sdpa_vector.h

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// Copyright © 2024 Apple Inc.
 
#include <metal_simdgroup>
 
using namespace metal;
 
template <typename T, int D>
[[kernel]] void sdpa_vector(
    const device T* queries [[buffer(0)]],
    const device T* keys [[buffer(1)]],
    const device T* values [[buffer(2)]],
    device T* out [[buffer(3)]],
    const constant int& gqa_factor,
    const constant int& N,
    const constant size_t& k_stride,
    const constant size_t& v_stride,
    const constant float& scale,
    uint3 tid [[threadgroup_position_in_grid]],
    uint simd_gid [[simdgroup_index_in_threadgroup]],
    uint simd_lid [[thread_index_in_simdgroup]]) {
  constexpr int BN = 32;
  constexpr int BD = 32;
  constexpr int elem_per_thread = D / BD;
 
  const int stride = BN * D;
 
  typedef float U;
 
  thread U q[elem_per_thread];
  thread U k[elem_per_thread];
  thread U o[elem_per_thread];
 
  threadgroup U outputs[BN * BD];
  threadgroup U max_scores[BN];
  threadgroup U sum_exp_scores[BN];
 
  // Adjust positions
  const int head_idx = tid.y;
  const int kv_head_idx = head_idx / gqa_factor;
  queries += head_idx * D + simd_lid * elem_per_thread;
  keys += kv_head_idx * k_stride + simd_gid * D + simd_lid * elem_per_thread;
  values += kv_head_idx * v_stride + simd_gid * D + simd_lid * elem_per_thread;
  out += head_idx * D + simd_gid * elem_per_thread;
 
  // Read the query and 0 the output accumulator
  for (int i = 0; i < elem_per_thread; i++) {
    q[i] = static_cast<U>(scale) * queries[i];
  }
  for (int i = 0; i < elem_per_thread; i++) {
    o[i] = 0;
  }
 
  U max_score = -INFINITY;
  U sum_exp_score = 0;
 
  // For each key
  for (int i = simd_gid; i < N; i += BN) {
    // Read the key
    for (int i = 0; i < elem_per_thread; i++) {
      k[i] = keys[i];
    }
 
    // Compute the i-th score
    U score = 0;
    for (int i = 0; i < elem_per_thread; i++) {
      score += q[i] * k[i];
    }
    score = simd_sum(score);
 
    // Update the accumulators
    U new_max = max(max_score, score);
    U factor = fast::exp(max_score - new_max);
    U exp_score = fast::exp(score - new_max);
 
    max_score = new_max;
    sum_exp_score = sum_exp_score * factor + exp_score;
 
    // Update the output accumulator
    for (int i = 0; i < elem_per_thread; i++) {
      o[i] = o[i] * factor + exp_score * values[i];
    }
 
    // Move the pointers to the next kv
    keys += stride;
    values += stride;
  }
  threadgroup_barrier(mem_flags::mem_threadgroup);
 
  // Each thread has a partial part of the output so we need to combine them.
 
  // First let's communicate the max and sum_exp
  if (simd_lid == 0) {
    max_scores[simd_gid] = max_score;
    sum_exp_scores[simd_gid] = sum_exp_score;
  }
  threadgroup_barrier(mem_flags::mem_threadgroup);
  max_score = max_scores[simd_lid];
  U new_max = simd_max(max_score);
  U factor = fast::exp(max_score - new_max);
  sum_exp_score = simd_sum(sum_exp_scores[simd_lid] * factor);
 
  // Now we need to aggregate all the outputs
  for (int i = 0; i < elem_per_thread; i++) {
    outputs[simd_lid * BD + simd_gid] = o[i];
    threadgroup_barrier(mem_flags::mem_threadgroup);
    o[i] = simd_sum(outputs[simd_gid * BD + simd_lid] * factor) / sum_exp_score;
    threadgroup_barrier(mem_flags::mem_threadgroup);
  }
 
  // And write the output
  if (simd_lid == 0) {
    for (int i = 0; i < elem_per_thread; i++) {
      out[i] = static_cast<T>(o[i]);
    }
  }
}