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# Copyright (c) 2026 audio-embeddings contributors
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from __future__ import annotations

import torch
import torch.nn as nn
import torch.nn.functional as F


class MixedPrecisionRMSNorm(nn.RMSNorm):
    """RMSNorm that keeps its master weight while matching activation dtype.

    PyTorch's fused RMSNorm requires the input and weight to have the same dtype.
    Mixed-precision training keeps parameters in FP32, so use a differentiable
    low-precision view of the weight for the operation itself.
    """

    def forward(self, input: torch.Tensor) -> torch.Tensor:
        weight = self.weight
        if weight is not None and weight.dtype != input.dtype:
            weight = weight.to(dtype=input.dtype)
        return F.rms_norm(input, self.normalized_shape, weight, self.eps)