scirs2-linalg 0.4.2

//! Specialized Attention Mechanisms
//!
//! This module provides various specialized attention mechanisms beyond
//! the standard scaled dot-product attention, including flash attention,
//! sparse attention, rotary embeddings, and more.

use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView1, ArrayView2, ArrayView3};
use scirs2_core::numeric::{Float, NumAssignOps, Zero};
use std::ops::{Add, Div, Mul, Sub};

use super::utils::{attention, AttentionMask};
use crate::error::{check_dimensions, LinalgError, LinalgResult};

/// Flash Attention - Memory-efficient attention implementation
///
/// Implements the Flash Attention algorithm which reduces memory usage by computing
/// attention in blocks, avoiding the materialization of the full attention matrix.
///
/// # Arguments
///
/// * `query` - Query tensor of shape [batchsize, seq_len_q, d_model]
/// * `key` - Key tensor of shape [batchsize, seq_len_k, d_model]
/// * `value` - Value tensor of shape [batchsize, seq_len_k, d_model]
/// * `mask` - Optional mask to apply to attention weights
/// * `scale` - Scaling factor for dot product
/// * `blocksize` - Block size for tiling (affects performance but not results)
///
/// # Returns
///
/// * Output tensor of shape [batchsize, seq_len_q, d_model]
#[allow(dead_code)]
pub fn flash_attention<F>(
    query: &ArrayView3<F>,
    key: &ArrayView3<F>,
    value: &ArrayView3<F>,
    mask: Option<&AttentionMask>,
    scale: F,
    blocksize: usize,
) -> LinalgResult<Array3<F>>
where
    F: Float + Add + Mul + Div + Sub + NumAssignOps + Zero + std::fmt::Debug,
{
    // Validate dimensions
    let (batchsize, seq_len_q, d_model) = (query.shape()[0], query.shape()[1], query.shape()[2]);
    let (batchsize_k, seq_len_k, d_model_k) = (key.shape()[0], key.shape()[1], key.shape()[2]);
    let (batchsize_v, seq_len_v, d_model_v) =
        (value.shape()[0], value.shape()[1], value.shape()[2]);

    check_dimensions(
        batchsize == batchsize_k,
        format!("Batch sizes must match: {batchsize} != {batchsize_k}"),
    )?;
    check_dimensions(
        batchsize == batchsize_v,
        format!("Batch sizes must match: {batchsize} != {batchsize_v}"),
    )?;
    check_dimensions(
        seq_len_k == seq_len_v,
        format!("Key and value sequence lengths must match: {seq_len_k} != {seq_len_v}"),
    )?;
    check_dimensions(
        d_model == d_model_k,
        format!("Query and key dimensions must match: {d_model} != {d_model_k}"),
    )?;

    // Determine block sizes
    let blocksize_q = blocksize.min(seq_len_q);
    let blocksize_k = blocksize.min(seq_len_k);

    // Initialize output
    let mut output = Array3::<F>::zeros((batchsize, seq_len_q, d_model_v));

    // Process batch by batch
    for b in 0..batchsize {
        // Process query blocks
        for q_start in (0..seq_len_q).step_by(blocksize_q) {
            let q_end = (q_start + blocksize_q).min(seq_len_q);
            let q_block = query.slice(scirs2_core::ndarray::s![b, q_start..q_end, ..]);

            // For each query block, process all key/value blocks
            let mut m_block = Array1::<F>::from_elem(q_end - q_start, F::neg_infinity());
            let mut l_block = Array1::<F>::zeros(q_end - q_start);

            for k_start in (0..seq_len_k).step_by(blocksize_k) {
                let k_end = (k_start + blocksize_k).min(seq_len_k);
                let k_block = key.slice(scirs2_core::ndarray::s![b, k_start..k_end, ..]);
                let v_block = value.slice(scirs2_core::ndarray::s![b, k_start..k_end, ..]);

                // Compute block of attention scores
                let mut scores_block = Array2::<F>::zeros((q_end - q_start, k_end - k_start));

                for i in 0..(q_end - q_start) {
                    for j in 0..(k_end - k_start) {
                        let mut dot_product = F::zero();
                        for k in 0..d_model {
                            dot_product += q_block[[i, k]] * k_block[[j, k]];
                        }
                        scores_block[[i, j]] = dot_product * scale;
                    }
                }

