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metal : optimize softmax

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Georgi Gerganov 2024-02-01 20:53:29 +02:00
parent 56e45a239e
commit cda5a60a41
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2 changed files with 22 additions and 17 deletions
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5
ggml-metal.m
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@ -2285,8 +2285,9 @@ static bool ggml_metal_graph_compute(
const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !! const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !!
const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !!
GGML_ASSERT(nqptg % 8 == 0); GGML_ASSERT(nqptg <= 32);
GGML_ASSERT(ncpsg % 32 == 0); GGML_ASSERT(nqptg % 8 == 0);
GGML_ASSERT(ncpsg % 32 == 0);
// simdgroups per threadgroup (a.k.a. warps) // simdgroups per threadgroup (a.k.a. warps)
// for small batches use more simdgroups (needs more tests, to confirm if it's worth it) // for small batches use more simdgroups (needs more tests, to confirm if it's worth it)

34
ggml-metal.metal
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@ -2188,6 +2188,8 @@ kernel void kernel_flash_attn_ext_f16(
// online softmax // online softmax
if (C == 32) { if (C == 32) {
half ms[Q];
for (int64_t j = 0; j < Q; ++j) { for (int64_t j = 0; j < Q; ++j) {
const int64_t p = tiisg; const int64_t p = tiisg;
@ -2197,20 +2199,22 @@ kernel void kernel_flash_attn_ext_f16(
smax = simd_max(max(smax, s)); smax = simd_max(max(smax, s));
M[j] = simd_max(max(M[j], s)); M[j] = simd_max(max(M[j], s));
const half ms = m == -INFINITY ? 0.0h : exp(m - M[j]); ms[j] = m == -INFINITY ? 0.0h : exp(m - M[j]);
const half vs = s == -INFINITY ? 0.0h : exp(s - M[j]); const half vs = s == -INFINITY ? 0.0h : exp(s - M[j]);
S[j] = S[j]*ms + simd_sum(vs); S[j] = S[j]*ms[j] + simd_sum(vs);
// create a QxQ diagonal matrix for rescaling the output
if (p == j) {
ss[j*T + C + j] = ms;
}
// the P matrix from the paper (Q rows, C columns) // the P matrix from the paper (Q rows, C columns)
ss[j*T + p] = vs; ss[j*T + p] = vs;
} }
// create a QxQ diagonal matrix for rescaling the output
if (tiisg < Q) {
ss[tiisg*T + C + tiisg] = ms[tiisg];
}
} else { } else {
half ms[Q];
for (int64_t j = 0; j < Q; ++j) { for (int64_t j = 0; j < Q; ++j) {
const half m = M[j]; const half m = M[j];
@ -2224,12 +2228,7 @@ kernel void kernel_flash_attn_ext_f16(
smax = simd_max(smax); smax = simd_max(smax);
M[j] = simd_max(M[j]); M[j] = simd_max(M[j]);
const half ms = m == -INFINITY ? 0.0h : exp(m - M[j]); ms[j] = m == -INFINITY ? 0.0h : exp(m - M[j]);
// create a QxQ diagonal matrix for rescaling the output
if (tiisg == j) {
ss[j*T + C + j] = ms;
}
// local sum // local sum
half ls = 0.0h; half ls = 0.0h;
@ -2245,7 +2244,12 @@ kernel void kernel_flash_attn_ext_f16(
ss[j*T + p] = vs; ss[j*T + p] = vs;
} }
S[j] = S[j]*ms + simd_sum(ls); S[j] = S[j]*ms[j] + simd_sum(ls);
}
// create a QxQ diagonal matrix for rescaling the output
if (tiisg < Q) {
ss[tiisg*T + C + tiisg] = ms[tiisg];
} }
} }