class HummingNvFp4LinearKernel(NvFp4LinearKernel):
"""Humming GEMM Kernel for NVFP4."""
@classmethod
def is_supported(
cls, compute_capability: int | None = None
) -> tuple[bool, str | None]:
if not current_platform.is_cuda():
return False, "Humming only supported on CUDA"
if not current_platform.has_device_capability(75):
return False, "Humming only supported on SM75+"
return True, None
@classmethod
def can_implement(cls, config: NvFp4LinearLayerConfig) -> tuple[bool, str | None]:
return True, None
def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
# Route through humming's compressed-tensors nvfp4 loader (same path as
# the MoE oracle); the native group_tensor schema mishandles a scalar
# global scale.
quant_config = {
"quant_method": "compressed-tensors",
"format": "nvfp4-pack-quantized",
"type": "float",
"num_bits": 4,
"strategy": "group",
"group_size": 16,
}
# CT pack-quantized reads `weight_packed`; the scheme renamed it to `weight`.
if not hasattr(layer, "weight_packed"):
layer.weight_packed = layer.weight
del layer.weight
# The CT linear scheme inverts the global scale (1/scale) for
# marlin/cutlass; humming wants the original.
layer.weight_global_scale = torch.nn.Parameter(
1.0 / layer.weight_global_scale, requires_grad=False
)
prepare_humming_layer(layer, quant_config)
def apply_weights(
self,
layer: torch.nn.Module,
x: torch.Tensor,
bias: torch.Tensor | None = None,
) -> torch.Tensor:
from vllm.utils.humming import HummingMethod
flatten_inputs = x.view(-1, x.size(-1))
output = HummingMethod.forward_layer(
layer=layer,
inputs=flatten_inputs,
compute_config=layer.compute_config,
)
return output.view(*x.shape[:-1], output.size(-1))