                // Apply mask if provided
                if let Some(mask_ref) = mask {
                    match mask_ref {
                        AttentionMask::Causal => {
                            for i in 0..(q_end - q_start) {
                                let q_idx = q_start + i;
                                for j in 0..(k_end - k_start) {
                                    let k_idx = k_start + j;
                                    if k_idx > q_idx {
                                        scores_block[[i, j]] = F::neg_infinity();
                                    }
                                }
                            }
                        }
                        // For other mask types, we would need to extract the relevant portion
                        // This is a simplified implementation for demonstration
                        _ => {
                            return Err(LinalgError::NotImplementedError(
                                "Flash attention currently only supports causal masks".to_string(),
                            ))
                        }
                    }
                }

                // Update max values and compute exponentials
                for i in 0..(q_end - q_start) {
                    let row = scores_block.slice(scirs2_core::ndarray::s![i, ..]);
                    let max_val =
                        row.fold(F::neg_infinity(), |max, &x| if x > max { x } else { max });

                    if max_val > m_block[i] {
                        // Update scaling factors
                        let m_prev = m_block[i];
                        let m_new = max_val;

                        // Update l_i <- l_i * exp(m_i - m'_i)
                        if m_prev != F::neg_infinity() {
                            l_block[i] *= (m_prev - m_new).exp();
                        }

                        // Update output with scaling
                        if l_block[i] > F::zero() {
                            let scale_factor = if l_block[i] != F::zero() {
                                (m_prev - m_new).exp() / l_block[i]
                            } else {
                                // Handle zero normalization factor
                                F::zero()
                            };
                            for j in 0..d_model_v {
                                output[[b, q_start + i, j]] *= scale_factor;
                            }
                        }

                        // Update m_i
                        m_block[i] = m_new;
                    }

                    // Compute contribution of current key-value block
                    let mut block_sum = F::zero();
                    let mut block_output = Array1::<F>::zeros(d_model_v);

                    for j in 0..(k_end - k_start) {
                        let exp_val = (scores_block[[i, j]] - m_block[i]).exp();
                        block_sum += exp_val;

                        // Update output
                        for k in 0..d_model_v {
                            block_output[k] += exp_val * v_block[[j, k]];
                        }
                    }

                    // Update l_i and output
                    l_block[i] += block_sum;
                    for j in 0..d_model_v {
                        output[[b, q_start + i, j]] += block_output[j];
                    }
                }
            }

            // Normalize output by l_block
            for i in 0..(q_end - q_start) {
                if l_block[i] > F::zero() {
                    for j in 0..d_model_v {
                        output[[b, q_start + i, j]] /= l_block[i];
                    }
                }
            }
        }
    }

    Ok(output)
}

/// Sparse Attention - Implements attention with sparse patterns
///
/// Computes attention using predetermined sparse attention patterns to reduce
/// computational complexity from O(n²) to O(n * log(n)) or even O(n).
///
/// # Arguments
///
/// * `query` - Query tensor of shape [batchsize, seq_len_q, d_model]
/// * `key` - Key tensor of shape [batchsize, seq_len_k, d_model]
/// * `value` - Value tensor of shape [batchsize, seq_len_k, d_model]
/// * `pattern_mask` - Boolean mask defining the sparse attention pattern [seq_len_q, seq_len_k]
/// * `scale` - Scaling factor for dot product
///
/// # Returns
///
/// * Output tensor of shape [batchsize, seq_len_q, d_model]
#[allow(dead_code)]
pub fn sparse_attention<F>(
    query: &ArrayView3<F>,
    key: &ArrayView3<F>,
    value: &ArrayView3<F>,
    pattern_mask: &ArrayView2<bool>,
    scale: F,
) -> LinalgResult<Array3<F>>
where
    F: Float + Add + Mul + Div + Sub + NumAssignOps + Zero + std::fmt::Debug,
{
    // Validate dimensions
    let (batchsize, seq_len_q, d_model) = (query.shape()[0], query.shape()[1], query.shape()[2]);
    let (_, seq_len_k, _) = (key.shape()[0], key.shape()[1], key.shape()[2]);
    let (_, _, d_model_v) = (value.shape()[0], value.shape()[1], value.shape()[2]);

    if pattern_mask.shape() != [seq_len_q, seq_len_k] {
        return Err(LinalgError::DimensionError(format!(
            "Pattern mask shape {:?} doesn't match query and key sequence lengths [{}, {}]",
            pattern_mask.shape(),
            seq_len_q,
            seq_len_k
        )));
    }

