355 lines
12 KiB
Python
355 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Online MXFP8 (microscaling FP8, block-32) quantization config and methods."""
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from typing import Any
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import torch
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from torch.nn import Module
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from vllm.logger import init_logger
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from vllm.model_executor.layers.attention import Attention
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from vllm.model_executor.layers.fused_moe import (
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FusedMoE,
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FusedMoEMethodBase,
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)
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from vllm.model_executor.layers.fused_moe.layer import UnquantizedFusedMoEMethod
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from vllm.model_executor.layers.fused_moe.oracle.mxfp8 import (
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select_mxfp8_moe_backend,
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)
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from vllm.model_executor.layers.linear import (
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LinearBase,
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UnquantizedLinearMethod,
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)
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from vllm.model_executor.layers.quantization import QuantizationMethods
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizeMethodBase,
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)
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from vllm.model_executor.layers.quantization.fp8 import (
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Fp8Config,
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Fp8KVCacheMethod,
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Fp8OnlineLinearMethod,
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Fp8OnlineMoEMethod,
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_copy_missing_attrs,
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)
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from vllm.model_executor.layers.quantization.utils.mxfp8_utils import (
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MXFP8_BLOCK_SIZE,
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Mxfp8LinearBackend,
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Mxfp8LinearOp,
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mxfp8_e4m3_quantize,
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swizzle_mxfp8_scale,
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)
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from vllm.model_executor.layers.quantization.utils.quant_utils import (
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is_layer_skipped,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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initialize_single_dummy_weight,
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)
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from vllm.model_executor.parameter import ModelWeightParameter
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from vllm.model_executor.utils import replace_parameter, set_weight_attrs
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from vllm.platforms import current_platform
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logger = init_logger(__name__)
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class Mxfp8Config(Fp8Config):
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"""Config class for online MXFP8 MoE quantization."""
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def __init__(
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self,
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activation_scheme: str = "dynamic",
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ignored_layers: list[str] | None = None,
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) -> None:
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if activation_scheme != "dynamic":
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raise ValueError("mxfp8 only supports dynamic activation scheme.")
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super().__init__(
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is_checkpoint_fp8_serialized=False,
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activation_scheme=activation_scheme,
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ignored_layers=ignored_layers,
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weight_block_size=None,
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)
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@classmethod
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def get_name(cls) -> QuantizationMethods:
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return "mxfp8"
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@classmethod
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def get_min_capability(cls) -> int:
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return 100
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@classmethod
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def from_config(cls, config: dict[str, Any]) -> "Mxfp8Config":
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activation_scheme = cls.get_from_keys_or(
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config, ["activation_scheme"], "dynamic"
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)
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ignored_layers = cls.get_from_keys_or(config, ["ignored_layers"], None)
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if not ignored_layers:
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ignored_layers = cls.get_from_keys_or(
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config, ["modules_to_not_convert"], None
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)
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return cls(
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activation_scheme=activation_scheme,
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ignored_layers=ignored_layers,
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)
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def get_quant_method(
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self, layer: torch.nn.Module, prefix: str
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) -> "QuantizeMethodBase | None":
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if isinstance(layer, LinearBase):
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if is_layer_skipped(
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prefix=prefix,
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ignored_layers=self.ignored_layers,
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fused_mapping=self.packed_modules_mapping,
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skip_with_substr=True,
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):
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return UnquantizedLinearMethod()
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return Mxfp8OnlineLinearMethod(self)
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elif isinstance(layer, FusedMoE):
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if is_layer_skipped(
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prefix=prefix,
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ignored_layers=self.ignored_layers,
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fused_mapping=self.packed_modules_mapping,
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skip_with_substr=True,
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):
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return UnquantizedFusedMoEMethod(layer.moe_config)
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return Mxfp8OnlineMoEMethod(self, layer)
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elif isinstance(layer, Attention):
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return Fp8KVCacheMethod(self)
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return None
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class Mxfp8OnlineLinearMethod(Fp8OnlineLinearMethod):
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"""Online MXFP8 linear method.
