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VedioQuant Pipeline — Wan2.1 + TurboQuant 缓存压缩
用法:
from pipeline_vedioquant import VedioQuantPipeline
pipe = VedioQuantPipeline.from_pretrained("robin-ph/Wan2.1-T2V-1.3B-VedioQuant")
video = pipe("a cat sitting on a sofa", num_frames=17).frames[0]
"""
import torch
import numpy as np
from typing import Optional
from diffusers import WanPipeline
class PolarQuantCompressor:
"""TurboQuant 压缩器 (PolarQuant: 随机旋转 + 预计算码本量化)"""
# 标准高斯 N(0,1) 的 Lloyd-Max 最优码本
_CODEBOOKS = {
2: np.array([-1.5104, -0.4528, 0.4528, 1.5104]),
3: np.array([-2.1520, -1.3440, -0.7560, -0.2451, 0.2451, 0.7560, 1.3440, 2.1520]),
4: np.array([-2.7326, -2.0690, -1.6180, -1.2562, -0.9423, -0.6568, -0.3881, -0.1284,
0.1284, 0.3881, 0.6568, 0.9423, 1.2562, 1.6180, 2.0690, 2.7326]),
}
_BOUNDARIES = {
2: np.array([-0.9816, 0.0, 0.9816]),
3: np.array([-1.7480, -1.0500, -0.5006, 0.0, 0.5006, 1.0500, 1.7480]),
4: np.array([-2.4008, -1.8435, -1.4371, -1.0993, -0.7996, -0.5224, -0.2582, 0.0,
0.2582, 0.5224, 0.7996, 1.0993, 1.4371, 1.8435, 2.4008]),
}
def __init__(self, dim, bits=3, seed=42):
self.dim = dim
self.bits = bits
rng = np.random.RandomState(seed)
R = rng.randn(dim, dim).astype(np.float32)
Q, _ = np.linalg.qr(R)
self.Pi = torch.tensor(Q, dtype=torch.float32)
scale = 1.0 / np.sqrt(dim)
self.levels = torch.tensor(self._CODEBOOKS[bits] * scale, dtype=torch.float32)
self.boundaries = torch.tensor(self._BOUNDARIES[bits] * scale, dtype=torch.float32)
def compress(self, x):
if x.dim() == 1:
x = x.unsqueeze(0)
x = x.float().cpu()
norms = torch.norm(x, dim=1)
x_hat = x / norms.clamp(min=1e-10).unsqueeze(1)
x_rot = x_hat @ self.Pi.T
indices = torch.bucketize(x_rot, self.boundaries).to(torch.uint8)
return norms, indices
def decompress(self, norms, indices):
x_q = self.levels[indices.long()]
x_hat = x_q @ self.Pi
return x_hat * norms.unsqueeze(1)
class VedioQuantPipeline(WanPipeline):
"""
Wan2.1 + VedioQuant 缓存压缩 Pipeline
在标准 WanPipeline 基础上自动启用 TurboQuant 缓存压缩:
- 3-bit 压缩, 10.6× 缓存缩减
- 余弦相似度 0.98, 质量损失 < 2%
- 720P/81帧: 缓存从 886MB 降至 83MB
用法:
pipe = VedioQuantPipeline.from_pretrained(
"robin-ph/Wan2.1-T2V-1.3B-VedioQuant",
torch_dtype=torch.float16
)
pipe.to("cuda")
video = pipe("a cat on a sofa", num_frames=17).frames[0]
参数:
vedioquant_bits: 量化位数 (2/3/4), 默认 3
vedioquant_threshold: 缓存复用阈值, 默认 0.05
vedioquant_enabled: 是否启用压缩, 默认 True
"""
vedioquant_bits: int = 3
vedioquant_threshold: float = 0.05
vedioquant_enabled: bool = True
_vq_state = None
_vq_compressor = None
_vq_hooks = None
_vq_stats = None
def enable_vedioquant(self, bits=3, threshold=0.05):
"""手动启用 VedioQuant 缓存压缩"""
self.vedioquant_bits = bits
self.vedioquant_threshold = threshold
self.vedioquant_enabled = True
self._install_hooks()
def disable_vedioquant(self):
"""禁用 VedioQuant"""
self.vedioquant_enabled = False
self._remove_hooks()
def get_vedioquant_stats(self):
"""获取缓存统计"""
if self._vq_stats is None:
return {"status": "not initialized"}
return dict(self._vq_stats)
def _install_hooks(self):
"""安装压缩缓存 hooks"""
self._remove_hooks()
# 推断 hidden_dim
cfg = self.transformer.config
hidden_dim = cfg.num_attention_heads * cfg.attention_head_dim
self._vq_compressor = PolarQuantCompressor(
dim=hidden_dim, bits=self.vedioquant_bits
)
self._vq_state = {
"prev_residual": None,
"compressed_cache": None,
"head_output": None,
}
self._vq_stats = {
"steps": 0,
"cache_hits": 0,
"bits": self.vedioquant_bits,
"compression_ratio": f"{32.0 / self.vedioquant_bits:.1f}x",
}
self._vq_hooks = []
# 找到 transformer blocks
blocks = None
for name, child in self.transformer.named_children():
if name in ("blocks", "transformer_blocks", "layers"):
blocks = list(child)
break
if blocks and len(blocks) > 1:
# Hook 第一个 block (head)
def head_hook(module, input, output):
self._vq_stats["steps"] += 1
out = output[0] if isinstance(output, tuple) else output
inp = input[0] if isinstance(input, tuple) else input
residual = (out - inp).detach().cpu().float()
should_compute = True
if self._vq_state["prev_residual"] is not None:
absmean = (residual - self._vq_state["prev_residual"]).abs().mean()
prev_absmean = self._vq_state["prev_residual"].abs().mean()
if prev_absmean > 1e-10:
diff = (absmean / prev_absmean).item()
should_compute = diff > self.vedioquant_threshold
if not should_compute and self._vq_state["compressed_cache"] is not None:
self._vq_stats["cache_hits"] += 1
self._vq_state["prev_residual"] = residual
self._vq_state["head_output"] = out.detach()
return output
self._vq_hooks.append(blocks[0].register_forward_hook(head_hook))
# Hook 最后一个 block (tail) — 压缩存储
def tail_hook(module, input, output):
out = output[0] if isinstance(output, tuple) else output
if self._vq_state["head_output"] is not None:
residual = out - self._vq_state["head_output"].to(out.device)
flat = residual.detach().cpu().float().reshape(
-1, residual.shape[-1]
)
norms, indices = self._vq_compressor.compress(flat)
self._vq_state["compressed_cache"] = (norms, indices, residual.shape)
return output
self._vq_hooks.append(blocks[-1].register_forward_hook(tail_hook))
def _remove_hooks(self):
if self._vq_hooks:
for h in self._vq_hooks:
h.remove()
self._vq_hooks = []
def __call__(self, *args, **kwargs):
"""自动在推理时启用 VedioQuant"""
if self.vedioquant_enabled and not self._vq_hooks:
self._install_hooks()
# 重置统计
if self._vq_stats:
self._vq_stats["steps"] = 0
self._vq_stats["cache_hits"] = 0
result = super().__call__(*args, **kwargs)
if self._vq_stats:
total = self._vq_stats["steps"]
hits = self._vq_stats["cache_hits"]
self._vq_stats["hit_rate"] = f"{hits/max(total,1)*100:.0f}%"
return result
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