0.95,
model_name="llama-2-7b-sub1bit"
)
model = from_fp16(args.model, config=config, checkpoint_dir=args.checkpoint_dir, device=args.device)
print(f"Loaded Sub1BitLLM with {len(model.layers)} layers")
print(f"Compression stats: {model.compression_stats()}")
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