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impl OwnedQuantizedModel {
// =========================================================================
// IMP-110: Multi-Head Parallel Attention
// =========================================================================
/// Reshape tensor from [seq_len, hidden_dim] to [num_heads, seq_len, head_dim]
///
/// IMP-110b: Prepares Q/K/V tensors for parallel multi-head processing.
/// Original layout stores all head features contiguously per position.
/// New layout groups by head for batched matmul operations.
///
/// # Arguments
/// * `input` - Input tensor [seq_len, hidden_dim]
/// * `seq_len` - Sequence length
/// * `num_heads` - Number of attention heads
/// * `head_dim` - Dimension per head (hidden_dim / num_heads)
///
/// # Returns
/// Reshaped tensor [num_heads, seq_len, head_dim]
#[cfg(feature = "gpu")]
pub fn reshape_for_parallel_heads(
&self,
input: &[f32],
seq_len: usize,
num_heads: usize,
head_dim: usize,
) -> Result<Vec<f32>> {
let hidden_dim = num_heads * head_dim;
let expected_len = seq_len * hidden_dim;
if input.len() != expected_len {
return Err(RealizarError::InvalidShape {
reason: format!(
"Input size {} doesn't match seq_len={} * hidden_dim={}={}",
input.len(),
seq_len,
hidden_dim,
expected_len
),
});
}
let mut reshaped = vec![0.0f32; num_heads * seq_len * head_dim];
// Transform: input[pos * hidden_dim + h * head_dim + d]
// -> reshaped[h * seq_len * head_dim + pos * head_dim + d]
for h in 0..num_heads {
for pos in 0..seq_len {
for d in 0..head_dim {
let orig_idx = pos * hidden_dim + h * head_dim + d;
let new_idx = h * seq_len * head_dim + pos * head_dim + d;
reshaped[new_idx] = input[orig_idx];
}
}
}
Ok(reshaped)
}
/// Compute batched Q@K^T scores for all heads in parallel
///
/// IMP-110c: Computes attention scores for all heads in a single batch.
/// Takes Q, K in original [seq_len, hidden_dim] layout and computes
/// Q@K^T for each head.
///
/// # Arguments
/// * `q` - Query tensor [seq_len, hidden_dim]
/// * `k` - Key tensor [seq_len, hidden_dim]
/// * `seq_len` - Sequence length
/// * `num_heads` - Number of attention heads
/// * `head_dim` - Dimension per head
/// * `scale` - Attention scale (1/sqrt(head_dim))
///
/// # Returns
/// Batched scores [num_heads, seq_len, seq_len]
#[cfg(feature = "gpu")]
pub fn parallel_batched_qk_scores(
&self,
q: &[f32],
k: &[f32],
seq_len: usize,
num_heads: usize,
head_dim: usize,
scale: f32,
) -> Result<Vec<f32>> {
use crate::gpu::HybridScheduler;
// Reshape Q and K to [num_heads, seq_len, head_dim]
let q_reshaped = self.reshape_for_parallel_heads(q, seq_len, num_heads, head_dim)?;
let k_reshaped = self.reshape_for_parallel_heads(k, seq_len, num_heads, head_dim)?;
let mut scheduler = HybridScheduler::with_threshold(1000).map_err(|e| {
RealizarError::UnsupportedOperation {
operation: "HybridScheduler::with_threshold".to_string(),
reason: format!("GPU scheduler initialization failed: {e}"),
}
})?;
// For each head: Q_h @ K_h^T -> [seq_len, seq_len]
// Total output: [num_heads, seq_len, seq_len]
let mut all_scores = Vec::with_capacity(num_heads * seq_len * seq_len);
for h in 0..num_heads {
let head_start = h * seq_len * head_dim;
let q_h = &q_reshaped[head_start..head_start + seq_len * head_dim];
let k_h = &k_reshaped[head_start..head_start + seq_len * head_dim];
// Transpose K_h: [seq_len, head_dim] -> [head_dim, seq_len]
let mut k_t = vec![0.0f32; head_dim * seq_len];
for i in 0..seq_len {
for j in 0..head_dim {
k_t[j * seq_len + i] = k_h[i * head_dim + j];
}
}
// Q_h @ K_h^T: [seq_len, head_dim] @ [head_dim, seq_len] -> [seq_len, seq_len]
let scores = scheduler
.matmul(q_h, &k_t, seq_len, head_dim, seq_len)
.map_err(|e| RealizarError::UnsupportedOperation {
operation: "parallel_batched_qk_scores".to_string(),
reason: format!("GPU matmul failed: {e}"),
})?;
// Apply scale and accumulate
for s in &scores {
all_scores.push(s * scale);
}
}
Ok(all_scores)
}
/// Multi-head attention with parallel head processing
///
/// IMP-110a: Processes all attention heads in parallel batches instead
/// of iterating head-by-head. This enables better GPU utilization.