    // Initialize output
    let mut output = Array3::<F>::zeros((batchsize, seq_len_q, d_model_v));

    // Process batch by batch
    for b in 0..batchsize {
        let q_b = query.slice(scirs2_core::ndarray::s![b, .., ..]);
        let k_b = key.slice(scirs2_core::ndarray::s![b, .., ..]);
        let v_b = value.slice(scirs2_core::ndarray::s![b, .., ..]);

        // For each query position
        for i in 0..seq_len_q {
            let q_i = q_b.slice(scirs2_core::ndarray::s![i, ..]);

            // Calculate sparse attention scores
            let mut scores = Vec::new();
            let mut indices = Vec::new();

            // Only compute scores for positions allowed by the pattern mask
            for j in 0..seq_len_k {
                if pattern_mask[[i, j]] {
                    let k_j = k_b.slice(scirs2_core::ndarray::s![j, ..]);

                    // Compute dot product
                    let mut dot_product = F::zero();
                    for k in 0..d_model {
                        dot_product += q_i[k] * k_j[k];
                    }

                    scores.push(dot_product * scale);
                    indices.push(j);
                }
            }

            // If no attention connections, continue to next query position
            if scores.is_empty() {
                continue;
            }

            // Apply softmax to the sparse scores
            let max_val = scores
                .iter()
                .fold(F::neg_infinity(), |max, &x| if x > max { x } else { max });

            let mut exp_scores = Vec::with_capacity(scores.len());
            let mut sum = F::zero();

            for &score in &scores {
                let exp_val = (score - max_val).exp();
                exp_scores.push(exp_val);
                sum += exp_val;
            }

            // Normalize scores
            if sum > F::zero() {
                for exp_score in &mut exp_scores {
                    *exp_score /= sum;
                }
            }

            // Compute weighted sum of values
            for j in 0..d_model_v {
                let mut weighted_sum = F::zero();

                for k in 0..indices.len() {
                    let v_idx = indices[k];
                    weighted_sum += exp_scores[k] * v_b[[v_idx, j]];
                }

                output[[b, i, j]] = weighted_sum;
            }
        }
    }

    Ok(output)
}

/// Attention with ALiBi (Attention with Linear Biases)
///
/// Implements attention with linear biases as described in the paper
/// "Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation"
///
/// # Arguments
///
/// * `query` - Query tensor of shape [batchsize, seq_len_q, d_model]
/// * `key` - Key tensor of shape [batchsize, seq_len_k, d_model]
/// * `value` - Value tensor of shape [batchsize, seq_len_k, d_model]
/// * `slopes` - Tensor of slope values for each attention head
/// * `scale` - Scaling factor for dot product
/// * `causal` - Whether to apply causal masking
///
/// # Returns
///
/// * Output tensor of shape [batchsize, seq_len_q, d_model]
#[allow(dead_code)]
pub fn attention_with_alibi<F>(
    query: &ArrayView3<F>,
    key: &ArrayView3<F>,
    value: &ArrayView3<F>,
    slopes: &ArrayView1<F>,
    scale: F,
    causal: bool,
) -> LinalgResult<Array3<F>>
where
    F: Float + Add + Mul + Div + Sub + NumAssignOps + Zero + std::fmt::Debug,
{
    // Validate dimensions
    let (batchsize, seq_len_q, d_model) = (query.shape()[0], query.shape()[1], query.shape()[2]);
    let (_, seq_len_k, _) = (key.shape()[0], key.shape()[1], key.shape()[2]);
    let (_, _, d_model_v) = (value.shape()[0], value.shape()[1], value.shape()[2]);

