wehub-resource-sync
54 lines
1.6 KiB
Python
54 lines
1.6 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.model_executor.layers.quantization.utils.mxfp4_utils import (
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xpu_mxfp4_quantize as quant_mxfp4,
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)
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from vllm.model_executor.utils import replace_parameter
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from vllm.platforms import current_platform
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from .base import MxFp4LinearKernel, MxFp4LinearLayerConfig
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class XPUMxFp4LinearKernel(MxFp4LinearKernel):
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"""MXFP4 W4A4 GEMM on XPU."""
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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_xpu():
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return False, "XPUMxFp4 only support on XPU"
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return True, None
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@classmethod
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def can_implement(cls, c: MxFp4LinearLayerConfig) -> 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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weight = layer.weight.view(torch.float4_e2m1fn_x2)
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replace_parameter(layer, "weight", weight.data.t())
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weight_scale = layer.weight_scale.view(torch.float8_e8m0fnu)
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weight_scale = weight_scale.t().contiguous()
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replace_parameter(layer, "weight_scale", weight_scale.data)
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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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out_dtype = x.dtype
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x_fp4, x_blockscale = quant_mxfp4(x)
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return torch.ops._xpu_C.fp4_gemm(
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x_fp4,
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layer.weight,
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x_blockscale,
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layer.weight_scale,
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out_dtype,
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bias,
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)
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