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Loads bf16/fp16 checkpoints and quantizes weights to MXFP8 (microscaling
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FP8 with block-32 scales) during weight loading.
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Args:
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quant_config: The MXFP8 quantization config.
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"""
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uses_meta_device: bool = True
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def __init__(self, quant_config: "Mxfp8Config"):
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self.quant_config = quant_config
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self.out_dtype = torch.get_default_dtype()
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self.mxfp8_linear = Mxfp8LinearOp(self._select_backend())
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logger.info_once(
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"Using %s backend for MXFP8 GEMM", self.mxfp8_linear.backend.value
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)
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@staticmethod
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def _select_backend() -> Mxfp8LinearBackend:
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try:
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from vllm.utils import flashinfer as fi
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_ = fi.mm_mxfp8
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return Mxfp8LinearBackend.FLASHINFER_CUTLASS
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except Exception:
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logger.warning(
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"FlashInfer mm_mxfp8 not available, "
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"falling back to MXFP8 emulation backend."
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)
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return Mxfp8LinearBackend.EMULATION
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def create_weights(
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self,
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layer: torch.nn.Module,
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input_size_per_partition: int,
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output_partition_sizes: list[int],
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input_size: int,
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output_size: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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if input_size_per_partition % MXFP8_BLOCK_SIZE != 0:
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raise ValueError(
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f"MXFP8 requires input_size_per_partition "
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f"({input_size_per_partition}) to be divisible by "
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f"{MXFP8_BLOCK_SIZE}."
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)
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super().create_weights(
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layer,
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input_size_per_partition,
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output_partition_sizes,
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input_size,
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output_size,
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params_dtype,
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**extra_weight_attrs,
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)
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def process_weights_after_loading(self, layer: Module) -> None:
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if getattr(layer, "_already_called_process_weights_after_loading", False):
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return
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if layer.weight.device == torch.device("meta"):
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weight = ModelWeightParameter(
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data=torch.empty_like(layer.weight, device=layer._load_device),
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input_dim=1,
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output_dim=0,
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weight_loader=layer.weight.weight_loader,
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)
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_copy_missing_attrs(layer.weight, weight)
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layer.register_parameter("weight", weight)
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initialize_single_dummy_weight(layer.weight)
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weight_fp8, weight_scale = mxfp8_e4m3_quantize(layer.weight.contiguous())
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if self.mxfp8_linear.backend == Mxfp8LinearBackend.FLASHINFER_CUTLASS:
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N, K = layer.weight.shape[0], layer.weight.shape[1]
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weight_scale = swizzle_mxfp8_scale(weight_scale, N, K)
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layer.input_scale = None
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replace_parameter(layer, "weight", weight_fp8.data)
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replace_parameter(layer, "weight_scale", weight_scale.data)
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layer._already_called_process_weights_after_loading = True
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def apply(
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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 self.mxfp8_linear.apply(
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input=x,
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weight=layer.weight,
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weight_scale=layer.weight_scale,
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out_dtype=self.out_dtype,
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bias=bias,
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)
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class Mxfp8OnlineMoEMethod(Fp8OnlineMoEMethod):
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"""MoE method for online MXFP8 (block) quantization."""
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uses_meta_device: bool = True
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def __init__(self, quant_config: Fp8Config, layer: torch.nn.Module):
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FusedMoEMethodBase.__init__(self, layer.moe_config)
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self.quant_config = quant_config
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assert not quant_config.is_checkpoint_fp8_serialized
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assert quant_config.activation_scheme == "dynamic"
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self.weight_block_size = [1, MXFP8_BLOCK_SIZE]
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self.block_quant = True
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self.weight_scale_name = "weight_scale"
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self.fp8_backend, self.experts_cls = select_mxfp8_moe_backend(config=self.moe)
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def create_weights(
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self,
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layer: Module,
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num_experts: int,
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hidden_size: int,
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intermediate_size_per_partition: int,
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params_dtype: torch.dtype,
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**extra_weight_attrs,
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):
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if (
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hidden_size % MXFP8_BLOCK_SIZE != 0
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or intermediate_size_per_partition % MXFP8_BLOCK_SIZE != 0
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):
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raise ValueError(
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"Online MXFP8 MoE requires hidden/intermediate sizes divisible "
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f"by {MXFP8_BLOCK_SIZE}."
