Harry MellorandClaude Opus 5
1eb3694521
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
77 lines
2.5 KiB
Python
77 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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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 MxFp6LinearLayerConfig:
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"""Configuration for an MXFP6 linear layer.
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All MXFP6 layers share the same structure: packed uint8 weights (2 FP4 values per
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byte) and per-block weight scales (group size 32).
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Attributes:
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weight_quant_key: Identifies the weight quantization format. Can be
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kMxfp6E2M3Static or kMxfp6E3M2Static.
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activation_quant_key: Identifies the activation quantization format,
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or `None` when activations must not be quantized.
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"""
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weight_quant_key: QuantKey
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activation_quant_key: QuantKey | None = None
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class MxFp6LinearKernel(ABC):
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"""Base class for MXFP6 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: MxFp6LinearLayerConfig) -> 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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@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: MxFp6LinearLayerConfig) -> 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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