///
/// # Arguments
/// * `q` - Query tensor [seq_len, hidden_dim]
/// * `k` - Key tensor [seq_len, hidden_dim]
/// * `v` - Value tensor [seq_len, hidden_dim]
/// * `seq_len` - Sequence length
///
/// # Returns
/// Attention output [seq_len, hidden_dim]
#[cfg(feature = "gpu")]
pub fn parallel_multihead_attention_gpu(
&self,
q: &[f32],
k: &[f32],
v: &[f32],
seq_len: usize,
) -> Result<Vec<f32>> {
use crate::gpu::HybridScheduler;
let hidden_dim = self.config.hidden_dim;
let num_heads = self.config.num_heads;
let head_dim = hidden_dim / num_heads;
let scale = 1.0 / (head_dim as f32).sqrt();
// Get batched scores for all heads: [num_heads, seq_len, seq_len]
let batched_scores =
self.parallel_batched_qk_scores(q, k, seq_len, num_heads, head_dim, scale)?;
// Apply causal mask and softmax per head
let mut batched_weights = vec![0.0f32; num_heads * seq_len * seq_len];
for h in 0..num_heads {
let head_offset = h * seq_len * seq_len;
let head_scores = &batched_scores[head_offset..head_offset + seq_len * seq_len];
let head_weights = self.apply_causal_mask_softmax(head_scores, seq_len);
batched_weights[head_offset..head_offset + seq_len * seq_len]
.copy_from_slice(&head_weights);
}
// Reshape V to [num_heads, seq_len, head_dim]
let v_reshaped = self.reshape_for_parallel_heads(v, seq_len, num_heads, head_dim)?;
// Compute attention output for all heads
let mut scheduler = HybridScheduler::with_threshold(1000).map_err(|e| {
RealizarError::UnsupportedOperation {
operation: "HybridScheduler::with_threshold".to_string(),
reason: format!("GPU scheduler initialization failed: {e}"),
}
})?;
// Output: [seq_len, hidden_dim]
let mut output = vec![0.0f32; seq_len * hidden_dim];
for h in 0..num_heads {
let weights_offset = h * seq_len * seq_len;
let v_offset = h * seq_len * head_dim;
let head_weights = &batched_weights[weights_offset..weights_offset + seq_len * seq_len];
let v_h = &v_reshaped[v_offset..v_offset + seq_len * head_dim];
// weights @ V_h: [seq_len, seq_len] @ [seq_len, head_dim] -> [seq_len, head_dim]
let head_output = scheduler
.matmul(head_weights, v_h, seq_len, seq_len, head_dim)
.map_err(|e| RealizarError::UnsupportedOperation {
operation: "parallel_multihead_attention_gpu".to_string(),
reason: format!("GPU matmul failed: {e}"),
})?;
// Copy to output in original layout
for pos in 0..seq_len {
let out_start = pos * hidden_dim + h * head_dim;
let head_start = pos * head_dim;
output[out_start..out_start + head_dim]
.copy_from_slice(&head_output[head_start..head_start + head_dim]);
}
}
Ok(output)
}
// =========================================================================
// IMP-111: Flash Attention-style Tiled Computation
// =========================================================================
/// Standard softmax (reference implementation)
///
/// IMP-111a: Reference implementation for testing online softmax.
/// Computes softmax in the standard way: exp(x - max) / sum(exp(x - max))
pub fn standard_softmax(&self, scores: &[f32]) -> Vec<f32> {
if scores.is_empty() {
return Vec::new();
}
// Find max for numerical stability
let max_score = scores.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
// Compute exp(x - max) and sum
let exp_scores: Vec<f32> = scores.iter().map(|&s| (s - max_score).exp()).collect();
let sum: f32 = exp_scores.iter().sum();
// Normalize
exp_scores.iter().map(|&e| e / sum).collect()
}
/// Online softmax with tiled processing (O(1) memory per tile)
///
/// IMP-111a: Implements the "online softmax" algorithm that processes
/// data in tiles without materializing the full softmax denominator.