    // Calculate attention scores (QK^T)
    let mut result = Array3::<F>::zeros((batchsize, seq_len_q, d_model_v));

    for b in 0..batchsize {
        // Calculate QK^T
        let q_b = query.slice(scirs2_core::ndarray::s![b, .., ..]);
        let k_b = key.slice(scirs2_core::ndarray::s![b, .., ..]);
        let v_b = value.slice(scirs2_core::ndarray::s![b, .., ..]);

        let mut scores = Array2::<F>::zeros((seq_len_q, seq_len_k));

        // Compute dot products
        for i in 0..seq_len_q {
            for j in 0..seq_len_k {
                let mut dot_product = F::zero();
                for k in 0..d_model {
                    dot_product += q_b[[i, k]] * k_b[[j, k]];
                }
                scores[[i, j]] = dot_product * scale;
            }
        }

        // Apply ALiBi bias (linear bias based on position difference)
        for i in 0..seq_len_q {
            for j in 0..seq_len_k {
                // In ALiBi, the bias is -slope * |i - j|
                // For simplicity, we'll use a single slope here
                let pos_diff =
                    F::from((i as isize - j as isize).abs() as f64).ok_or_else(|| {
                        LinalgError::ValueError(
                            "Failed to convert position difference to target type".to_string(),
                        )
                    })?;
                let slope = slopes[0]; // Using first slope for simplicity
                scores[[i, j]] -= slope * pos_diff;
            }
        }

        // Apply causal mask if requested
        if causal {
            for i in 0..seq_len_q {
                for j in 0..seq_len_k {
                    if j > i {
                        scores[[i, j]] = F::neg_infinity();
                    }
                }
            }
        }

        // Apply softmax to each row
        for i in 0..seq_len_q {
            let mut row = scores.slice_mut(scirs2_core::ndarray::s![i, ..]);

            // Find max for numerical stability
            let max_val = row.fold(F::neg_infinity(), |max, &x| if x > max { x } else { max });

            // Compute exp and sum
            let mut sum = F::zero();
            for j in 0..seq_len_k {
                let exp_val = (row[j] - max_val).exp();
                row[j] = exp_val;
                sum += exp_val;
            }

            // Normalize
            if sum > F::zero() {
                for j in 0..seq_len_k {
                    row[j] /= sum;
                }
            }
        }

        // Compute output: scores @ value
        let mut output = Array2::<F>::zeros((seq_len_q, d_model_v));

        for i in 0..seq_len_q {
            for j in 0..d_model_v {
                let mut sum = F::zero();
                for k in 0..seq_len_k {
                    sum += scores[[i, k]] * v_b[[k, j]];
                }
                output[[i, j]] = sum;
            }
        }

        // Store result
        result
            .slice_mut(scirs2_core::ndarray::s![b, .., ..])
            .assign(&output);
    }

    Ok(result)
}

/// Rotary Position Embeddings (RoPE)
///
/// Applies rotary position embeddings to query and key tensors as described in
/// "RoFormer: Enhanced Transformer with Rotary Position Embedding"
///
/// # Arguments
///
/// * `x` - Input tensor of shape [batchsize, seq_len, d_model]
/// * `freq_base` - Base frequency for the rotations (default: 10000.0)
///
/// # Returns
///
/// * Tensor with rotary position embeddings applied
#[allow(dead_code)]
pub fn rotary_embedding<F>(x: &ArrayView3<F>, freqbase: F) -> LinalgResult<Array3<F>>
where
    F: Float + Add + Mul + Div + Sub + NumAssignOps + Zero + std::fmt::Debug,
{
    let (batchsize, seq_len, d_model) = (x.shape()[0], x.shape()[1], x.shape()[2]);

    // Ensure dimension is even for proper pairing of dimensions
    if d_model % 2 != 0 {
        return Err(LinalgError::ValueError(
            "Dimension must be even for rotary embeddings".to_string(),
        ));
    }

    let mut result = Array3::<F>::zeros((batchsize, seq_len, d_model));

