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//! Normalization operations trait.
use crate::error::{Error, Result};
use crate::runtime::Runtime;
use crate::tensor::Tensor;
/// Normalization operations
pub trait NormalizationOps<R: Runtime> {
/// RMS Normalization: output = input * rsqrt(mean(input^2) + eps) * weight
///
/// RMSNorm is used in LLaMA and other modern transformer architectures.
/// It normalizes over the last dimension.
///
/// # Arguments
///
/// * `input` - Input tensor of shape `[..., hidden_size]`
/// * `weight` - Weight tensor of shape `[hidden_size]`
/// * `eps` - Small constant for numerical stability (typically 1e-5 or 1e-6)
fn rms_norm(&self, input: &Tensor<R>, weight: &Tensor<R>, eps: f32) -> Result<Tensor<R>> {
let _ = (input, weight, eps);
Err(Error::NotImplemented {
feature: "NormalizationOps::rms_norm",
})
}
/// Layer Normalization: output = (input - mean) / sqrt(variance + eps) * weight + bias
///
/// LayerNorm normalizes across the last dimension for each batch element.
///
/// # Arguments
///
/// * `input` - Input tensor of shape `[..., hidden_size]`
/// * `weight` - Weight (gamma) tensor of shape `[hidden_size]`
/// * `bias` - Bias (beta) tensor of shape `[hidden_size]`
/// * `eps` - Small constant for numerical stability (typically 1e-5)
fn layer_norm(
&self,
input: &Tensor<R>,
weight: &Tensor<R>,
bias: &Tensor<R>,
eps: f32,
) -> Result<Tensor<R>> {
let _ = (input, weight, bias, eps);
Err(Error::NotImplemented {
feature: "NormalizationOps::layer_norm",
})
}
/// Group Normalization: normalize over groups of channels.
///
/// Divides channels into `num_groups` groups and normalizes each group
/// independently. Used in some vision architectures and diffusion models.
///
/// # Arguments
///
/// * `input` - Input tensor of shape `[batch, channels, ...]`
/// * `weight` - Scale (gamma) of shape `[channels]`
/// * `bias` - Bias (beta) of shape `[channels]`
/// * `num_groups` - Number of groups (must divide channels evenly)
/// * `eps` - Small constant for numerical stability
fn group_norm(
&self,
input: &Tensor<R>,
weight: &Tensor<R>,
bias: &Tensor<R>,
num_groups: usize,
eps: f32,
) -> Result<Tensor<R>> {
let _ = (input, weight, bias, num_groups, eps);
Err(Error::NotImplemented {
feature: "NormalizationOps::group_norm",
})
}
/// Fused Add + RMS Normalization: pre_norm = x + residual, output = rms_norm(pre_norm, weight, eps)
///
/// Saves one full memory pass vs separate add + rms_norm. Used in every
/// transformer residual connection. Returns `(output, pre_norm)` where
/// `pre_norm` is needed for backward pass and residual chaining.
///
/// # Arguments
///
/// * `x` - Input tensor of shape `[..., hidden_size]`
/// * `residual` - Residual tensor of same shape as `x`
/// * `weight` - Weight tensor of shape `[hidden_size]`
/// * `eps` - Small constant for numerical stability
fn fused_add_rms_norm(
&self,
x: &Tensor<R>,
residual: &Tensor<R>,
weight: &Tensor<R>,
eps: f32,
) -> Result<(Tensor<R>, Tensor<R>)> {
let _ = (x, residual, weight, eps);
Err(Error::NotImplemented {
feature: "NormalizationOps::fused_add_rms_norm",
})
}
/// Backward pass for fused add + RMS normalization.
///
/// Returns `(d_input_residual, d_weight)` where `d_input_residual` is the
/// gradient for both `x` and `residual` (they share the same gradient since
/// `d(x + residual)/dx = d(x + residual)/d(residual) = 1`).
///
/// # Arguments
///
/// * `grad` - Upstream gradient of shape `[..., hidden_size]`
/// * `pre_norm` - The `x + residual` value from forward pass
/// * `weight` - Weight tensor of shape `[hidden_size]`
/// * `eps` - Same eps used in forward pass
fn fused_add_rms_norm_bwd(
&self,
grad: &Tensor<R>,
pre_norm: &Tensor<R>,
weight: &Tensor<R>,
eps: f32,
) -> Result<(Tensor<R>, Tensor<R>)> {
let _ = (grad, pre_norm, weight, eps);
Err(Error::NotImplemented {
feature: "NormalizationOps::fused_add_rms_norm_bwd",
})
}
/// Fused Add + Layer Normalization: pre_norm = x + residual, output = layer_norm(pre_norm, weight, bias, eps)
///
/// Saves one full memory pass vs separate add + layer_norm.
/// Returns `(output, pre_norm)`.
///
/// # Arguments
///
/// * `x` - Input tensor of shape `[..., hidden_size]`
/// * `residual` - Residual tensor of same shape as `x`
/// * `weight` - Weight (gamma) tensor of shape `[hidden_size]`
/// * `bias` - Bias (beta) tensor of shape `[hidden_size]`
/// * `eps` - Small constant for numerical stability
fn fused_add_layer_norm(
&self,
x: &Tensor<R>,
residual: &Tensor<R>,
weight: &Tensor<R>,
bias: &Tensor<R>,
eps: f32,
) -> Result<(Tensor<R>, Tensor<R>)> {
let _ = (x, residual, weight, bias, eps);
Err(Error::NotImplemented {
feature: "NormalizationOps::fused_add_layer_norm",
})
}
/// Backward pass for fused add + layer normalization.
///
/// Returns `(d_input_residual, d_weight, d_bias)`.
///
/// # Arguments
///
/// * `grad` - Upstream gradient of shape `[..., hidden_size]`
/// * `pre_norm` - The `x + residual` value from forward pass
/// * `weight` - Weight (gamma) tensor of shape `[hidden_size]`
/// * `bias` - Bias (beta) tensor of shape `[hidden_size]`
/// * `eps` - Same eps used in forward pass
fn fused_add_layer_norm_bwd(
&self,
grad: &Tensor<R>,
pre_norm: &Tensor<R>,
weight: &Tensor<R>,
bias: &Tensor<R>,
eps: f32,
) -> Result<(Tensor<R>, Tensor<R>, Tensor<R>)> {
let _ = (grad, pre_norm, weight, bias, eps);
Err(Error::NotImplemented {
feature: "NormalizationOps::fused_add_layer_norm_bwd",
})
}
}