# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# Supports FP-Quant compression, see https://arxiv.org/abs/2509.23202

from typing import Any

import torch
from torch.nn.parameter import Parameter

from vllm._custom_ops import (
    cutlass_scaled_fp4_mm,
    fusedQuantizeMx,
    fusedQuantizeNv,
    matmul_mxf4_bf16_tn,
)
from vllm.model_executor.layers.linear import (
    LinearBase,
    LinearMethodBase,
    UnquantizedLinearMethod,
)
from vllm.model_executor.layers.quantization import QuantizationMethods
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
from vllm.model_executor.layers.quantization.qutlass_utils import to_blocked
from vllm.model_executor.utils import set_weight_attrs
from vllm.platforms import current_platform
from vllm.utils import direct_register_custom_op


class FPQuantConfig(QuantizationConfig):
    """Config class for FPQuant."""

    def __init__(
        self,
        hadamard_group_size: int = 32,
        forward_dtype: str = "mxfp4",
        forward_method: str = "abs_max",
        pseudoquantization: bool = False,
        modules_to_not_convert: list[str] | None = None,
    ) -> None:
        super().__init__()
        self.hadamard_group_size = hadamard_group_size
        self.forward_dtype = forward_dtype
        self.forward_method = forward_method
        self.pseudoquantization = pseudoquantization
        self.modules_to_not_convert = modules_to_not_convert

        if pseudoquantization:
            raise ValueError("Pseudoquantization is not supported for vLLM")

    def __repr__(self) -> str:
        return (
            f"FPQuantConfig(hadamard_group_size={self.hadamard_group_size}, "
            f"forward_dtype={self.forward_dtype}, "
            f"forward_method={self.forward_method}, "
            f"pseudoquantization={self.pseudoquantization}, "
            f"modules_to_not_convert={self.modules_to_not_convert})"
        )

    @classmethod
    def get_name(cls) -> QuantizationMethods:
        return "fp_quant"

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

    @classmethod
    def get_min_capability(cls) -> int:
        return 100

    @classmethod
    def get_config_filenames(cls) -> list[str]:
        return []  # no extra configs.

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "FPQuantConfig":
        hadamard_group_size = cls.get_from_keys(config, ["hadamard_group_size"])
        forward_dtype = cls.get_from_keys(config, ["forward_dtype"])
        forward_method = cls.get_from_keys(config, ["forward_method"])
        pseudoquantization = cls.get_from_keys(config, ["pseudoquantization"])
        modules_to_not_convert = cls.get_from_keys(config, ["modules_to_not_convert"])
        return cls(
            hadamard_group_size,
            forward_dtype,
            forward_method,
            pseudoquantization,
            modules_to_not_convert,
        )

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> LinearMethodBase | None:
        if self.modules_to_not_convert is not None and any(
            prefix.endswith(module) for module in self.modules_to_not_convert
        ):
            return UnquantizedLinearMethod()

        if isinstance(layer, LinearBase):
            return FPQuantLinearMethod(self)
        return None


class FPQuantLinearMethod(LinearMethodBase):
    """Linear method for FPQuant.

    Args:
        quant_config: The FPQuant quantization config.
    """

    def __init__(self, quant_config: FPQuantConfig):
        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 output_size  # Unused.
        del input_size  # Unused.

        if params_dtype != torch.bfloat16:
            raise ValueError("Only bfloat16 is currently supported by FPQuant")
        if input_size_per_partition % self.quant_config.hadamard_group_size != 0:  # noqa: E501
            raise ValueError(
                "The input size is not aligned with the quantized "
                "weight shape. This can be caused by too large "
                "tensor parallel size. Or other skill issues."
            )

        assert self.quant_config.forward_dtype in ["mxfp4", "nvfp4"], (
            "Only mxfp4 and nvfp4 are supported for now"
        )
        if self.quant_config.forward_dtype == "mxfp4":
            group_size = 32
        elif self.quant_config.forward_dtype == "nvfp4":
            group_size = 16
        else:
            raise ValueError(
                f"Unsupported forward_dtype: {self.quant_config.forward_dtype}"
            )