    // Create position frequencies
    if d_model % 2 != 0 {
        return Err(LinalgError::ValueError(
            "Model dimension must be even for rotary embeddings".to_string(),
        ));
    }
    let half_dim = d_model / 2;
    let mut freqs = Vec::with_capacity(half_dim);

    for i in 0..half_dim {
        let exponent = F::from(2.0 * i as f64 / d_model as f64).ok_or_else(|| {
            LinalgError::ValueError(
                "Failed to convert frequency exponent to target type".to_string(),
            )
        })?;
        let freq = F::one() / freqbase.powf(exponent);
        freqs.push(freq);
    }

    // Apply rotary embeddings
    for b in 0..batchsize {
        for pos in 0..seq_len {
            for (i, _) in freqs.iter().enumerate().take(half_dim) {
                let i2 = 2 * i;

                // Get current values
                let x_i = x[[b, pos, i2]];
                let x_i_plus_1 = x[[b, pos, i2 + 1]];

                // Calculate rotation
                let pos_f = F::from(pos as f64).ok_or_else(|| {
                    LinalgError::ValueError("Failed to convert position to target type".to_string())
                })?;
                let theta = pos_f * freqs[i];
                let cos_theta = theta.cos();
                let sin_theta = theta.sin();

                // Apply rotation
                result[[b, pos, i2]] = x_i * cos_theta - x_i_plus_1 * sin_theta;
                result[[b, pos, i2 + 1]] = x_i * sin_theta + x_i_plus_1 * cos_theta;
            }
        }
    }

    Ok(result)
}

/// Linear Attention
///
/// Implements linear attention which reduces computational complexity from O(n²) to O(n)
/// by using a linearized kernel function.
///
/// # Arguments
///
/// * `query` - Query tensor of shape [batchsize, seq_len_q, d_model]
/// * `key` - Key tensor of shape [batchsize, seq_len_k, d_model]
/// * `value` - Value tensor of shape [batchsize, seq_len_k, d_model]
/// * `scale` - Scaling factor for dot product
///
/// # Returns
///
/// * Output tensor of shape [batchsize, seq_len_q, d_model]
#[allow(dead_code)]
pub fn linear_attention<F>(
    query: &ArrayView3<F>,
    key: &ArrayView3<F>,
    value: &ArrayView3<F>,
    scale: F,
) -> LinalgResult<Array3<F>>
where
    F: Float + Add + Mul + Div + Sub + NumAssignOps + Zero + std::fmt::Debug,
{
    let (batchsize, seq_len_q, d_model) = (query.shape()[0], query.shape()[1], query.shape()[2]);
    let (_, seq_len_k, _) = (key.shape()[0], key.shape()[1], key.shape()[2]);
    let (_, _, d_model_v) = (value.shape()[0], value.shape()[1], value.shape()[2]);

    let mut result = Array3::<F>::zeros((batchsize, seq_len_q, d_model_v));

    // Apply elu + 1 feature mapping to query and key
    for b in 0..batchsize {
        // Feature map: elu(x) + 1
        let mut q_prime = Array2::<F>::zeros((seq_len_q, d_model));
        let mut k_prime = Array2::<F>::zeros((seq_len_k, d_model));

        // Apply feature map to query
        for i in 0..seq_len_q {
            for j in 0..d_model {
                let x = query[[b, i, j]];
                q_prime[[i, j]] = if x > F::zero() {
                    x
                } else {
                    (x.exp() - F::one()) + F::one()
                };
            }
        }

        // Apply feature map to key
        for i in 0..seq_len_k {
            for j in 0..d_model {
                let x = key[[b, i, j]] * scale;
                k_prime[[i, j]] = if x > F::zero() {
                    x
                } else {
                    (x.exp() - F::one()) + F::one()
                };
            }
        }

        // Compute KV first (linear complexity)
        let mut kv = Array2::<F>::zeros((d_model, d_model_v));

        for i in 0..d_model {
            for j in 0..d_model_v {
                let mut sum = F::zero();
                for k in 0..seq_len_k {
                    sum += k_prime[[k, i]] * value[[b, k, j]];
                }
                kv[[i, j]] = sum;
            }
        }

        // Compute normalization factor
        let mut z = Array1::<F>::zeros(seq_len_q);

        for i in 0..seq_len_q {
            let mut sum = F::zero();
            for j in 0..d_model {
                let mut k_sum = F::zero();
                for k in 0..seq_len_k {
                    k_sum += k_prime[[k, j]];
                }
                sum += q_prime[[i, j]] * k_sum;
            }
            z[i] = sum;
        }

