wehub-resource-sync
58 lines
1.8 KiB
Python
58 lines
1.8 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.marlin_utils_fp4 import (
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apply_fp4_marlin_linear,
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is_fp4_marlin_supported,
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prepare_fp4_layer_for_marlin,
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)
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from .base import NvFp4LinearKernel, NvFp4LinearLayerConfig
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logger = init_logger(__name__)
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class MarlinNvFp4LinearKernel(NvFp4LinearKernel):
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"""NVFP4 weight-only GEMM via Marlin (W4A16)."""
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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 is_fp4_marlin_supported():
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return True, None
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return False, "Marlin FP4 not available"
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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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logger.warning_once(
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"Your GPU does not have native support for FP4 computation but "
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"FP4 quantization is being used. Weight-only FP4 compression "
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"will be used leveraging the Marlin kernel. This may degrade "
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"performance for compute-heavy workloads."
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)
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prepare_fp4_layer_for_marlin(layer)
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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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return apply_fp4_marlin_linear(
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input=x,
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weight=layer.weight,
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weight_scale=layer.weight_scale,
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weight_global_scale=layer.weight_global_scale,
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workspace=layer.workspace,
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size_n=layer.output_size_per_partition,
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size_k=layer.input_size_per_partition,
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bias=bias,
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)
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