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llama.cpp
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feat: Parallelize of d_state for mamba-1 

Branch: GraniteFourPerf

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
This commit is contained in:
Gabe Goodhart 2025-07-22 11:00:19 -06:00
parent d06d08769c
commit 80545ef568
2 changed files with 85 additions and 29 deletions
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13
ggml/src/ggml-metal/ggml-metal.m
View File

@ -3015,12 +3015,9 @@ static bool ggml_metal_encode_node(
[encoder setBuffer:id_dst offset:offs_dst atIndex:7];
[encoder setBytes:&args length:sizeof(args) atIndex:8];
if (ne30 == 1) {
// Mamba-2
// One shared memory bucket for each simd group in the threadgroup
// One shared memory bucket for each simd group in the threadgroup
if (d_state >= 32) {
const int64_t shmem_size = d_state / 32;
GGML_ASSERT(shmem_size * 32 == d_state);
// The final simd_sum won't work if the number of simd groups is
// larger than the size of a single simd group. If this case is
@ -3033,10 +3030,14 @@ static bool ggml_metal_encode_node(
GGML_ASSERT(d_state <= (int64_t)pipeline.maxTotalThreadsPerThreadgroup);
[encoder setThreadgroupMemoryLength:(shmem_size)*sizeof(float) atIndex:0];
}
if (ne30 == 1) {
// Mamba-2
[encoder dispatchThreadgroups:MTLSizeMake(d_inner, n_head, n_seqs) threadsPerThreadgroup:MTLSizeMake(d_state, 1, 1)];
} else {
GGML_ASSERT(d_inner == 1);
[encoder dispatchThreadgroups:MTLSizeMake(n_head, n_seqs, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
[encoder dispatchThreadgroups:MTLSizeMake(n_head, n_seqs, 1) threadsPerThreadgroup:MTLSizeMake(d_state, 1, 1)];
}
} break;
case GGML_OP_RWKV_WKV6:

