File size: 9,517 Bytes
b6a8400 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 | # -*- coding: utf-8 -*-
"""Nunchaku parameter packer for QwenImage transformer blocks.
Encapsulates NVFP4 (SVDQW4A4Linear) and AWQ INT4 (AWQW4A16Linear) quantization
and memory-layout transformation for Nunchaku inference.
"""
import sys
import os
import torch
# Ensure packages/deepcompressor is available
PACKAGE_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", "packages", "deepcompressor"))
if PACKAGE_ROOT not in sys.path:
sys.path.insert(0, PACKAGE_ROOT)
from deepcompressor.data.dtype import QDType
from deepcompressor.quantizer.config.base import QuantizerConfig
from deepcompressor.quantizer.processor import Quantizer
from deepcompressor.backend.nunchaku.convert import (
convert_to_nunchaku_w4x4y16_linear_state_dict,
convert_to_nunchaku_w4x16_adanorm_zero_state_dict,
)
def quantize_and_pack_nvfp4_linear(
weight: torch.Tensor,
bias: torch.Tensor | None = None,
smooth: torch.Tensor | None = None,
lora: tuple[torch.Tensor, torch.Tensor] | None = None,
per_channel: bool = False,
device: str = "cuda",
) -> dict[str, torch.Tensor]:
"""Quantize a linear layer to NVFP4 and pack into Nunchaku SVDQ format.
Args:
weight: Original weight tensor [out_features, in_features] in BF16/FP16.
bias: Optional bias tensor [out_features].
smooth: Optional channel smoothing factors [in_features].
lora: Optional tuple of (lora_down, lora_up) where:
lora_down has shape [rank, in_features] and
lora_up has shape [out_features, rank].
per_channel: If True, uses per-channel scales (wcscales); otherwise per-tensor (wtscale).
device: Device to perform quantization on.
Returns:
dict containing:
- qweight: [out_features, in_features // 2] int8
- wscales: [in_features // 16, out_features] float8_e4m3fn
- wcscales (if per_channel) or wtscale (if not per_channel)
- bias: [out_features]
- smooth_factor: [in_features]
- smooth_factor_orig: [in_features]
- proj_down: [in_features, rank]
- proj_up: [out_features, rank]
"""
weight = weight.detach().clone().to(device=device)
if bias is not None:
bias = bias.detach().clone().to(device=device)
else:
bias = torch.zeros(weight.shape[0], dtype=weight.dtype, device=device)
if smooth is not None:
smooth = smooth.detach().clone().to(device=device)
else:
smooth = torch.ones(weight.shape[1], dtype=weight.dtype, device=device)
if lora is not None:
lora_down, lora_up = lora
lora = (lora_down.detach().clone().to(device=device), lora_up.detach().clone().to(device=device))
# Configure NVFP4 quantizer
# Group shapes: 1st level is per-channel ([1, -1]) or per-tensor ([-1, -1]), 2nd level is group 16
first_level = [1, -1] if per_channel else [-1, -1]
cfg = QuantizerConfig(
dtype=QDType.sfp4_e2m1_all,
group_shapes=[first_level, [1, 16, 1, 1, 1]],
scale_dtypes=[None, QDType.sfp8_e4m3_nan],
)
q = Quantizer(config=cfg, develop_dtype=torch.float32)
# Compute effective residual weight if lora is present
if lora is not None:
# unsmoothed weight for quantization
w_res = weight - (lora[1] @ lora[0])
else:
w_res = weight
res = q.quantize(w_res, return_with_dequant=True, return_with_quant=True)
scale_sd = res.scale.state_dict("scale")
sd = convert_to_nunchaku_w4x4y16_linear_state_dict(
weight=w_res,
scale=scale_sd["scale.0"],
subscale=scale_sd["scale.1"],
bias=bias,
smooth=smooth,
lora=lora,
float_point=True,
)
# Rename keys to match Nunchaku SVDQW4A4Linear parameter names
packed: dict[str, torch.Tensor] = {}
packed["qweight"] = sd["qweight"].cpu()
packed["wscales"] = sd["wscales"].cpu()
if per_channel:
packed["wcscales"] = sd["wcscales"].cpu()
else:
packed["wtscale"] = sd["wtscale"].cpu()
packed["bias"] = sd["bias"].cpu()
packed["smooth_factor"] = sd["smooth"].cpu()
packed["smooth_factor_orig"] = sd["smooth_orig"].cpu()
if lora is not None:
packed["proj_down"] = sd["lora_down"].cpu()
packed["proj_up"] = sd["lora_up"].cpu()
return packed
def quantize_and_pack_qkv_nvfp4(
weights: list[torch.Tensor],
biases: list[torch.Tensor],
lora: tuple[torch.Tensor, torch.Tensor] | None = None,
device: str = "cuda",
) -> dict[str, torch.Tensor]:
"""Quantize concatenated Q, K, V projections to NVFP4 with independent per-tensor Level-0 scales.
