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

from typing import Any, Optional

import torch

from vllm import _custom_ops as ops
from vllm.logger import init_logger
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,
    QuantizeMethodBase,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils import (
    GPTQ_MARLIN_MAX_PARALLEL,
    GPTQ_MARLIN_MIN_THREAD_N,
    marlin_make_empty_g_idx,
    marlin_permute_bias,
    marlin_permute_scales,
)
from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
    MarlinWorkspace,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import gptq_pack
from vllm.model_executor.parameter import (
    BasevLLMParameter,
    GroupQuantScaleParameter,
    PackedvLLMParameter,
)
from vllm.scalar_type import scalar_types

logger = init_logger(__name__)


class HQQMarlinConfig(QuantizationConfig):
    """Config class for HQQ Marlin"""

    def __init__(
        self,
        weight_bits: int,
        group_size: int,
        skip_modules: list[str] | None = None,
    ) -> None:
        super().__init__()
        assert group_size == 64, "The only supported HQQ group size is currently 64."
        assert weight_bits == 4, (
            "The only supported HQQ quantization bitsize is currently 4."
        )

        self.weight_bits = weight_bits
        self.group_size = group_size
        self.pack_factor = 32 // weight_bits  # packed into int32 in GPTQ format
        self.quant_type = scalar_types.uint4
        self.skip_modules = skip_modules

    def __repr__(self) -> str:
        return (
            f"HQQMarlinConfig(quant_type={self.quant_type}, "
            f"group_size={self.group_size})"
        )

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

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

    @classmethod
    def get_min_capability(cls) -> int:
        return 80

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

    @classmethod
    def from_config(cls, config: dict[str, Any]) -> "HQQMarlinConfig":
        wq_params = config["quant_config"]["weight_quant_params"]
        weight_bits = cls.get_from_keys(wq_params, ["nbits"])
        group_size = cls.get_from_keys(wq_params, ["group_size"])
        skip_modules = config["skip_modules"]
        return cls(weight_bits, group_size, skip_modules)

    def is_layer_skipped(self, prefix: str) -> bool:
        # Split the prefix into its dot-separated components
        components = prefix.split(".")

        # Check if any of the skip modules exactly matches any component
        return self.skip_modules is not None and any(
            module_name in components for module_name in self.skip_modules
        )

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


# Empty HQQ parameter, will be ignored during loading
class HQQEmptyParameter(BasevLLMParameter):
    def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):
        pass

    def load_row_parallel_weight(self, loaded_weight: torch.Tensor):
        pass

    def load_qkv_weight(self, loaded_weight: torch.Tensor, **kwargs):
        pass


def error_loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None:
    raise ValueError("No loader provided for HQQ parameter!")


# HQQ packing creates issues with sharding - therefore, prior to loading, we
# repack to GPTQ. We also reshape the weights to their proper GPTQ shape.
class HQQweightParameter(PackedvLLMParameter):
    # unpack function from https://github.com/mobiusml/hqq
    def unpack_4bit_u8(self, W_q: torch.Tensor) -> torch.Tensor:  # uint8/2 > uint8
        assert self.weight_bits == 4, "Unsupported quant bitsize (must be 4)"

        dtype = torch.uint8
        step = W_q.shape[0]
        tmp = torch.empty([2 * step, W_q.shape[1]], dtype=dtype, device=W_q.device)
        tmp[:step] = (W_q & 0b11110000) >> 4
        tmp[step:] = W_q & 0b00001111
        return tmp

    def __init__(self, packed_factor: int, packed_dim: int, weight_bits: int, **kwargs):
        super().__init__(packed_factor, packed_dim, None, **kwargs)
        self.weight_bits = weight_bits
        self.input_shape = self.shape[self.input_dim] * self.packed_factor
        self.output_shape = self.shape[self.output_dim]

    def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):
        loaded_weight = self.unpack_4bit_u8(loaded_weight)
        loaded_weight = loaded_weight.reshape(-1, self.input_shape).transpose(1, 0)
        loaded_weight = gptq_pack(
            loaded_weight,
            self.weight_bits,
            loaded_weight.shape[0],
            loaded_weight.shape[1],
        )
        super().load_merged_column_weight(loaded_weight, **kwargs)

    def load_row_parallel_weight(self, loaded_weight: torch.Tensor):
        loaded_weight = self.unpack_4bit_u8(loaded_weight)
        loaded_weight = loaded_weight.reshape(self.output_shape, -1).transpose(1, 0)
        loaded_weight = gptq_pack(
            loaded_weight,
            self.weight_bits,
            loaded_weight.shape[0],
            loaded_weight.shape[1],
        )
        super().load_row_parallel_weight(loaded_weight)

    def load_qkv_weight(self, loaded_weight: torch.Tensor, **kwargs):
        loaded_weight = self.unpack_4bit_u8(loaded_weight)
        loaded_weight = loaded_weight.reshape(-1, self.input_shape).transpose(1, 0)
        loaded_weight = gptq_pack(
            loaded_weight,
            self.weight_bits,
            loaded_weight.shape[0],
            loaded_weight.shape[1],
        )
        super().load_qkv_weight(loaded_weight, **kwargs)


