Jinzhen LinandOpenAI Codex
fcde8e1460
Signed-off-by: Jinzhen Lin <jinzhen.ljz@antgroup.com> Co-authored-by: OpenAI Codex <noreply@openai.com>
82 lines
2.9 KiB
Python
82 lines
2.9 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.humming_utils import (
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apply_humming_linear,
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convert_linear_layer_to_humming_standard,
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get_humming_linear_compute_config,
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prepare_humming_linear_layer_config,
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)
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from vllm.model_executor.layers.quantization.utils.quant_utils import kMxfp4Dynamic
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from vllm.platforms import current_platform
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from vllm.utils.import_utils import has_humming
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from .base import MxFp4LinearKernel, MxFp4LinearLayerConfig
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logger = init_logger(__name__)
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class HummingMxFp4LinearKernel(MxFp4LinearKernel):
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"""Humming GEMM Kernel for MXFP4."""
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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_cuda():
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return False, "Humming only supported on CUDA"
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if not has_humming():
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return False, "Humming is not installed"
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if not current_platform.has_device_capability(75):
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return False, "Humming only supported on SM75+"
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return True, None
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@classmethod
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def can_implement(cls, config: MxFp4LinearLayerConfig) -> tuple[bool, str | None]:
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if config.activation_quant_key not in (None, kMxfp4Dynamic):
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return False, "only supports MXFP4 dynamic or unquantized activations"
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if config.activation_quant_key is not None:
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logger.warning_once(
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"HummingMxFp4LinearKernel is a weight-only (A16) kernel; "
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"the requested activation quantization (%s) is ignored.",
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config.activation_quant_key,
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)
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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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layer.weight_scale.data = layer.weight_scale.data.view(torch.float8_e8m0fnu)
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name_map = {"weight": "weight", "weight_scale": "weight_scale"}
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quant_config = {
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"quant_method": "humming",
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"dtype": "float4e2m1",
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"scale_dtype": "float8e8m0",
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"group_size": 32,
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"weight_scale_type": "group",
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}
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convert_linear_layer_to_humming_standard(layer=layer, name_map=name_map)
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self.layer_config = prepare_humming_linear_layer_config(layer, quant_config)
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self.compute_config = get_humming_linear_compute_config()
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self.locks = torch.zeros(1024, dtype=torch.int32, device=layer.weight.device)
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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_humming_linear(
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layer,
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x,
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layer_config=self.layer_config,
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compute_config=self.compute_config,
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locks=self.locks,
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)
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