sgl-project--sglang/python/sglang/srt/layers/quantization/petit.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/modelopt.py


import logging
from typing import Any, Dict, List, Optional

import regex as re
import torch
from torch.nn.parameter import Parameter

from sglang.srt.layers.linear import LinearBase
from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
from sglang.srt.layers.quantization.base_config import (
    LinearMethodBase,
    QuantizationConfig,
    QuantizeMethodBase,
)
from sglang.srt.layers.quantization.petit_utils import (
    apply_petit_nvfp4_linear,
    prepare_nvfp4_layer_for_petit,
    verify_petit_nvfp4_supported,
)
from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
from sglang.srt.layers.quantization.utils import is_layer_skipped
from sglang.srt.utils import is_hip

_is_hip = is_hip()

# Initialize logger for the module
logger = logging.getLogger(__name__)


# Configuration class to support the NVFP4 quantized model generated by the ModelOpt quantization tool
class PetitNvFp4Config(QuantizationConfig):
    """Config class for Petit FP4."""

    def __init__(
        self,
        is_checkpoint_nvfp4_serialized: bool = False,
        kv_cache_quant_algo: str = None,
        group_size: int = None,
        exclude_modules: List[str] = None,
    ) -> None:
        self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
        if is_checkpoint_nvfp4_serialized:
            logger.warning(
                "Detected nvfp4 checkpoint. Please note that the "
                "format is experimental and subject to change."
            )
        self.group_size = group_size
        self.kv_cache_quant_algo = kv_cache_quant_algo
        self.exclude_modules = exclude_modules

    @classmethod
    def get_name(cls) -> str:
        return "petit_nvfp4"

    @classmethod
    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
        return [torch.bfloat16, torch.half]

    @classmethod
    def get_min_capability(cls) -> int:
        # Petit supports the gfx90a and gfx942 GPUs
        return 90

    @classmethod
    def get_config_filenames(cls) -> List[str]:
        return ["hf_quant_config.json"]

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> "PetitNvFp4Config":
        quant_config = cls.get_from_keys(config, ["quantization"])
        quant_method = quant_config["quant_algo"]
        group_size = quant_config.get("group_size", None)
        verify_petit_nvfp4_supported(quant_method, group_size)

        is_checkpoint_nvfp4_serialized = "NVFP4" in quant_method
        kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
        if not kv_cache_quant_algo:
            kv_cache_quant_algo = "auto"
        exclude_modules = quant_config.get("exclude_modules", None)
        if not (group_size and kv_cache_quant_algo and (exclude_modules is not None)):
            logger.warning(
                f"group_size: {group_size},"
                f"kv_cache_quant_algo: {kv_cache_quant_algo},"
                f"exclude_modules: {exclude_modules}"
            )
            raise ValueError(
                "NVFP4 quantization requires group size and "
                "kv_cache_quant_algo specified in "
                "hf_quant_config.json"
            )
        return cls(
            is_checkpoint_nvfp4_serialized,
            kv_cache_quant_algo,
            group_size,
            exclude_modules,
        )

    @classmethod
    def override_quantization_method(cls, hf_quant_cfg, user_quant) -> Optional[str]:
        can_convert = cls.is_petit_nvfp4_compatible(hf_quant_cfg)
        if can_convert:
            return cls.get_name()
        return None

    @classmethod
    def is_petit_nvfp4_compatible(cls, quant_config: Dict[str, Any]) -> bool:
        quant_method = quant_config.get("quant_method", "").lower()
        return _is_hip and quant_method == "modelopt"

    def is_layer_excluded(self, prefix: str, exclude_modules: list):
        for pattern in exclude_modules:
            regex_str = pattern.replace(".", r"\.").replace("*", r".*")
            if re.fullmatch(regex_str, prefix):
                return True
        return False

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional["QuantizeMethodBase"]:
        if isinstance(layer, LinearBase):
            if is_layer_skipped(prefix, self.exclude_modules) or self.is_layer_excluded(
                prefix, self.exclude_modules
            ):
                return UnquantizedLinearMethod()
            return PetitNvFp4LinearMethod(self)
        return None

    def get_scaled_act_names(self) -> List[str]:
        return []


class PetitNvFp4LinearMethod(LinearMethodBase):
    """Linear method for NVFP4.
    Supports loading NVFP4 checkpoints with the following structure:

    |Tensor Name           | datatype      |  shape      |
    |----------------------------------------------------|
    |input_scale           | torch.float32 | scalar      |
    |weight                | NVFP4(SE2M1)  | [1, X, y/2] |
    |weight_scale          | FP8-E4M3      | [X, Y]      |
    |weight_scale_2        | torch.float32 | scalar      |

    The weights are quantized per block of 16 elements.
    Args: quant_config: The ModelOpt quantization config.
    """

    def __init__(self, quant_config: PetitNvFp4Config):
        self.quant_config = quant_config

    def create_weights(
        self,
        layer: torch.nn.Module,
        input_size_per_partition: int,
        output_partition_sizes: List[int],
        input_size: int,
        output_size: int,
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ):
        del input_size, output_size
        if not self.quant_config.is_checkpoint_nvfp4_serialized:
            raise ValueError(
                "NVFP4 quantization was selected, "
                " dynamic quantization is not supported."
            )

        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")

        layer.logical_widths = output_partition_sizes

        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition
        if input_size_per_partition % 16 != 0:
            raise ValueError(
                "Unsupported model when in features size is " "not multiple of 16"
            )

        weight_dtype = (
            torch.float8_e4m3fn
            if self.quant_config.is_checkpoint_nvfp4_serialized
            else params_dtype
        )

        weight = ModelWeightParameter(
            data=torch.empty(
                # 2 fp4 data is packed in one uint8 in the input dimension
                output_size_per_partition,
                input_size_per_partition // 2,
                dtype=torch.uint8,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight", weight)

        input_scale = PerTensorScaleParameter(
            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
            weight_loader=weight_loader,
        )

        layer.register_parameter("input_scale", input_scale)

        weight_scale_2 = PerTensorScaleParameter(
            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
            weight_loader=weight_loader,
        )
        layer.register_parameter("weight_scale_2", weight_scale_2)

        weight_scale = ModelWeightParameter(
            data=torch.empty(
                output_size_per_partition,
                input_size_per_partition // self.quant_config.group_size,
                dtype=weight_dtype,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )

        layer.register_parameter("weight_scale", weight_scale)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        input_scale_2 = layer.input_scale.max().to(torch.float32)
        weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
        layer.input_scale = Parameter(input_scale_2, requires_grad=False)
        layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)
        layer.alpha = Parameter(
            layer.input_scale * layer.weight_scale_2, requires_grad=False
        )

        prepare_nvfp4_layer_for_petit(layer)
        del layer.input_scale

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        return apply_petit_nvfp4_linear(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            weight_scale_2=layer.weight_scale_2,
            size_n=layer.output_size_per_partition,
            size_k=layer.input_size_per_partition,
            bias=bias,
        )