# Zero points and scales in HQQ must also be reshaped to correspond to W_q's
# GPTQ shape (transposed - we transpose them too when processing weights).
class HQQZeroScaleParameter(GroupQuantScaleParameter):
    def load_merged_column_weight(self, loaded_weight: torch.Tensor, **kwargs):
        loaded_weight = loaded_weight.reshape(-1, self.shape[1])
        super().load_merged_column_weight(loaded_weight, **kwargs)

    def load_row_parallel_weight(self, loaded_weight: torch.Tensor):
        loaded_weight = loaded_weight.reshape(self.shape[0], -1)
        super().load_row_parallel_weight(loaded_weight)

    def load_qkv_weight(self, loaded_weight: torch.Tensor, **kwargs):
        loaded_weight = loaded_weight.reshape(-1, self.shape[1])
        super().load_qkv_weight(loaded_weight, **kwargs)


class HQQMarlinMethod(LinearMethodBase):
    """Linear method for HQQ Marlin."""

    def __init__(
        self,
        quant_config: HQQMarlinConfig,
    ):
        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,
    ) -> None:
        self.output_size_per_partition = sum(output_partition_sizes)
        self.input_size_per_partition = input_size_per_partition

        weight_loader = extra_weight_attrs.get("weight_loader", error_loader)

        self.scales_and_zp_size = (
            input_size_per_partition // self.quant_config.group_size
        )

        qweight = HQQweightParameter(
            data=torch.empty(
                self.input_size_per_partition // self.quant_config.pack_factor,
                self.output_size_per_partition,
                dtype=torch.int32,
            ),
            input_dim=0,
            output_dim=1,
            packed_dim=0,
            packed_factor=self.quant_config.pack_factor,
            weight_bits=self.quant_config.weight_bits,
            weight_loader=weight_loader,
        )

        zeros = HQQZeroScaleParameter(
            data=torch.empty(
                self.output_size_per_partition,
                self.scales_and_zp_size,
                dtype=params_dtype,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )

        scales = HQQZeroScaleParameter(
            data=torch.empty(
                self.output_size_per_partition,
                self.scales_and_zp_size,
                dtype=params_dtype,
            ),
            input_dim=1,
            output_dim=0,
            weight_loader=weight_loader,
        )

        layer.register_parameter("W_q", qweight)
        layer.register_parameter("zero", zeros)
        layer.register_parameter("scale", scales)

        # Ignore extra parameters in the HQQ model.
        # To be added as needed.
        ignore_parameters = (
            "axis",
            "channel_wise",
            "compute_dtype",
            "encoded_state_dict",
            "group_size",
            "nbits",
            "offload_meta",
            "optimize",
            "packing",
            "quant_scale",
            "quant_zero",
            "round_zero",
            "shape",
            "stores_quant_config",
            "unpack_view_dtype",
            "view_as_float",
        )
        for name in ignore_parameters:
            layer.register_parameter(
                name,
                HQQEmptyParameter(data=torch.empty(0), weight_loader=weight_loader),
            )

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        dev = layer.W_q.device

        # Repack to Marlin
        sort_indices = torch.empty(0, dtype=torch.int, device=dev)
        marlin_w_q = ops.gptq_marlin_repack(
            layer.W_q,
            sort_indices,
            self.input_size_per_partition,
            self.output_size_per_partition,
            self.quant_config.weight_bits,
        ).to(dev)
        marlin_s = marlin_permute_scales(
            layer.scale.transpose(1, 0),
            self.input_size_per_partition,
            self.output_size_per_partition,
            self.quant_config.group_size,
        ).to(dev)
        marlin_zp = marlin_permute_scales(
            layer.zero.transpose(1, 0),
            self.input_size_per_partition,
            self.output_size_per_partition,
            self.quant_config.group_size,
        ).to(dev)

        layer.g_idx = marlin_make_empty_g_idx(dev)
        layer.g_idx_sort_indices = marlin_make_empty_g_idx(dev)

        layer.marlin_qweight = marlin_w_q
        layer.marlin_zeros = marlin_zp
        layer.marlin_scales = marlin_s

        if hasattr(layer, "bias") and layer.bias is not None:
            layer.bias.data = marlin_permute_bias(layer.bias)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: torch.Tensor | None = None,
    ) -> torch.Tensor:
        workspace = MarlinWorkspace(
            self.output_size_per_partition,
            GPTQ_MARLIN_MIN_THREAD_N,
            GPTQ_MARLIN_MAX_PARALLEL,
        )

        scales = layer.marlin_scales
        zeros = layer.marlin_zeros
        orig_type = x.dtype

        if orig_type != torch.float16:
            x = x.to(torch.float16)
            scales = scales.to(torch.float16)
            zeros = zeros.to(torch.float16)

        marlin_out = ops.gptq_marlin_gemm(
            x,
            None,
            layer.marlin_qweight,
            bias,
            scales,
            None,
            None,
            zeros,
            layer.g_idx,
            layer.g_idx_sort_indices,
            workspace.scratch,
            scalar_types.uint4,
            x.shape[0],
            self.output_size_per_partition,
            self.input_size_per_partition,
            True,  # is_k_full
            False,  # use atomic add
            True,  # use 32-bit reduce
            True,  # use float zp
        )

        if orig_type != torch.float16:
            marlin_out = marlin_out.to(orig_type)

        return marlin_out