///
/// Algorithm:
/// 1. Process tiles, tracking running max (m) and denominator (d)
/// 2. When new tile has larger max, rescale previous denominator
/// 3. Final pass normalizes all values
///
/// # Arguments
/// * `scores` - Input scores to apply softmax
/// * `tile_size` - Size of each tile for processing
///
/// # Returns
/// Softmax probabilities
pub fn online_softmax(&self, scores: &[f32], tile_size: usize) -> Result<Vec<f32>> {
if scores.is_empty() {
return Ok(Vec::new());
}
let n = scores.len();
let tile_size = tile_size.max(1);
// Running statistics
let mut global_max = f32::NEG_INFINITY;
let mut global_sum = 0.0f32;
// First pass: compute global max and sum using online algorithm
for tile_start in (0..n).step_by(tile_size) {
let tile_end = (tile_start + tile_size).min(n);
// Find local max in this tile
let local_max = scores[tile_start..tile_end]
.iter()
.cloned()
.fold(f32::NEG_INFINITY, f32::max);
if local_max > global_max {
// Rescale previous sum when we find a new max
let rescale = (global_max - local_max).exp();
global_sum *= rescale;
global_max = local_max;
}
// Add this tile's contribution to sum
for &s in &scores[tile_start..tile_end] {
global_sum += (s - global_max).exp();
}
}
// Second pass: compute final softmax values
let mut result = Vec::with_capacity(n);
for &s in scores {
result.push((s - global_max).exp() / global_sum);
}
Ok(result)
}
/// Standard single-head attention (reference implementation)
///
/// IMP-111b: Reference implementation that materializes full attention matrix.
/// Used to verify tiled attention correctness.
///
/// # Arguments
/// * `q` - Query tensor [seq_len, head_dim]
/// * `k` - Key tensor [seq_len, head_dim]
/// * `v` - Value tensor [seq_len, head_dim]
/// * `seq_len` - Sequence length
/// * `head_dim` - Dimension per head
/// * `scale` - Attention scale (1/sqrt(head_dim))
pub fn standard_single_head_attention(
&self,
q: &[f32],
k: &[f32],
v: &[f32],
seq_len: usize,
head_dim: usize,
scale: f32,
) -> Result<Vec<f32>> {
// Compute attention scores: Q @ K^T -> [seq_len, seq_len]
let mut scores = vec![0.0f32; seq_len * seq_len];
for i in 0..seq_len {
for j in 0..seq_len {
let mut dot = 0.0f32;
for d in 0..head_dim {
dot += q[i * head_dim + d] * k[j * head_dim + d];
}
scores[i * seq_len + j] = dot * scale;
}
}
// Apply softmax per row
let mut weights = vec![0.0f32; seq_len * seq_len];
for i in 0..seq_len {
let row_start = i * seq_len;
let row = &scores[row_start..row_start + seq_len];
let softmax = self.standard_softmax(row);
weights[row_start..row_start + seq_len].copy_from_slice(&softmax);
}
// Compute output: weights @ V -> [seq_len, head_dim]
let mut output = vec![0.0f32; seq_len * head_dim];
for i in 0..seq_len {
for d in 0..head_dim {
let mut acc = 0.0f32;
for j in 0..seq_len {
acc += weights[i * seq_len + j] * v[j * head_dim + d];
}
output[i * head_dim + d] = acc;
}
}
Ok(output)
}
/// Tiled single-head attention (non-causal)
///
/// IMP-111b: Flash Attention-style tiled computation.
/// Processes K/V in tiles, maintaining running softmax statistics.
#[allow(clippy::too_many_arguments)]
pub fn tiled_single_head_attention(
&self,
q: &[f32],
k: &[f32],
v: &[f32],
seq_len: usize,
head_dim: usize,
scale: f32,
tile_size: usize,
) -> Result<Vec<f32>> {
let tile_size = tile_size.max(1);
let mut output = vec![0.0f32; seq_len * head_dim];
// Process each query position
for i in 0..seq_len {
let q_i = &q[i * head_dim..(i + 1) * head_dim];
// Running statistics for online softmax
let mut running_max = f32::NEG_INFINITY;
let mut running_sum = 0.0f32;
let mut running_output = vec![0.0f32; head_dim];
// Process K/V in tiles
for tile_start in (0..seq_len).step_by(tile_size) {
let tile_end = (tile_start + tile_size).min(seq_len);
// Compute scores for this tile: q_i @ K_tile^T
let mut tile_scores = Vec::with_capacity(tile_end - tile_start);
for j in tile_start..tile_end {
let mut dot = 0.0f32;
for d in 0..head_dim {
dot += q_i[d] * k[j * head_dim + d];
}
tile_scores.push(dot * scale);
}
// Find tile max
let tile_max = tile_scores
.iter()
.cloned()
.fold(f32::NEG_INFINITY, f32::max);
// Update running statistics
let new_max = running_max.max(tile_max);
// Rescale previous output and sum
if new_max > running_max && running_sum > 0.0 {
let rescale = (running_max - new_max).exp();
running_sum *= rescale;
for out_val in &mut running_output {
*out_val *= rescale;
}
}
running_max = new_max;
// Accumulate this tile's contribution
for (idx, &score) in tile_scores.iter().enumerate() {
let j = tile_start + idx;
let weight = (score - running_max).exp();
running_sum += weight;
for d in 0..head_dim {
running_output[d] += weight * v[j * head_dim + d];
}
}
}
// Normalize output
for d in 0..head_dim {
output[i * head_dim + d] = running_output[d] / running_sum;
}
}
Ok(output)
}
}