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)
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super().create_weights(
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layer=layer,
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num_experts=num_experts,
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hidden_size=hidden_size,
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intermediate_size_per_partition=intermediate_size_per_partition,
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params_dtype=params_dtype,
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**extra_weight_attrs,
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)
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w13_weight_scale = torch.nn.Parameter(
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torch.zeros(
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num_experts,
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2 * intermediate_size_per_partition,
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hidden_size // MXFP8_BLOCK_SIZE,
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dtype=torch.uint8,
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),
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requires_grad=False,
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)
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w2_weight_scale = torch.nn.Parameter(
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torch.zeros(
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num_experts,
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hidden_size,
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intermediate_size_per_partition // MXFP8_BLOCK_SIZE,
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dtype=torch.uint8,
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),
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requires_grad=False,
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)
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layer.register_parameter("w13_weight_scale", w13_weight_scale)
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layer.register_parameter("w2_weight_scale", w2_weight_scale)
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set_weight_attrs(w13_weight_scale, extra_weight_attrs)
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set_weight_attrs(w2_weight_scale, extra_weight_attrs)
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layer.weight_block_size = [1, MXFP8_BLOCK_SIZE]
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def _quantize_mxfp8_moe_weight(
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self, weight: torch.Tensor
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""Batch quantization: bf16/fp16 weights -> MXFP8 (fp8 + uint8 scales)."""
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num_batches = weight.size(0)
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w_quant = []
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w_scales = []
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for i in range(num_batches):
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mx_fp8_quant, mx_fp8_scale = mxfp8_e4m3_quantize(
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weight[i], is_sf_swizzled_layout=False
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)
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w_quant.append(mx_fp8_quant)
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w_scales.append(mx_fp8_scale)
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return torch.stack(w_quant), torch.stack(w_scales)
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def process_weights_after_loading(self, layer: Module) -> None:
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if getattr(layer, "_already_called_process_weights_after_loading", False):
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return
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if layer.w13_weight.device == torch.device("meta"):
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w13_weight = torch.nn.Parameter(
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torch.empty_like(layer.w13_weight, device=layer._load_device),
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requires_grad=False,
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)
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set_weight_attrs(
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w13_weight, {"weight_loader": layer.w13_weight.weight_loader}
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)
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_copy_missing_attrs(layer.w13_weight, w13_weight)
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layer.register_parameter("w13_weight", w13_weight)
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initialize_single_dummy_weight(layer.w13_weight)
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if layer.w2_weight.device == torch.device("meta"):
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w2_weight = torch.nn.Parameter(
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torch.empty_like(layer.w2_weight, device=layer._load_device),
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requires_grad=False,
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)
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set_weight_attrs(
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w2_weight, {"weight_loader": layer.w2_weight.weight_loader}
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)
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_copy_missing_attrs(layer.w2_weight, w2_weight)
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layer.register_parameter("w2_weight", w2_weight)
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initialize_single_dummy_weight(layer.w2_weight)
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fp8_dtype = current_platform.fp8_dtype()
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w13 = torch.empty_like(layer.w13_weight, dtype=fp8_dtype)
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w2 = torch.empty_like(layer.w2_weight, dtype=fp8_dtype)
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w13_scale = layer.w13_weight_scale
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w2_scale = layer.w2_weight_scale
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w13, w13_scale = self._quantize_mxfp8_moe_weight(layer.w13_weight)
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w2, w2_scale = self._quantize_mxfp8_moe_weight(layer.w2_weight)
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self._setup_kernel(
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layer,
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w13,
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w2,
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w13_scale,
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w2_scale,
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layer.w13_input_scale,
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layer.w2_input_scale,
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)
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layer._already_called_process_weights_after_loading = True
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