        qweight = Parameter(
            torch.empty(
                sum(output_partition_sizes),
                input_size_per_partition // 2,
                dtype=torch.uint8,
            ),
            requires_grad=False,
        )
        set_weight_attrs(
            qweight,
            {
                "input_dim": 1,
                "output_dim": 0,
                "packed_dim": 1,
                "pack_factor": 2,
            }
            | extra_weight_attrs,
        )
        layer.register_parameter("qweight", qweight)

        scales = Parameter(
            torch.empty(
                sum(output_partition_sizes),
                input_size_per_partition // group_size,
                dtype=torch.uint8,
            ),
            requires_grad=False,
        )
        set_weight_attrs(
            scales,
            {
                "input_dim": 1,
                "output_dim": 0,
                "packed_dim": 1,
                "pack_factor": group_size,
            }
            | extra_weight_attrs,
        )
        layer.register_parameter("scales", scales)

        weight_global_scale = Parameter(
            torch.empty(1, dtype=torch.float32),
            requires_grad=False,
        )
        set_weight_attrs(
            weight_global_scale, {"ignore_warning": True} | extra_weight_attrs
        )
        layer.register_parameter("weight_global_scale", weight_global_scale)

        act_global_scale = Parameter(
            torch.empty(1, dtype=torch.float32),
            requires_grad=False,
        )
        set_weight_attrs(
            act_global_scale, {"ignore_warning": True} | extra_weight_attrs
        )
        layer.register_parameter("act_global_scale", act_global_scale)

        forward_hadamard_matrix = Parameter(
            torch.empty(
                self.quant_config.hadamard_group_size,
                self.quant_config.hadamard_group_size,
                dtype=params_dtype,
            ),
            requires_grad=False,
        )
        set_weight_attrs(
            forward_hadamard_matrix, {"ignore_warning": True} | extra_weight_attrs
        )
        layer.register_parameter("forward_hadamard_matrix", forward_hadamard_matrix)

        backward_hadamard_matrix = Parameter(
            torch.empty(
                self.quant_config.hadamard_group_size,
                self.quant_config.hadamard_group_size,
                dtype=params_dtype,
            ),
            requires_grad=False,
        )
        set_weight_attrs(
            backward_hadamard_matrix, {"ignore_warning": True} | extra_weight_attrs
        )
        layer.register_parameter("backward_hadamard_matrix", backward_hadamard_matrix)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        return quantized_forward(
            x,
            layer.qweight,
            layer.scales,
            layer.weight_global_scale,
            layer.act_global_scale,
            bias,
            layer.forward_hadamard_matrix,
            self.quant_config.forward_method,
            self.quant_config.forward_dtype,
        )


def ceil_div(a, b):
    return (a + b - 1) // b


def fused_quantize_mx(
    x_flat: torch.Tensor, hadamard_matrix: torch.Tensor, forward_method: str
) -> tuple[torch.Tensor, torch.Tensor]:
    return fusedQuantizeMx(x_flat, hadamard_matrix, method=forward_method)


def fused_quantize_mx_fake(x_flat, hadamard_matrix, forward_method):
    rows, cols = x_flat.size(0), x_flat.size(1) // 32
    padded_rows = ((rows + 128 - 1) // 128) * 128
    padded_cols = ((cols + 4 - 1) // 4) * 4

    xh_e2m1 = torch.empty(
        x_flat.size(0), x_flat.size(1) // 2, dtype=torch.uint8, device=x_flat.device
    )
    xh_e8m0 = torch.empty(
        padded_rows, padded_cols, dtype=torch.float8_e8m0fnu, device=x_flat.device
    )

    return xh_e2m1, xh_e8m0


direct_register_custom_op(
    op_name="fused_quantize_mx",
    op_func=fused_quantize_mx,
    mutates_args=[],
    fake_impl=fused_quantize_mx_fake,
    dispatch_key=current_platform.dispatch_key,
)


def matmul_mxf4_bf16(
    x: torch.Tensor,
    w: torch.Tensor,
    xs: torch.Tensor,
    ws: torch.Tensor,
    alpha: torch.Tensor,
) -> torch.Tensor:
    return matmul_mxf4_bf16_tn(
        x,
        w,
        to_blocked(xs, backend="triton").view(torch.float8_e8m0fnu),
        to_blocked(ws, backend="triton").view(torch.float8_e8m0fnu),
        alpha,
    )


