rustytorch/crates/training/rtx-flash-metal-attention/metal/flash_attention_backward_dq.metal

//
// Flash Attention Backward Pass - dQ Kernel
//
// Computes gradient with respect to Q:
// dQ = scale * dP @ K
// where dP = P * (dO @ V^T - D), D = rowsum(dO * O)
//

#include <metal_stdlib>
using namespace metal;

// Apple Silicon GPUs have 32KB threadgroup memory limit

#ifndef BLOCK_Q
#define BLOCK_Q 16
#endif

#ifndef BLOCK_KV
#define BLOCK_KV 16
#endif

#ifndef HEAD_DIM
#define HEAD_DIM 64
#endif

/// Parameters for Flash Attention backward pass
struct FlashBackwardParams {
    uint batch_size;
    uint num_heads;
    uint seq_len_q;
    uint seq_len_kv;
    uint head_dim;
    float softmax_scale;
    uint causal;
};

/// Compute dQ gradient
///
/// Grid: (num_q_blocks, num_heads, batch_size)
/// Threadgroup: (BLOCK_Q, 1, 1)
kernel void flash_attention_backward_dq(
    device const float* Q [[buffer(0)]],       // [batch, heads, seq_q, head_dim]
    device const float* K [[buffer(1)]],       // [batch, heads, seq_kv, head_dim]
    device const float* V [[buffer(2)]],       // [batch, heads, seq_kv, head_dim]
    device const float* O [[buffer(3)]],       // [batch, heads, seq_q, head_dim]
    device const float* dO [[buffer(4)]],      // [batch, heads, seq_q, head_dim]
    device const float* LSE [[buffer(5)]],     // [batch, heads, seq_q]
    device float* dQ [[buffer(6)]],            // [batch, heads, seq_q, head_dim]
    constant FlashBackwardParams& params [[buffer(7)]],
    uint3 tgid [[threadgroup_position_in_grid]],
    uint tid [[thread_index_in_threadgroup]]
) {
    // Shared memory for tiles
    threadgroup float Q_shared[BLOCK_Q * HEAD_DIM];
    threadgroup float K_shared[BLOCK_KV * HEAD_DIM];
    threadgroup float V_shared[BLOCK_KV * HEAD_DIM];
    threadgroup float dO_shared[BLOCK_Q * HEAD_DIM];
    threadgroup float O_shared[BLOCK_Q * HEAD_DIM];

    uint batch_idx = tgid.z;
    uint head_idx = tgid.y;
    uint q_block = tgid.x;
    uint q_start = q_block * BLOCK_Q;
    uint q_idx = q_start + tid;

    // Memory strides
    uint stride_batch = params.num_heads * params.seq_len_q * params.head_dim;
    uint stride_head = params.seq_len_q * params.head_dim;
    uint base_qo = batch_idx * stride_batch + head_idx * stride_head;

    uint stride_kv_batch = params.num_heads * params.seq_len_kv * params.head_dim;
    uint stride_kv_head = params.seq_len_kv * params.head_dim;
    uint base_kv = batch_idx * stride_kv_batch + head_idx * stride_kv_head;

    uint lse_base = batch_idx * params.num_heads * params.seq_len_q + head_idx * params.seq_len_q;

    // Load Q, O, dO tiles
    if (q_idx < params.seq_len_q) {
        for (uint d = 0; d < params.head_dim; d++) {
            Q_shared[tid * HEAD_DIM + d] = Q[base_qo + q_idx * params.head_dim + d];
            O_shared[tid * HEAD_DIM + d] = O[base_qo + q_idx * params.head_dim + d];
            dO_shared[tid * HEAD_DIM + d] = dO[base_qo + q_idx * params.head_dim + d];
        }
    }
    threadgroup_barrier(mem_flags::mem_threadgroup);

    // Compute D = rowsum(dO * O) for this Q row
    float D_i = 0.0f;
    if (q_idx < params.seq_len_q) {
        for (uint d = 0; d < params.head_dim; d++) {
            D_i += dO_shared[tid * HEAD_DIM + d] * O_shared[tid * HEAD_DIM + d];
        }
    }

    // Get LSE for this row
    float lse_i = (q_idx < params.seq_len_q) ? LSE[lse_base + q_idx] : 0.0f;

    // Accumulator for dQ
    float dq_acc[HEAD_DIM];
    for (uint d = 0; d < params.head_dim; d++) {
        dq_acc[d] = 0.0f;
    }

    // Iterate over K/V blocks
    uint num_kv_blocks = (params.seq_len_kv + BLOCK_KV - 1) / BLOCK_KV;

    for (uint kv_block = 0; kv_block < num_kv_blocks; kv_block++) {
        uint kv_start = kv_block * BLOCK_KV;

        // Causal: skip future blocks
        if (params.causal != 0 && kv_start > q_start + BLOCK_Q - 1) {
            break;
        }

        // Load K, V tiles
        for (uint i = tid; i < BLOCK_KV * params.head_dim; i += BLOCK_Q) {
            uint kv_row = i / params.head_dim;
            uint d = i % params.head_dim;
            uint kv_idx = kv_start + kv_row;

            if (kv_idx < params.seq_len_kv) {
                K_shared[kv_row * HEAD_DIM + d] = K[base_kv + kv_idx * params.head_dim + d];
                V_shared[kv_row * HEAD_DIM + d] = V[base_kv + kv_idx * params.head_dim + d];
            }
        }
        threadgroup_barrier(mem_flags::mem_threadgroup);

        // Compute dQ contribution from this K/V block
        if (q_idx < params.seq_len_q) {
            for (uint j = 0; j < BLOCK_KV; j++) {
                uint kv_idx = kv_start + j;
                if (kv_idx >= params.seq_len_kv) continue;
                if (params.causal != 0 && kv_idx > q_idx) continue;

                // Recompute attention score
                float score = 0.0f;
                for (uint d = 0; d < params.head_dim; d++) {
                    score += Q_shared[tid * HEAD_DIM + d] * K_shared[j * HEAD_DIM + d];
                }
                score *= params.softmax_scale;

                // Recompute P_ij = exp(score - lse)
                float p_ij = exp(score - lse_i);

                // Compute dS_ij = P_ij * (dO @ V^T - D)
                float dov = 0.0f;
                for (uint d = 0; d < params.head_dim; d++) {
                    dov += dO_shared[tid * HEAD_DIM + d] * V_shared[j * HEAD_DIM + d];
                }
                float ds_ij = p_ij * (dov - D_i);

                // Accumulate dQ += dS @ K
                for (uint d = 0; d < params.head_dim; d++) {
                    dq_acc[d] += ds_ij * K_shared[j * HEAD_DIM + d];
                }
            }
        }
        threadgroup_barrier(mem_flags::mem_threadgroup);
    }

    // Write dQ with scale
    if (q_idx < params.seq_len_q) {
        for (uint d = 0; d < params.head_dim; d++) {
            dQ[base_qo + q_idx * params.head_dim + d] = dq_acc[d] * params.softmax_scale;
        }
    }
}