# 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 torch
from torch.nn.parameter import Parameter
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
    apply_fp8_linear,
    cutlass_fp8_supported,
    requantize_with_max_scale,
)

from sglang.srt.layers.attention.base_attn_backend import AttentionBackend
from sglang.srt.layers.linear import LinearBase, LinearMethodBase
from sglang.srt.layers.parameter import ModelWeightParameter, PerTensorScaleParameter
from sglang.srt.layers.quantization.base_config import (
    QuantizationConfig,
    QuantizeMethodBase,
)

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

# Supported activation schemes for the current configuration
ACTIVATION_SCHEMES = ["static"]


class ModelOptFp8Config(QuantizationConfig):
    """Configuration for ModelOpt FP8 quantization, including serialization and compatibility checks."""

    def __init__(self, is_checkpoint_fp8_serialized: bool = False) -> None:
        """
        Args:
            is_checkpoint_fp8_serialized (bool): Indicates if the checkpoint uses serialized FP8 format.
        """
        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
        if is_checkpoint_fp8_serialized:
            logger.warning(
                "Detected ModelOpt FP8 checkpoint. The format is experimental and subject to change."
            )

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

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

    @classmethod
    def get_min_capability(cls) -> int:
        return 89  # Minimum hardware capability (e.g., Hopper GPUs).

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

    @classmethod
    def from_config(cls, config: Dict[str, Any]) -> "ModelOptFp8Config":
        quant_method = cls.get_from_keys(config, ["quantization"]).get("quant_algo")

        if "FP8" not in quant_method:
            raise ValueError(
                "ModelOpt only supports static FP8 quantization in SGLang. "
                "Check the `hf_quant_config.json` file for your model's configuration."
            )

        return cls(is_checkpoint_fp8_serialized=True)

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional["QuantizeMethodBase"]:

        if isinstance(layer, LinearBase):
            return ModelOptFp8LinearMethod(self)
        if isinstance(layer, AttentionBackend):
            return ModelOptFp8KVCacheMethod(self)

        return None

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


class ModelOptFp8LinearMethod(LinearMethodBase):
    """Linear method for ModelOpt static FP8 quantization.

    Supports loading FP8 checkpoints with static weight and activation scales.
    Future support may include dynamic scales.

    **Limitations**:
    1. Only supports per-tensor quantization due to `torch._scaled_mm` limitations.
    2. Only supports the `float8_e4m3fn` data type.

    Args:
        quant_config (ModelOptFp8Config): The ModelOpt quantization configuration.
    """

    def __init__(self, quant_config: ModelOptFp8Config):
        super().__init__()
        self.quant_config = quant_config
        self.cutlass_fp8_supported = cutlass_fp8_supported()

    def create_weights(
        self,
        layer: torch.nn.Module,
        input_size_per_partition: int,
        output_partition_sizes: List[int],
        params_dtype: torch.dtype,
        **extra_weight_attrs,
    ) -> None:
        """Creates and registers weights, weight scales, and input scales for FP8 quantization."""
        output_size_per_partition = sum(output_partition_sizes)
        weight_loader = extra_weight_attrs.get("weight_loader")
        weight_dtype = (
            torch.float8_e4m3fn
            if self.quant_config.is_checkpoint_fp8_serialized
            else params_dtype
        )

        # Set layer attributes
        layer.logical_widths = output_partition_sizes
        layer.input_size_per_partition = input_size_per_partition
        layer.output_size_per_partition = output_size_per_partition

        # Register weight
        layer.register_parameter(
            "weight",
            ModelWeightParameter(
                data=torch.empty(
                    output_size_per_partition,
                    input_size_per_partition,
                    dtype=weight_dtype,
                ),
                input_dim=1,
                output_dim=0,
                weight_loader=weight_loader,
            ),
        )

        if self.quant_config.is_checkpoint_fp8_serialized:
            # Register weight and input scales
            for scale_name in ["weight_scale", "input_scale"]:
                layer.register_parameter(
                    scale_name,
                    PerTensorScaleParameter(
                        data=torch.full(
                            (len(output_partition_sizes),),
                            torch.finfo(torch.float32).min,
                            dtype=torch.float32,
                        ),
                        weight_loader=weight_loader,
                    ),
                )

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        """Requantizes weights after loading using the maximum scale."""
        max_w_scale, quantized_weight = requantize_with_max_scale(
            layer.weight, layer.weight_scale, layer.logical_widths
        )
        layer.weight = Parameter(quantized_weight.t(), requires_grad=False)
        layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
        layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Applies FP8 linear transformation."""
        return apply_fp8_linear(
            input=x,
            weight=layer.weight,
            weight_scale=layer.weight_scale,
            input_scale=layer.input_scale,
            bias=bias,
            cutlass_fp8_supported=self.cutlass_fp8_supported,
        )


class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
    """
    Handles loading FP8 kv-cache scaling factors from modelopt quantized checkpoints.
    """

    def __init__(self, quant_config: ModelOptFp8Config):
        super().__init__(quant_config)
