wehub-resource-sync
70 lines
2.0 KiB
Python
70 lines
2.0 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._custom_ops import scaled_fp4_quant
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from vllm.model_executor.layers.quantization.utils.nvfp4_utils import (
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slice_nvfp4_output,
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swizzle_blockscale,
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)
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from vllm.utils.import_utils import has_fbgemm_gpu
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from .base import NvFp4LinearKernel, NvFp4LinearLayerConfig
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class FbgemmNvFp4LinearKernel(NvFp4LinearKernel):
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"""NVFP4 GEMM via FBGEMM."""
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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 has_fbgemm_gpu():
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return True, None
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return False, "fbgemm_gpu required"
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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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swizzled = swizzle_blockscale(layer.weight_scale.data)
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layer.weight_scale = torch.nn.Parameter(
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swizzled.view(-1).view(torch.uint8), requires_grad=False
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)
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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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import fbgemm_gpu # noqa: F401 - registers torch.ops.fbgemm.*
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output_size = layer.output_size_per_partition
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output_dtype = x.dtype
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output_shape = [*x.shape[:-1], output_size]
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x_fp4, x_blockscale = scaled_fp4_quant(
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x,
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layer.input_global_scale_inv,
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is_sf_swizzled_layout=True,
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backend="fbgemm",
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)
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out = torch.ops.fbgemm.f4f4bf16(
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x_fp4,
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layer.weight,
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x_blockscale.view(-1).view(torch.uint8),
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layer.weight_scale,
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layer.alpha,
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use_mx=False,
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).to(output_dtype)
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out = slice_nvfp4_output(out, output_size)
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if bias is not None:
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out = out + bias
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return out.view(*output_shape)
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