101
ggml/src/ggml-metal/ggml-metal.metal
View File

@ -1700,10 +1700,16 @@ kernel void kernel_ssm_scan_f32(
device const void * src5,
device const void * src6,
device float * dst,
threadgroup float * shared [[threadgroup(0)]],
constant ggml_metal_kargs_ssm_scan & args,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort sgptg[[simdgroups_per_threadgroup]],
uint3 tgpg[[threadgroups_per_grid]]) {
const int64_t i0 = tpitg.x;
const int64_t i1 = 0;
const int64_t ir = tgpig.x; // current head
const int64_t i3 = tgpig.y; // current seq
@ -1718,37 +1724,85 @@ kernel void kernel_ssm_scan_f32(
const int64_t ng = args.n_group;
const int64_t n_t = args.n_seq_tokens;
const int64_t s_off = nr * nh * n_t * args.n_seqs * sizeof(float);
const int64_t s_off = args.s_off;
device const int32_t * ids = (device const int32_t *) src6;
device const float * s0 = (device const float *) ((device const char *) src0 + ir*args.nb02 + ids[i3]*args.nb03);
device float * s = (device float *) ((device char *) dst + ir*args.nb02 + i3*args.nb03 + s_off);
device const float * s0_buff = (device const float *) ((device const char *) src0 + ir*args.nb02 + ids[i3]*args.nb03);
device float * s_buff = (device float *) ((device char *) dst + ir*args.nb02 + i3*args.nb03 + s_off);
const int64_t i = i0 + i1*nc;
float s0 = s0_buff[i];
float s = s_buff[i];
device const float * A = (device const float *) ((device const char *) src3 + ir*args.nb31);
device const float * x_block = (device const float *) ((device const char *) src1 + i1*nb10 + ir*args.nb11 + i3*args.nb13);
device const float * dt_block = (device const float *) ((device const char *) src2 + ir*nb20 + i3*args.nb22);
device const float * B_block = (device const float *) ((device const char *) src4 + (ir & (ng - 1))*args.nb41 + i3*args.nb43);
device const float * C_block = (device const float *) ((device const char *) src5 + (ir & (ng - 1))*args.nb51 + i3*args.nb53);
device float * y_block = (device float *) ((device char *) dst + (i1 + ir*(nr) + i3*(n_t*nh*nr))*nb00);
for (int64_t i2 = 0; i2 < n_t; ++i2) {
device const float * x = (device const float *) ((device const char *) src1 + i1*nb10 + ir*args.nb11 + i2*args.nb12 + i3*args.nb13); // {dim, nh, nt, ns}
device const float * dt = (device const float *) ((device const char *) src2 + ir*nb20 + i2*args.nb21 + i3*args.nb22); // {nh, nt, ns}
device const float * A = (device const float *) ((device const char *) src3 + ir*args.nb31); // {d_state, nh}
device const float * B = (device const float *) ((device const char *) src4 + (ir & (ng - 1))*args.nb41 + i2*args.nb42 + i3*args.nb43); // {d_state, ng, nt, ns}
device const float * C = (device const float *) ((device const char *) src5 + (ir & (ng - 1))*args.nb51 + i2*args.nb52 + i3*args.nb53); // {d_state, ng, nt, ns}
device float * y = (device float *) ((device char *) dst + (i1 + ir*(nr) + i2*(nh*nr) + i3*(n_t*nh*nr))*nb00); // {dim, nh, nt, ns}
device const float * x = (device const float *) ((device const char *) x_block + i2*args.nb12); // {dim, nh, nt, ns}
device const float * dt = (device const float *) ((device const char *) dt_block + i2*args.nb21); // {nh, nt, ns}
device const float * B = (device const float *) ((device const char *) B_block + i2*args.nb42); // {d_state, ng, nt, ns}
device const float * C = (device const float *) ((device const char *) C_block + i2*args.nb52); // {d_state, ng, nt, ns}
device float * y = (device float *) ((device char *) y_block + i2*(nh*nr*nb00)); // {dim, nh, nt, ns}
const float dt_soft_plus = dt[0] <= 20.0f ? log(1.0f + exp(dt[0])) : dt[0];
const float x_dt = x[0] * dt_soft_plus;
float sumf = 0.0f;
for (int64_t i0 = 0; i0 < nc; ++i0) {
const int64_t i = i0 + i1*nc;
const float state = (s0[i] * exp(dt_soft_plus * A[i0])) + (B[i0] * x_dt);
sumf += state * C[i0];
s[i] = state;
const float state = (s0 * exp(dt_soft_plus * A[i0])) + (B[i0] * x_dt);
s = state;
// Parallel sum: This relies on the fact that this kernel will be
// dispatched with each threadgroup having (d_state, 1, 1) threads which
// are subdivided into SIMD groups of size `sgptg`. The goal is to
// compute y = sum({state * C[i] for i in range(d_state)}).
// To parallelize this effectively, we first use simd_sum over each SIMD
// group to compute the sum of each SIMD group, then place the result in
// the SIMD group's indexed bucket in the shared memory. We then sum
// over the individual group sums to compute the final sum.
// Computed for each thread
float sumf = state * C[i0];
// Sum the threads in the simd group => simd sum
sumf = simd_sum(sumf);
if (sgptg > 1) {
// Once per simd group, place the group sum into the shared buffer
if (tiisg == 0) {
shared[sgitg] = sumf;
}
// Wait for all threads in the threadgroup to reach this point. This
// ensures that all elements of the shared buffer are populated with the
// sum of the individual simd groups.
threadgroup_barrier(mem_flags::mem_threadgroup);
// For simd group 0 at indices < num simd groups, extract the shared
// simd sum
sumf = 0.0f;
if (sgitg == 0) {
if (tiisg < sgptg) {
sumf = shared[tiisg];
}
sumf = simd_sum(sumf);
if (tiisg == 0) {
y[0] = sumf;
}
}
} else if (tiisg == 0) {
y[0] = sumf;
}
y[0] = sumf;
// recurse
s0 = s;
}
// Assign the final state to the output buffer
s_buff[i] = s;
}
// ref: ggml.c:ggml_compute_forward_ssm_scan_f32, Mamba-2 part
@ -1770,6 +1824,7 @@ kernel void kernel_ssm_scan_f32_group(
ushort sgptg[[simdgroups_per_threadgroup]],
uint3 tgpg[[threadgroups_per_grid]]) {
const int64_t i0 = tpitg.x;
const int64_t i1 = tgpig.x;
const int64_t ir = tgpig.y; // current head
const int64_t i3 = tgpig.z; // current seq
@ -1790,7 +1845,7 @@ kernel void kernel_ssm_scan_f32_group(
device const float * s0_buff = (device const float *) ((device const char *) src0 + ir*args.nb02 + ids[i3]*args.nb03);
device float * s_buff = (device float *) ((device char *) dst + ir*args.nb02 + i3*args.nb03 + s_off);
const int64_t i = tpitg.x + i1*nc;
const int64_t i = i0 + i1*nc;
float s0 = s0_buff[i];
float s = s_buff[i];
@ -1812,7 +1867,7 @@ kernel void kernel_ssm_scan_f32_group(
const float x_dt = x[0] * dt_soft_plus;
const float dA = exp(dt_soft_plus * A[0]);
const float state = (s0 * dA) + (B[tpitg.x] * x_dt);
const float state = (s0 * dA) + (B[i0] * x_dt);
s = state;
// Parallel sum: This relies on the fact that this kernel will be
@ -1825,7 +1880,7 @@ kernel void kernel_ssm_scan_f32_group(
// over the individual group sums to compute the final sum.
// Computed for each thread
float sumf = state * C[tpitg.x];
float sumf = state * C[i0];
// Sum the threads in the simd group => simd sum
sumf = simd_sum(sumf);