def matmul_mxf4_bf16_fake(x, w, xs, ws, alpha):
    return torch.empty(*x.shape[:-1], w.shape[0], dtype=torch.bfloat16, device=x.device)


direct_register_custom_op(
    op_name="matmul_mxf4_bf16",
    op_func=matmul_mxf4_bf16,
    mutates_args=[],
    fake_impl=matmul_mxf4_bf16_fake,
    dispatch_key=current_platform.dispatch_key,
)


def fused_quantize_nv(
    x_flat: torch.Tensor, hadamard_matrix: torch.Tensor, global_scale: torch.Tensor
) -> tuple[torch.Tensor, torch.Tensor]:
    return fusedQuantizeNv(x_flat, hadamard_matrix, global_scale)


def fused_quantize_nv_fake(x_flat, hadamard_matrix, global_scale):
    rows, cols = x_flat.size(0), x_flat.size(1) // 16
    padded_rows = ((rows + 128 - 1) // 128) * 128
    padded_cols = ((cols + 4 - 1) // 4) * 4

    xh_e2m1 = torch.empty(
        x_flat.size(0), x_flat.size(1) // 2, dtype=torch.uint8, device=x_flat.device
    )
    xh_e8m0 = torch.empty(
        padded_rows, padded_cols, dtype=torch.float8_e4m3fn, device=x_flat.device
    )

    return xh_e2m1, xh_e8m0


direct_register_custom_op(
    op_name="fused_quantize_nv",
    op_func=fused_quantize_nv,
    mutates_args=[],
    fake_impl=fused_quantize_nv_fake,
    dispatch_key=current_platform.dispatch_key,
)


def matmul_nvf4_bf16(
    x: torch.Tensor,
    w: torch.Tensor,
    xs: torch.Tensor,
    ws: torch.Tensor,
    alpha: torch.Tensor,
) -> torch.Tensor:
    return cutlass_scaled_fp4_mm(
        x,
        w,
        to_blocked(xs, backend="triton")
        .view(torch.float8_e4m3fn)
        .view(-1, x.shape[1] // 8),  # *2//16
        to_blocked(ws, backend="triton")
        .view(torch.float8_e4m3fn)
        .view(-1, x.shape[1] // 8),
        alpha,
        torch.bfloat16,
    )


def matmul_nvf4_bf16_fake(x, w, xs, ws, alpha):
    return torch.empty(*x.shape[:-1], w.shape[0], dtype=torch.bfloat16, device=x.device)


direct_register_custom_op(
    op_name="matmul_nvf4_bf16",
    op_func=matmul_nvf4_bf16,
    mutates_args=[],
    fake_impl=matmul_nvf4_bf16_fake,
    dispatch_key=current_platform.dispatch_key,
)


def quantized_forward(
    x: torch.Tensor,
    qweight: torch.Tensor,
    weight_scales: torch.Tensor,
    weight_global_scale: torch.Tensor,
    act_global_scale: torch.Tensor,
    bias: torch.Tensor | None,
    forward_hadamard_matrix: torch.Tensor,
    forward_method: str,
    forward_dtype: str,
) -> torch.Tensor:
    x_flat = x.contiguous().flatten(end_dim=-2)

    if forward_dtype == "mxfp4":
        x_flat_q, x_flat_scales = torch.ops.vllm.fused_quantize_mx(
            x_flat, forward_hadamard_matrix, forward_method
        )
        y = torch.ops.vllm.matmul_mxf4_bf16(
            x_flat_q,
            qweight,
            x_flat_scales,
            weight_scales,
            1 / (weight_global_scale * act_global_scale),
        )
    elif forward_dtype == "nvfp4":
        x_flat_q, x_flat_scales = torch.ops.vllm.fused_quantize_nv(
            x_flat, forward_hadamard_matrix, act_global_scale
        )
        y = torch.ops.vllm.matmul_nvf4_bf16(
            x_flat_q,
            qweight,
            x_flat_scales,
            weight_scales,
            1 / (weight_global_scale * act_global_scale),
        )
    else:
        raise ValueError(f"Unsupported forward_dtype: {forward_dtype}")

    y = y.view(*x.shape[:-1], y.shape[-1])
    if bias is not None:
        y += bias

    return y