        // Compute final output: (Q·(K^T·V)) / z
        for i in 0..seq_len_q {
            for j in 0..d_model_v {
                let mut sum = F::zero();
                for k in 0..d_model {
                    sum += q_prime[[i, k]] * kv[[k, j]];
                }

                if z[i] > F::zero() {
                    result[[b, i, j]] = if z[i] != F::zero() {
                        sum / z[i]
                    } else {
                        // Handle zero normalization - typically indicates all-masked row
                        F::zero()
                    };
                }
            }
        }
    }

    Ok(result)
}

/// Relative Position Attention
///
/// Implements attention with relative position encodings as described in
/// "Self-Attention with Relative Position Representations"
///
/// # Arguments
///
/// * `query` - Query tensor of shape [batchsize, seq_len_q, d_model]
/// * `key` - Key tensor of shape [batchsize, seq_len_k, d_model]
/// * `value` - Value tensor of shape [batchsize, seq_len_k, d_model]
/// * `rel_emb` - Relative position embeddings of shape [2*max_len-1, d_model]
/// * `scale` - Scaling factor for dot product
///
/// # Returns
///
/// * Output tensor of shape [batchsize, seq_len_q, d_model]
#[allow(dead_code)]
pub fn relative_position_attention<F>(
    query: &ArrayView3<F>,
    key: &ArrayView3<F>,
    value: &ArrayView3<F>,
    rel_emb: &ArrayView2<F>,
    scale: F,
) -> LinalgResult<Array3<F>>
where
    F: Float + Add + Mul + Div + Sub + NumAssignOps + Zero + std::fmt::Debug,
{
    let (batchsize, seq_len_q, d_model) = (query.shape()[0], query.shape()[1], query.shape()[2]);
    let (_, seq_len_k, _) = (key.shape()[0], key.shape()[1], key.shape()[2]);
    let (_, _, d_model_v) = (value.shape()[0], value.shape()[1], value.shape()[2]);

    // Validate relative embedding dimensions
    let expected_rel_emb_len = 2 * seq_len_k.max(seq_len_q) - 1;
    if rel_emb.shape()[0] != expected_rel_emb_len || rel_emb.shape()[1] != d_model {
        return Err(LinalgError::DimensionError(format!(
            "Relative embedding shape should be [{}, {}], got {:?}",
            expected_rel_emb_len,
            d_model,
            rel_emb.shape()
        )));
    }

    let mut result = Array3::<F>::zeros((batchsize, seq_len_q, d_model_v));

    // Process batch by batch
    for b in 0..batchsize {
        // Calculate content-content attention: QK^T
        let mut content_scores = Array2::<F>::zeros((seq_len_q, seq_len_k));

        for i in 0..seq_len_q {
            for j in 0..seq_len_k {
                let mut dot_product = F::zero();
                for k in 0..d_model {
                    dot_product += query[[b, i, k]] * key[[b, j, k]];
                }
                content_scores[[i, j]] = dot_product * scale;
            }
        }

        // Calculate content-position attention: QR^T
        let mut pos_scores = Array2::<F>::zeros((seq_len_q, seq_len_k));

        for i in 0..seq_len_q {
            for j in 0..seq_len_k {
                let rel_pos = (seq_len_k - 1) + i - j; // Offset for zero-indexing
                let mut dot_product = F::zero();
                for k in 0..d_model {
                    dot_product += query[[b, i, k]] * rel_emb[[rel_pos, k]];
                }
                pos_scores[[i, j]] = dot_product * scale;
            }
        }

        // Combine scores and apply softmax
        let mut combined_scores = Array2::<F>::zeros((seq_len_q, seq_len_k));

        for i in 0..seq_len_q {
            // Combine content and position scores
            for j in 0..seq_len_k {
                combined_scores[[i, j]] = content_scores[[i, j]] + pos_scores[[i, j]];
            }

