"""
Copyright 2023-2024 SGLang Team
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

"""Fused operators for normalization layers."""

from typing import Optional, Tuple, Union

import torch
import torch.nn as nn
from flashinfer.norm import (
    fused_add_rmsnorm,
    gemma_fused_add_rmsnorm,
    gemma_rmsnorm,
    rmsnorm,
)
from vllm.model_executor.custom_op import CustomOp


class RMSNorm(CustomOp):
    def __init__(
        self,
        hidden_size: int,
        eps: float = 1e-6,
    ) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward_cuda(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:

        if residual is not None:
            fused_add_rmsnorm(x, residual, self.weight.data, self.variance_epsilon)
            return x, residual
        out = rmsnorm(x, self.weight.data, self.variance_epsilon)
        return out

    def forward_native(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        orig_dtype = x.dtype
        x = x.to(torch.float32)
        if residual is not None:
            x = x + residual.to(torch.float32)
            residual = x.to(orig_dtype)

        variance = x.pow(2).mean(dim=-1, keepdim=True)
        x = x * torch.rsqrt(variance + self.variance_epsilon)
        x = x.to(orig_dtype) * self.weight
        if residual is None:
            return x
        else:
            return x, residual


class GemmaRMSNorm(CustomOp):
    def __init__(
        self,
        hidden_size: int,
        eps: float = 1e-6,
    ) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward_native(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        orig_dtype = x.dtype
        if residual is not None:
            x = x + residual
            residual = x

        x = x.float()
        variance = x.pow(2).mean(dim=-1, keepdim=True)
        x = x * torch.rsqrt(variance + self.variance_epsilon)
        x = x * (1.0 + self.weight.float())
        x = x.to(orig_dtype)
        return x if residual is None else (x, residual)

    def forward_cuda(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        if residual is not None:
            gemma_fused_add_rmsnorm(
                x, residual, self.weight.data, self.variance_epsilon
            )
            return x, residual
        out = gemma_rmsnorm(x, self.weight.data, self.variance_epsilon)
        return out