In DeepCompressor / Nunchaku, QKV projections are concatenated, but each individual projection
(e.g. Q, K, V) has its own distinct dynamic range. Applying per-tensor scaling independently to each
projection and then concatenating the Level-0 scales into `wcscales` prevents catastrophic scale underflow
and FP8 subscale clipping (which otherwise causes pebbled noise).
Args:
weights: List of weight tensors [w_q, w_k, w_v] in BF16.
biases: List of bias tensors [b_q, b_k, b_v] in BF16.
lora: Optional tuple of (lora_down, lora_up) for joint low-rank branch.
device: Device to perform quantization on.
Returns:
dict containing:
- qweight: [out_features, in_features // 2] int8
- wscales: [in_features // 16, out_features] float8_e4m3fn
- wcscales: [out_features] bfloat16
- bias: [out_features] bfloat16
- smooth_factor: [in_features] bfloat16
- smooth_factor_orig: [in_features] bfloat16
- proj_down: [in_features, rank] (if lora is present)
- proj_up: [out_features, rank] (if lora is present)
"""
weights = [w.detach().clone().to(device=device, dtype=torch.bfloat16) for w in weights]
biases = [b.detach().clone().to(device=device, dtype=torch.bfloat16) for b in biases]
w_total = torch.cat(weights, dim=0)
b_total = torch.cat(biases, dim=0)
if lora is not None:
ld, lu = lora
ld = ld.detach().clone().to(device=device, dtype=torch.bfloat16)
lu = lu.detach().clone().to(device=device, dtype=torch.bfloat16)
w_res = (w_total.float() - (lu.float() @ ld.float())).to(torch.bfloat16)
cur = 0
w_res_slices = []
for w in weights:
w_res_slices.append(w_res[cur:cur + w.shape[0]])
cur += w.shape[0]
else:
w_res = w_total
w_res_slices = weights
ld, lu = None, None
cfg = QuantizerConfig(
dtype=QDType.sfp4_e2m1_all,
group_shapes=[[-1, -1], [1, 16, 1, 1, 1]],
scale_dtypes=[None, QDType.sfp8_e4m3_nan],
)
q = Quantizer(config=cfg, develop_dtype=torch.float32)
scales_0 = []
subscales_1 = []
for w_s in w_res_slices:
res = q.quantize(w_s, return_with_quant=True)
sd_s = res.scale.state_dict("scale")
scales_0.append(sd_s["scale.0"].view(-1).expand(w_s.shape[0]).reshape(w_s.shape[0], 1, 1, 1))
subscales_1.append(sd_s["scale.1"])
scale_cat = torch.cat(scales_0, dim=0)
subscale_cat = torch.cat(subscales_1, dim=0)
sd = convert_to_nunchaku_w4x4y16_linear_state_dict(
weight=w_res,
scale=scale_cat,
subscale=subscale_cat,
bias=b_total,
lora=(ld, lu) if lora is not None else None,
float_point=True,
)
packed: dict[str, torch.Tensor] = {
"qweight": sd["qweight"].cpu(),
"wscales": sd["wscales"].cpu(),
"wcscales": sd["wcscales"].cpu(),
"bias": sd["bias"].cpu(),
"smooth_factor": sd["smooth"].cpu(),
"smooth_factor_orig": sd["smooth_orig"].cpu(),
}
if lora is not None:
packed["proj_down"] = sd["lora_down"].cpu()
packed["proj_up"] = sd["lora_up"].cpu()
return packed
def quantize_and_pack_adanorm_linear(
weight: torch.Tensor,
bias: torch.Tensor | None = None,
device: str = "cuda",
) -> dict[str, torch.Tensor]:
"""Quantize a modulation linear layer (img_mod.1 or txt_mod.1) to AWQ INT4 with 6 splits.
Args:
weight: Original weight tensor [18432, 3072] in BF16/FP16.
bias: Original bias tensor [18432] in BF16/FP16.
device: Device to perform quantization on.
Returns:
dict containing:
- qweight: [4608, 1536] int32
- wscales: [48, 18432] bf16
- wzeros: [48, 18432] bf16
- bias: [18432] bf16
"""
weight = weight.detach().clone().to(device=device)
if bias is not None:
bias = bias.detach().clone().to(device=device)
else:
bias = torch.zeros(weight.shape[0], dtype=weight.dtype, device=device)
cfg = QuantizerConfig(
dtype=QDType.sint4,
group_shapes=[[1, 64, 1, 1, 1]],
scale_dtypes=[None],
)
q = Quantizer(config=cfg, develop_dtype=torch.float32)
res = q.quantize(weight, return_with_dequant=True, return_with_quant=True)
scale_sd = res.scale.state_dict("scale")
sd = convert_to_nunchaku_w4x16_adanorm_zero_state_dict(
weight=weight,
scale=scale_sd["scale.0"],
bias=bias,
)
packed: dict[str, torch.Tensor] = {}
packed["qweight"] = sd["qweight"].cpu()
packed["wscales"] = sd["wscales"].cpu()
packed["wzeros"] = sd["wzeros"].cpu()
packed["bias"] = sd["bias"].cpu()
return packed
|