from typing import Any, Dict, List, Optional

import torch
from loguru import logger
from torch.nn.parameter import Parameter

from aphrodite import _custom_ops as ops
from aphrodite.modeling.layers.linear import LinearBase, LinearMethodBase
from aphrodite.modeling.layers.vocab_parallel_embedding import ParallelLMHead
from aphrodite.modeling.parameter import (BaseAphroditeParameter,
                                          ChannelQuantScaleParameter,
                                          GroupQuantScaleParameter,
                                          PackedAphroditeParameter)
from aphrodite.quantization.base_config import QuantizationConfig


class MarlinConfig(QuantizationConfig):
    """Config class for Marlin.

    Reference: https://github.com/IST-DASLab/marlin/tree/master
    """

    def __init__(
        self,
        group_size: int,
        lm_head_quantized: bool,
    ) -> None:
        # Group size for the quantization.
        self.group_size = group_size
        self.lm_head_quantized = lm_head_quantized
        if self.group_size != 128 and self.group_size != -1:
            raise ValueError(
                "Currently, only group size 128 and -1 (channelwise) "
                "is supported for Marlin, but got group_size of "
                f"{self.group_size}"
            )

        # 4 Bits packed into 32 bit datatype.
        self.pack_factor = 32 // 4

        # Tile size used by marlin kernels.
        self.tile_size = 16

        # Min out_features dim
        self.min_n_threads = 64

        # Min in_features dim
        self.min_k_threads = 128

        # Max parallel problems to solve at once (improves large
        # batch performance)
        self.max_parallel = 16

        # Permutation length used by the marlin kernels.
        self.perm_len = 1024

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

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

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

    @classmethod
    # Need to figure it out
    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]) -> "MarlinConfig":
        group_size = cls.get_from_keys(config, ["group_size"])
        lm_head_quantized = cls.get_from_keys_or(
            config, ["lm_head"], default=False
        )
        return cls(group_size, lm_head_quantized)

    @classmethod
    def override_quantization_method(
        cls, hf_quant_cfg, user_quant
    ) -> Optional[str]:
        # compat: autogptq >=0.8.0 use checkpoint_format: str
        # compat: autogptq <=0.7.1 is_marlin_format: bool
        is_marlin_format = hf_quant_cfg.get(
            "checkpoint_format"
        ) == "marlin" or hf_quant_cfg.get("is_marlin_format", False)

        is_valid_user_quant = (
            user_quant is None or user_quant == "gptq" or user_quant == "marlin"
        )

        if is_marlin_format and is_valid_user_quant:
            msg = (
                "The model is serialized in {} format. Using {} kernel.".format(
                    cls.get_name(), cls.get_name()
                )
            )
            logger.info(msg)
            return cls.get_name()

        return None

    def get_quant_method(
        self, layer: torch.nn.Module, prefix: str
    ) -> Optional["MarlinLinearMethod"]:
        if isinstance(layer, LinearBase) or (
            isinstance(layer, ParallelLMHead) and self.lm_head_quantized
        ):
            return MarlinLinearMethod(self)
        return None

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


class MarlinLinearMethod(LinearMethodBase):
    """Linear method for Marlin.

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

    def __init__(self, quant_config: MarlinConfig):
        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.
        weight_loader = extra_weight_attrs["weight_loader"]

        if params_dtype != torch.float16:
            raise ValueError(
                f"The params dtype must be float16, but got {params_dtype}"
            )

        # Validate output_size_per_partition
        output_size_per_partition = sum(output_partition_sizes)
        if output_size_per_partition % self.quant_config.min_n_threads != 0:
            raise ValueError(
                f"Weight output_size_per_partition = "
                f"{output_size_per_partition} is not divisible by "
                f"min_n_threads = {self.quant_config.min_n_threads}."
            )
        if output_size_per_partition % self.quant_config.pack_factor != 0:
            raise ValueError(
                f"Weight output_size_per_partition = "
                f"{output_size_per_partition} is not divisible by "
                f"pack_factor = {self.quant_config.pack_factor}."
            )

        # Validate input_size_per_partition
        if input_size_per_partition % self.quant_config.min_k_threads != 0:
            raise ValueError(
                f"Weight input_size_per_partition = "
                f"{input_size_per_partition} is not divisible by "
                f"min_k_threads = {self.quant_config.min_k_threads}."
            )
        if (
            self.quant_config.group_size != -1
            and input_size_per_partition % self.quant_config.group_size != 0
        ):
            raise ValueError(
                f"Weight input_size_per_partition = "
                f"{input_size_per_partition} is not divisible by "
                f"group_size = {self.quant_config.group_size}."
            )

        # Check that we have at least 4 tiles horizontally in the shard
        num_tiles_per_perm = self.quant_config.perm_len // (
            self.quant_config.tile_size**2
        )
        if output_size_per_partition % num_tiles_per_perm != 0:
            raise ValueError(
                "Each permutation group must reside on the same gpu"
            )

        # Quantized 4Bit weights packed into Int32.
        qweight = PackedAphroditeParameter(
            data=torch.empty(
                input_size_per_partition // self.quant_config.tile_size,
                output_size_per_partition
                * self.quant_config.tile_size
                // self.quant_config.pack_factor,
                device="cuda",
                dtype=torch.int32,
            ),
            input_dim=0,
            output_dim=1,
            packed_dim=1,
            packed_factor=self.quant_config.pack_factor,
            marlin_tile_size=self.quant_config.tile_size,
            weight_loader=weight_loader,
        )

        # Determine if channelwise or not
        input_groups = (
            1
            if self.quant_config.group_size == -1
            else input_size_per_partition // self.quant_config.group_size
        )

        weight_scale_args = {
            "data": torch.empty(
                input_groups,
                output_size_per_partition,
                device="cuda",
                dtype=params_dtype,
            ),
            "weight_loader": weight_loader,
        }
        if input_groups == 1:
            scales = ChannelQuantScaleParameter(
                output_dim=1, **weight_scale_args
            )
        else:
            scales = GroupQuantScaleParameter(
                output_dim=1, input_dim=0, **weight_scale_args
            )

        # Allocate workspace (Used for internal locking mechanism)
        max_workspace_size = (
            output_size_per_partition // self.quant_config.min_n_threads
        ) * self.quant_config.max_parallel
        workspace = BaseAphroditeParameter(
            data=torch.zeros(
                max_workspace_size, device="cuda", dtype=torch.int
            ),
            weight_loader=weight_loader,
        )

        layer.register_parameter("B", qweight)
        layer.register_parameter("s", scales)
        layer.register_parameter("workspace", workspace)

    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
        # required by torch.compile
        layer.B = Parameter(layer.B.data, requires_grad=False)
        layer.s = Parameter(layer.s.data, requires_grad=False)
        layer.workspace = Parameter(layer.workspace.data, requires_grad=False)

    def apply(
        self,
        layer: torch.nn.Module,
        x: torch.Tensor,
        bias: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        qweight = layer.B
        scales = layer.s
        workspace = layer.workspace

        x_2d = x.view(-1, x.shape[-1])

        size_m = x_2d.shape[0]
        size_k = x_2d.shape[1]
        size_n = scales.shape[1]

        output_2d = ops.marlin_gemm(
            x_2d, qweight, scales, workspace, size_m, size_n, size_k
        )

        output = output_2d.view(x.shape[:-1] + (output_2d.shape[1],))

        if bias is not None:
            output.add_(bias)  # In-place add

        return output