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import itertools
import math
from contextlib import nullcontext
from typing import Any
import torch
import torch.nn as nn
from ...utils import is_accelerate_available, is_kernels_available
if is_accelerate_available():
from accelerate import init_empty_weights
_HF_KERNEL_REPO = "rootonchair/nunchaku-lite-kernels"
_HF_KERNEL_VERSION = 2
if is_kernels_available():
from kernels import get_kernel
ops = get_kernel(_HF_KERNEL_REPO, version=_HF_KERNEL_VERSION, trust_remote_code=True).ops
else:
raise ImportError(
"Loading Nunchaku checkpoints requires the Hugging Face `kernels` package. "
"Install it with `pip install kernels`."
)
def _gemm_w4a4(
act: torch.Tensor,
wgt: torch.Tensor,
out: torch.Tensor,
ascales: torch.Tensor,
wscales: torch.Tensor,
lora_act_in: torch.Tensor,
lora_up: torch.Tensor,
bias: torch.Tensor | None,
act_unsigned: bool,
lora_scales: list[float],
nvfp4: bool,
alpha: torch.Tensor | None,
wcscales: torch.Tensor | None,
) -> None:
ops.gemm_w4a4(
act,
wgt,
out,
None,
ascales,
wscales,
None,
None,
lora_act_in,
lora_up,
None,
None,
None,
None,
None,
bias,
None,
None,
None,
act_unsigned,
lora_scales,
False,
nvfp4,
alpha,
wcscales,
None,
None,
None,
0,
)
def replace_with_nunchaku_linear(
model: nn.Module, quantization_config: dict[str, Any], compute_dtype: torch.dtype
) -> int:
num_replaced = 0
svdq_config = quantization_config.get("svdq_w4a4")
awq_config = quantization_config.get("awq_w4a16")
if svdq_config is not None:
num_replaced += _replace_quantize_targets(model, "svdq_w4a4", svdq_config, compute_dtype)
if awq_config is not None:
num_replaced += _replace_quantize_targets(model, "awq_w4a16", awq_config, compute_dtype)
if num_replaced == 0:
raise ValueError(
"Nunchaku compact quantization config must include `svdq_w4a4.targets` or `awq_w4a16.targets`."
)
return num_replaced
class SVDQW4A4Linear(nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
rank: int = 32,
bias: bool = True,
precision: str = "int4",
group_size: int = 64,
torch_dtype: torch.dtype = torch.bfloat16,
device: str | torch.device | None = None,
act_unsigned: bool = False,
):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.rank = rank
self.precision = precision
self.group_size = group_size
self.torch_dtype = torch_dtype
self.act_unsigned = act_unsigned
self.qweight = nn.Parameter(
torch.empty(out_features, in_features // 2, dtype=torch.int8, device=device), requires_grad=False
)
self.bias = (
nn.Parameter(torch.empty(out_features, dtype=torch_dtype, device=device), requires_grad=False)
if bias
else None
)
self.wscales = nn.Parameter(
torch.empty(
in_features // group_size,
out_features,
dtype=torch_dtype if precision == "int4" else torch.float8_e4m3fn,
device=device,
),
requires_grad=False,
)
self.smooth_factor = nn.Parameter(
torch.empty(in_features, dtype=torch_dtype, device=device), requires_grad=False
)
self.proj_down = nn.Parameter(
torch.empty(in_features, rank, dtype=torch_dtype, device=device), requires_grad=False
)
self.proj_up = nn.Parameter(
torch.empty(out_features, rank, dtype=torch_dtype, device=device), requires_grad=False
)
if precision == "nvfp4":
self.wcscales = nn.Parameter(
torch.ones(out_features, dtype=torch_dtype, device=device), requires_grad=False
)
self.wtscale = nn.Parameter(torch.ones(1, dtype=torch_dtype, device=device), requires_grad=False)
else:
self.wcscales = None
self.wtscale = None
def forward(self, x: torch.Tensor, output: torch.Tensor | None = None) -> torch.Tensor:
original_shape = x.shape
channels = x.shape[-1]
x = x.reshape(-1, channels)
rows = x.shape[0]
if output is None:
output = torch.empty(rows, self.out_features, dtype=self.torch_dtype, device=x.device)
pad_size = 256
batch_size_pad = math.ceil(x.shape[0] / pad_size) * pad_size
quantized_x = torch.empty(batch_size_pad, channels // 2, dtype=torch.uint8, device=x.device)
if self.precision == "nvfp4":
ascales = torch.empty(channels // 16, batch_size_pad, dtype=torch.float8_e4m3fn, device=x.device)
else:
ascales = torch.empty(channels // 64, batch_size_pad, dtype=x.dtype, device=x.device)
lora_act = torch.empty(batch_size_pad, self.rank, dtype=torch.float32, device=x.device)
ops.quantize_w4a4_act_fuse_lora(
x,
quantized_x,
ascales,
self.proj_down,
lora_act,
self.smooth_factor,
False,
self.precision == "nvfp4",
)
lora_scales = [1.0] * math.ceil(self.rank / 16)
_gemm_w4a4(
quantized_x,
self.qweight,
output,
ascales,
self.wscales,
lora_act,
self.proj_up,
self.bias,
self.act_unsigned,
lora_scales,
self.precision == "nvfp4",
self.wtscale,
self.wcscales,
)
return output.reshape(*original_shape[:-1], self.out_features)
class AWQW4A16Linear(nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
bias: bool = True,
group_size: int = 64,
torch_dtype: torch.dtype = torch.bfloat16,
device: str | torch.device | None = None,
):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.group_size = group_size
self.qweight = nn.Parameter(
torch.empty(out_features // 4, in_features // 2, dtype=torch.int32, device=device), requires_grad=False
)
self.bias = (
nn.Parameter(torch.empty(out_features, dtype=torch_dtype, device=device), requires_grad=False)
if bias
else None
)
self.wscales = nn.Parameter(
torch.empty(in_features // group_size, out_features, dtype=torch_dtype, device=device), requires_grad=False
)
self.wzeros = nn.Parameter(
torch.empty(in_features // group_size, out_features, dtype=torch_dtype, device=device), requires_grad=False
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if x.shape[-1] != self.in_features:
raise ValueError(
f"AWQW4A16Linear expected input last dimension {self.in_features}, got shape {tuple(x.shape)}."
