wehub-resource-sync
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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import torch
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from vllm.model_executor.layers.quantization.utils.quant_utils import QuantKey
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@dataclass
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class NvFp4LinearLayerConfig:
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"""Configuration for an NVFP4 linear layer.
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All NVFP4 layers share the same structure: packed uint8 weights (2 FP4 values per
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byte), FP8-E4M3 per-block weight scales (group size 16), and scalar global
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scales for both weights and activations.
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"""
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pass
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class NvFp4LinearKernel(ABC):
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"""Base class for NVFP4 quantized linear kernels.
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Each subclass implements a specific GEMM backend (CUTLASS, Marlin, etc).
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The kernel selection mechanism iterates over registered subclasses in
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priority order,calling ``is_supported`` and ``can_implement`` to find the best
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match for the current hardware.
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"""
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def __init__(self, config: NvFp4LinearLayerConfig) -> None:
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assert self.can_implement(config)[0]
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assert self.is_supported()[0]
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self.config = config
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def input_quant_key(self) -> QuantKey | None:
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"""Return the input quantization key supported by this kernel. If the kernel
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does not support input quantization outside of the kernel, return None.
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"""
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return None
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@classmethod
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@abstractmethod
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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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"""Return whether this kernel can run on the current platform."""
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raise NotImplementedError
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@classmethod
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@abstractmethod
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def can_implement(cls, config: NvFp4LinearLayerConfig) -> tuple[bool, str | None]:
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"""Return whether this kernel can handle *config*."""
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raise NotImplementedError
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@abstractmethod
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def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
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"""Transform weights into the format required by this kernel.
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Called once after checkpoint weights have been loaded onto the
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device. Implementations should repack / swizzle / pad weights
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and scales in-place on *layer*.
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"""
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raise NotImplementedError
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@abstractmethod
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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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"""Run the quantized GEMM."""
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raise NotImplementedError
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