wehub-resource-sync
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import torch
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from vllm.logger import init_logger
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from vllm.model_executor.layers.quantization.utils.humming_utils import (
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prepare_humming_layer,
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)
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from vllm.platforms import current_platform
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from .base import NvFp4LinearKernel, NvFp4LinearLayerConfig
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logger = init_logger(__name__)
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class HummingNvFp4LinearKernel(NvFp4LinearKernel):
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"""Humming GEMM Kernel for NVFP4."""
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@classmethod
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def is_supported(
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cls, compute_capability: int | None = None
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) -> tuple[bool, str | None]:
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if not current_platform.is_cuda():
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return False, "Humming only supported on CUDA"
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if not current_platform.has_device_capability(75):
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return False, "Humming only supported on SM75+"
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return True, None
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@classmethod
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def can_implement(cls, config: NvFp4LinearLayerConfig) -> tuple[bool, str | None]:
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return True, None
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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# Route through humming's compressed-tensors nvfp4 loader (same path as
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# the MoE oracle); the native group_tensor schema mishandles a scalar
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# global scale.
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quant_config = {
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"quant_method": "compressed-tensors",
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"format": "nvfp4-pack-quantized",
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"type": "float",
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"num_bits": 4,
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"strategy": "group",
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"group_size": 16,
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}
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# CT pack-quantized reads `weight_packed`; the scheme renamed it to `weight`.
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if not hasattr(layer, "weight_packed"):
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layer.weight_packed = layer.weight
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del layer.weight
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# The CT linear scheme inverts the global scale (1/scale) for
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# marlin/cutlass; humming wants the original.
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layer.weight_global_scale = torch.nn.Parameter(
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1.0 / layer.weight_global_scale, requires_grad=False
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)
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prepare_humming_layer(layer, quant_config)
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def apply_weights(
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self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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bias: torch.Tensor | None = None,
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) -> torch.Tensor:
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from vllm.utils.humming import HummingMethod
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flatten_inputs = x.view(-1, x.size(-1))
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output = HummingMethod.forward_layer(
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layer=layer,
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inputs=flatten_inputs,
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compute_config=layer.compute_config,
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)
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return output.view(*x.shape[:-1], output.size(-1))
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