)
output_shape = (*x.shape[:-1], self.out_features)
x_flat = x.reshape(-1, self.in_features).contiguous()
if x_flat.shape[0] == 0:
output = x.new_empty(output_shape)
elif self._use_gemm(x_flat.shape[0]):
output = ops.awq_gemm_w4a16_g64_int32(x_flat, self.qweight, self.wscales, self.wzeros).reshape(
output_shape
)
else:
output = self._forward_gemv_chunks(x_flat, ops.gemv_awq).reshape(output_shape)
if self.bias is not None:
output = output + self.bias.view([1] * (output.ndim - 1) + [-1])
return output
def _use_gemm(self, rows: int) -> bool:
return rows >= 16 and self.in_features % 64 == 0 and self.out_features % 128 == 0
def _forward_gemv_chunks(self, x_flat: torch.Tensor, gemv) -> torch.Tensor:
outputs = []
for start in range(0, x_flat.shape[0], 8):
chunk = x_flat[start : start + 8]
outputs.append(
gemv(
chunk,
self.qweight,
self.wscales,
self.wzeros,
chunk.shape[0],
self.out_features,
self.in_features,
64,
)
)
return torch.cat(outputs, dim=0)
def _replace_quantize_targets(model: nn.Module, op: str, raw: Any, compute_dtype: torch.dtype) -> int:
precision = raw["precision"]
group_size = raw["group_size"]
targets = raw["targets"]
rank = raw["rank"] if op == "svdq_w4a4" else 0
for target in targets:
try:
module = model.get_submodule(target)
except AttributeError as exc:
raise ValueError(f"Nunchaku target {target!r} does not exist in the model.") from exc
in_features = getattr(module, "in_features", None)
out_features = getattr(module, "out_features", None)
bias = getattr(module, "bias", None)
if not isinstance(in_features, int) or not isinstance(out_features, int):
raise TypeError(f"Nunchaku target {target!r} must expose integer in_features/out_features.")
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
if op == "svdq_w4a4":
replacement = SVDQW4A4Linear(
in_features,
out_features,
rank=rank,
bias=bias is not None,
precision=precision,
group_size=group_size,
torch_dtype=compute_dtype,
)
elif op == "awq_w4a16":
replacement = AWQW4A16Linear(
in_features,
out_features,
bias=bias is not None,
group_size=group_size,
torch_dtype=compute_dtype,
)
_set_submodule(model, target, replacement)
return len(targets)
def _set_submodule(model: nn.Module, path: str, module: nn.Module) -> None:
parent_path, _, child_name = path.rpartition(".")
parent = model.get_submodule(parent_path) if parent_path else model
if child_name.isdigit() and isinstance(parent, (nn.Sequential, nn.ModuleList)):
parent[int(child_name)] = module
else:
setattr(parent, child_name, module)
def check_strict_state_dict_match(model: nn.Module, state_dict: dict[str, Any]) -> None:
expected_keys = {n for n, _ in itertools.chain(model.named_parameters(), model.named_buffers())}
loaded_keys = set(state_dict.keys())
missing_keys = sorted(expected_keys - loaded_keys)
unexpected_keys = sorted(loaded_keys - expected_keys)
if missing_keys or unexpected_keys:
message = "Nunchaku checkpoint keys must exactly match the patched model state dict."
if missing_keys:
message += f" Missing keys: {missing_keys[:10]}"
if len(missing_keys) > 10:
message += f" and {len(missing_keys) - 10} more"
message += "."
if unexpected_keys:
message += f" Unexpected keys: {unexpected_keys[:10]}"
if len(unexpected_keys) > 10:
message += f" and {len(unexpected_keys) - 10} more"
message += "."
raise ValueError(message)
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