import torch
import torch.distributed as dist
import torch.nn.functional as F
from vllm.kvprune.utils.tp_collectives import tensor_parallel_all_reduce
from vllm.kvprune.utils.tp_utils import (
    tensor_parallel_rank_for_sharding,
    tensor_parallel_world_size_for_sharding,
)
from torch import nn


def divide(numerator, denominator):
    assert numerator % denominator == 0
    return numerator // denominator


class LinearBase(nn.Module):
    def __init__(
        self,
        input_size: int,
        output_size: int,
        bias: bool = False,
        tp_dim: int | None = None,
    ):
        super().__init__()
        self.tp_dim = tp_dim
        self.tp_rank = tensor_parallel_rank_for_sharding()
        self.tp_size = tensor_parallel_world_size_for_sharding()
        self.weight = nn.Parameter(torch.empty(output_size, input_size))
        self.weight.weight_loader = self.weight_loader
        if bias:
            self.bias = nn.Parameter(torch.empty(output_size))
            self.bias.weight_loader = self.weight_loader
        else:
            self.register_parameter("bias", None)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        raise NotImplementedError


class ReplicatedLinear(LinearBase):
    def __init__(
        self,
        input_size: int,
        output_size: int,
        bias: bool = False,
    ):
        super().__init__(input_size, output_size, bias)

    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
        param.data.copy_(loaded_weight)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return F.linear(x, self.weight, self.bias)


class ColumnParallelLinear(LinearBase):
    def __init__(
        self,
        input_size: int,
        output_size: int,
        bias: bool = False,
    ):
        tp_size = tensor_parallel_world_size_for_sharding()
        super().__init__(input_size, divide(output_size, tp_size), bias, 0)

    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
        param_data = param.data
        shard_size = param_data.size(self.tp_dim)
        start_idx = self.tp_rank * shard_size
        loaded_weight = loaded_weight.narrow(self.tp_dim, start_idx, shard_size)
        param_data.copy_(loaded_weight)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return F.linear(x, self.weight, self.bias)


class MergedColumnParallelLinear(ColumnParallelLinear):
    def __init__(
        self,
        input_size: int,
        output_sizes: list[int],
        bias: bool = False,
    ):
        self.output_sizes = output_sizes
        super().__init__(input_size, sum(output_sizes), bias)

    def weight_loader(
        self, param: nn.Parameter, loaded_weight: torch.Tensor, loaded_shard_id: int
    ):
        param_data = param.data
        shard_offset = sum(self.output_sizes[:loaded_shard_id]) // self.tp_size
        shard_size = self.output_sizes[loaded_shard_id] // self.tp_size
        param_data = param_data.narrow(self.tp_dim, shard_offset, shard_size)
        loaded_weight = loaded_weight.chunk(self.tp_size, self.tp_dim)[self.tp_rank]
        param_data.copy_(loaded_weight)


class QKVParallelLinear(ColumnParallelLinear):
    def __init__(
        self,
        hidden_size: int,
        head_size: int,
        total_num_heads: int,
        total_num_kv_heads: int | None = None,
        bias: bool = False,
    ):
        tp_size = tensor_parallel_world_size_for_sharding()
        total_num_kv_heads = total_num_kv_heads or total_num_heads
        self.head_size = head_size
        self.num_heads = divide(total_num_heads, tp_size)
        self.num_kv_heads = divide(total_num_kv_heads, tp_size)
        output_size = (total_num_heads + 2 * total_num_kv_heads) * self.head_size
        super().__init__(hidden_size, output_size, bias)

    def weight_loader(
        self, param: nn.Parameter, loaded_weight: torch.Tensor, loaded_shard_id: str
    ):
        param_data = param.data
        assert loaded_shard_id in ["q", "k", "v"]
        if loaded_shard_id == "q":
            shard_size = self.num_heads * self.head_size
            shard_offset = 0
        elif loaded_shard_id == "k":
            shard_size = self.num_kv_heads * self.head_size
            shard_offset = self.num_heads * self.head_size
        else:
            shard_size = self.num_kv_heads * self.head_size
            shard_offset = (
                self.num_heads * self.head_size + self.num_kv_heads * self.head_size
            )
        param_data = param_data.narrow(self.tp_dim, shard_offset, shard_size)
        loaded_weight = loaded_weight.chunk(self.tp_size, self.tp_dim)[self.tp_rank]
        param_data.copy_(loaded_weight)


class RowParallelLinear(LinearBase):
    def __init__(
        self,
        input_size: int,
        output_size: int,
        bias: bool = False,
    ):
        tp_size = tensor_parallel_world_size_for_sharding()
        super().__init__(divide(input_size, tp_size), output_size, bias, 1)

    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
        param_data = param.data
        shard_size = param_data.size(self.tp_dim)
        start_idx = self.tp_rank * shard_size
        loaded_weight = loaded_weight.narrow(self.tp_dim, start_idx, shard_size)
        param_data.copy_(loaded_weight)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        y = F.linear(x, self.weight, self.bias if self.tp_rank == 0 else None)
        if self.tp_size > 1:
            tensor_parallel_all_reduce(y)
        return y