            // Apply softmax
            let mut row = combined_scores.slice_mut(scirs2_core::ndarray::s![i, ..]);
            let max_val = row.fold(F::neg_infinity(), |max, &x| if x > max { x } else { max });

            let mut sum = F::zero();
            for j in 0..seq_len_k {
                let exp_val = (row[j] - max_val).exp();
                row[j] = exp_val;
                sum += exp_val;
            }

            // Normalize
            if sum > F::zero() {
                for j in 0..seq_len_k {
                    row[j] /= sum;
                }
            }
        }

        // Compute output: combined_scores @ value
        let mut output = Array2::<F>::zeros((seq_len_q, d_model_v));

        for i in 0..seq_len_q {
            for j in 0..d_model_v {
                let mut sum = F::zero();
                for k in 0..seq_len_k {
                    sum += combined_scores[[i, k]] * value[[b, k, j]];
                }
                output[[i, j]] = sum;
            }
        }

        // Store the result for this batch
        result
            .slice_mut(scirs2_core::ndarray::s![b, .., ..])
            .assign(&output);
    }

    Ok(result)
}

/// Attention with Relative Position Encodings (RPE)
///
/// Flexible interface for relative position encoding that supports
/// multiple implementation variants.
///
/// # Arguments
///
/// * `query` - Query tensor [batchsize, seq_len_q, d_model]
/// * `key` - Key tensor [batchsize, seq_len_k, d_model]
/// * `value` - Value tensor [batchsize, seq_len_k, d_model]
/// * `rel_emb` - Relative embeddings tensor
/// * `scale` - Scaling factor for attention
/// * `use_xpos` - Whether to use the xPos style of relative positioning
///
/// # Returns
///
/// * Output tensor of shape [batchsize, seq_len_q, d_model]
#[allow(dead_code)]
pub fn attention_with_rpe<F>(
    query: &ArrayView3<F>,
    key: &ArrayView3<F>,
    value: &ArrayView3<F>,
    rel_emb: &ArrayView2<F>,
    scale: F,
    use_xpos: bool,
) -> LinalgResult<Array3<F>>
where
    F: Float + Add + Mul + Div + Sub + NumAssignOps + Zero + std::fmt::Debug,
{
    // If using xPos, apply the specialized implementation
    if use_xpos {
        // This is a simplified implementation of xPos relative position encoding
        // In a full implementation, we would implement the complete xPos algorithm

        let (batchsize, seq_len_q, d_model) =
            (query.shape()[0], query.shape()[1], query.shape()[2]);
        let (_, seq_len_k, _) = (key.shape()[0], key.shape()[1], key.shape()[2]);
        let (_, _, _d_model_v) = (value.shape()[0], value.shape()[1], value.shape()[2]); // For future use

        // Create scaled arrays for computation

        // Apply xPos scaling to query and key based on position
        let mut q_scaled = Array3::<F>::zeros((batchsize, seq_len_q, d_model));
        let mut k_scaled = Array3::<F>::zeros((batchsize, seq_len_k, d_model));

        // Apply rotary-style position encoding with xPos modifications
        for b in 0..batchsize {
            for i in 0..seq_len_q {
                let pos_i = F::from(i as f64 + 1.0).expect("Operation failed"); // 1-indexed position
                for j in 0..d_model {
                    // Apply position-dependent scaling
                    let dim_factor = F::from(j as f64 / d_model as f64).expect("Operation failed");
                    let scale_factor = F::one() / pos_i.powf(dim_factor);
                    q_scaled[[b, i, j]] = query[[b, i, j]] * scale_factor;
                }
            }

            for i in 0..seq_len_k {
                let pos_i = F::from(i as f64 + 1.0).expect("Operation failed"); // 1-indexed position
                for j in 0..d_model {
                    // Apply position-dependent scaling
                    let dim_factor = F::from(j as f64 / d_model as f64).expect("Operation failed");
                    let scale_factor = F::one() / pos_i.powf(dim_factor);
                    k_scaled[[b, i, j]] = key[[b, i, j]] * scale_factor;
                }
            }
        }

        // Now compute attention with the scaled tensors
        return attention(&q_scaled.view(), &k_scaled.view(), value, None, scale);
    }

    // Otherwise use standard relative position embeddings
    relative_position_attention(query, key, value, rel_emb, scale)
}