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Feature/attention with sink #2

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merged 2 commits into from
Sep 10, 2025
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Feature/attention with sink #2

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a317bae
Debug for softmax LSE calculation.
Henry0215 Sep 4, 2025
594365e
Debug for sink calculation.
Henry0215 Sep 5, 2025
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45 changes: 20 additions & 25 deletions csrc/flash_attn/src/flash_fwd_kernel.h
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Original file line number Diff line number Diff line change
Expand Up @@ -1196,36 +1196,31 @@ inline __device__ void combine_attn_seqk_parallel(const Params &params) {
for (int l = 1; l < kNLsePerThread; ++l) { lse_sum += __expf(lse_accum(l) - lse_max); }
SumOp<float> sum_op;
lse_sum = Allreduce<kRowsPerLoadTranspose>::run(lse_sum, sum_op);

if constexpr(Has_sink){
const int row = tidx % kRowsPerLoadTranspose;
const int col = tidx / kRowsPerLoadTranspose;
if (row < params.num_splits && col < kBlockM) {
const index_t lse_offset = row_offset_lse + col;
if(params.unpadded_lse){
// LSE is written as (h, seqlen_q, b) or (h, b, seqlen_q).
const int head_idx = lse_offset / (params.b * params.seqlen_q);
const float sink_val_exp = params.learnable_sink_ptr == nullptr ? 0.f : __expf(reinterpret_cast<float *>(params.learnable_sink_ptr)[head_idx] - lse_max);
lse_sum += sink_val_exp;
}else{
// LSE is written as (b, h, seqlen_q).
const int head_idx = (lse_offset % (params.h * params.seqlen_q)) / params.seqlen_q;
const float sink_val_exp = params.learnable_sink_ptr == nullptr ? 0.f : __expf(reinterpret_cast<float *>(params.learnable_sink_ptr)[head_idx] - lse_max);
lse_sum += sink_val_exp;
}
}
}

// For the case where all local lse == -INFINITY, we want to set lse_logsum to INFINITY. Otherwise
// lse_logsum is log(0.0) = -INFINITY and we get NaN when we do lse_accum(l) - lse_logsum.
ElementAccum lse_logsum = (lse_sum == 0.f || lse_sum != lse_sum) ? INFINITY : __logf(lse_sum) + lse_max;
// if (bidx == 0 && tidx < 32) { printf("tidx = %d, lse = %f, lse_max = %f, lse_logsum = %f\n", tidx, lse_accum(0), lse_max, lse_logsum); }

if constexpr (Has_sink) {
#pragma unroll
for (int l = 0; l < kNLsePerThread; ++l) {
const int row = l * kRowsPerLoadTranspose + tidx % kRowsPerLoadTranspose;
const int col = tidx / kRowsPerLoadTranspose;
if (row < params.num_splits && col < kBlockM) {
const index_t lse_offset = row_offset_lse + tidx / kRowsPerLoadTranspose;
if (params.unpadded_lse) {
// LSE is written as (h, seqlen_q, b) or (h, b, seqlen_q).
if (lse_offset < lse_size) {
const int head_idx = lse_offset / (params.b * params.seqlen_q);
const float lse_logsum_sink = __expf(lse_logsum);
const float sink_val_exp = params.learnable_sink_ptr == nullptr ? 0.f : __expf(reinterpret_cast<float *>(params.learnable_sink_ptr)[head_idx]);
lse_logsum = __logf(lse_logsum_sink + sink_val_exp);;
}
} else {
// LSE is written as (b, h, seqlen_q).
const int head_idx = (lse_offset % (params.h * params.seqlen_q)) / params.seqlen_q;
const float lse_logsum_sink = __expf(lse_logsum);
const float sink_val_exp = params.learnable_sink_ptr == nullptr ? 0.f : __expf(reinterpret_cast<float *>(params.learnable_sink_ptr)[head_idx]);
lse_logsum = __logf(lse_logsum_sink + sink_val_exp);
}
}
}
}
if (tidx % kRowsPerLoadTranspose == 0 && tidx / kRowsPerLoadTranspose < kBlockM) {
if (params.unpadded_lse) {
const index_t lse_offset = row_offset_lse + tidx / kRowsPerLoadTranspose;
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