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jeffbolznv
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vulkan: Implement topk_moe fused shader, ported from CUDA (ggml-org#16641)
This is similar to the CUDA shader from ggml-org#16130, but doesn't use shared memory and handles different subgroup sizes.
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4 files changed

+412
-8
lines changed

‎ggml/src/ggml-impl.h‎

Lines changed: 11 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -565,14 +565,23 @@ static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) {
565565
#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x)
566566
#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x)
567567

568+
static inline int32_t ggml_node_get_use_count(const struct ggml_cgraph * cgraph, int node_idx) {
569+
const struct ggml_tensor * node = cgraph->nodes[node_idx];
570+
571+
size_t hash_pos = ggml_hash_find(&cgraph->visited_hash_set, node);
572+
if (!ggml_bitset_get(cgraph->visited_hash_set.used, hash_pos)) {
573+
return 0;
574+
}
575+
return cgraph->use_counts[hash_pos];
576+
}
577+
568578
// return true if the node's results are only used by N other nodes
569579
// and can be fused into their calculations.
570580
static inline bool ggml_node_has_n_uses(const struct ggml_cgraph * cgraph, int node_idx, int32_t n_uses) {
571581
const struct ggml_tensor * node = cgraph->nodes[node_idx];
572582

573583
// check the use count against how many we're replacing
574-
size_t hash_pos = ggml_hash_find(&cgraph->visited_hash_set, node);
575-
if (!ggml_bitset_get(cgraph->visited_hash_set.used, hash_pos) || cgraph->use_counts[hash_pos] != n_uses) {
584+
if (ggml_node_get_use_count(cgraph, node_idx) != n_uses) {
576585
return false;
577586
}
578587

‎ggml/src/ggml-vulkan/ggml-vulkan.cpp‎

Lines changed: 260 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -385,6 +385,14 @@ enum shader_reduction_mode {
385385

386386
static constexpr uint32_t num_argsort_pipelines = 11;
387387
static constexpr uint32_t max_argsort_cols = 1 << (num_argsort_pipelines-1);
388+
static constexpr uint32_t num_topk_moe_pipelines = 10;
389+
390+
static constexpr std::array topk_moe_norm{ GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT,
391+
GGML_OP_VIEW, GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
392+
GGML_OP_SUM_ROWS, GGML_OP_DIV, GGML_OP_RESHAPE };
393+
static constexpr std::array topk_moe { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT,
394+
GGML_OP_VIEW, GGML_OP_GET_ROWS };
395+
388396

389397
struct vk_device_struct {
390398
std::recursive_mutex mutex;
@@ -598,6 +606,9 @@ struct vk_device_struct {
598606

599607
vk_pipeline pipeline_flash_attn_split_k_reduce;
600608

609+
// [2] is {!norm, norm}
610+
vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][2];
611+
601612
std::vector<vk_pipeline_ref> all_pipelines;
602613

603614
std::vector<std::tuple<void*, size_t, vk_buffer>> pinned_memory;
@@ -941,6 +952,11 @@ struct vk_op_multi_add_push_constants {
941952
static_assert(MAX_PARAMETER_COUNT == 12);
942953
static_assert(sizeof(vk_op_multi_add_push_constants) <= 256);
943954

955+
struct vk_op_topk_moe_push_constants {
956+
uint32_t n_rows;
957+
uint32_t n_expert_used;
958+
};
959+
944960
struct vk_op_add_id_push_constants {
945961
uint32_t ne0;
946962
uint32_t ne1;
@@ -3722,6 +3738,11 @@ static void ggml_vk_load_shaders(vk_device& device) {
37223738
ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f16_f32, "conv2d_dw_whcn_f16_f32", conv2d_dw_whcn_f16_f32_len, conv2d_dw_whcn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
37233739
ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
37243740

3741+
for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) {
3742+
ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][0], "topk_moe_f32_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0}, 1, true, true);
3743+
ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][1], "topk_moe_f32_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 1}, 1, true, true);
3744+
}
3745+
37253746
for (auto &c : compiles) {
37263747
c.wait();
37273748
}
@@ -8004,6 +8025,13 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
80048025
GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16);
80058026
GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32);
80068027

8028+
if (ctx->num_additional_fused_ops) {
8029+
uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0])));
8030+
GGML_ASSERT(idx < num_topk_moe_pipelines);
8031+
bool with_norm = ctx->num_additional_fused_ops == topk_moe_norm.size() - 1;
8032+
return ctx->device->pipeline_topk_moe[idx][with_norm];
8033+
}
8034+
80078035
if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
80088036
return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32;
80098037
}
@@ -9589,6 +9617,87 @@ static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& sub
95899617
ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX_BACK, { (uint32_t)src0->ne[0], (uint32_t)ggml_nrows(src0), op_params[0], op_params[1] }, dryrun);
95909618
}
95919619

9620+
static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx, bool dryrun = false) {
9621+
9622+
bool with_norm = ctx->num_additional_fused_ops == topk_moe_norm.size() - 1;
9623+
ggml_tensor * logits = cgraph->nodes[node_idx + 0]->src[0];
9624+
ggml_tensor * weights = with_norm ? cgraph->nodes[node_idx + 8] : cgraph->nodes[node_idx + 4];
9625+
ggml_tensor * ids = cgraph->nodes[node_idx + 3];
9626+
9627+
GGML_ASSERT(logits->type == GGML_TYPE_F32);
9628+
GGML_ASSERT(weights->type == GGML_TYPE_F32);
9629+
GGML_ASSERT(ids->type == GGML_TYPE_I32);
9630+
9631+
const int n_experts = logits->ne[0];
9632+
const int n_rows = logits->ne[1];
9633+
const int n_expert_used = weights->ne[1];
9634+
9635+
GGML_ASSERT(ids->nb[1] / ggml_type_size(ids->type) == (size_t) n_experts);
9636+
9637+
vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, nullptr, nullptr, nullptr, cgraph->nodes[node_idx], GGML_OP_SOFT_MAX);
9638+
9639+
if (dryrun) {
9640+
ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
9641+
return;
9642+
}
9643+
9644+
ggml_backend_vk_buffer_context * logits_buf_ctx = (ggml_backend_vk_buffer_context *)logits->buffer->context;
9645+
ggml_backend_vk_buffer_context * weights_buf_ctx = (ggml_backend_vk_buffer_context *)weights->buffer->context;
9646+
ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context;
9647+
9648+
vk_buffer d_logits = nullptr;
9649+
size_t logits_buf_offset = 0;
9650+
vk_buffer d_weights = nullptr;
9651+
size_t weights_buf_offset = 0;
9652+
vk_buffer d_ids = nullptr;
9653+
size_t ids_buf_offset = 0;
9654+
9655+
bool logits_uma = false;
9656+
bool weights_uma = false;
9657+
bool ids_uma = false;
9658+
9659+
if (ctx->device->uma) {
9660+
ggml_vk_host_get(ctx->device, logits->data, d_logits, logits_buf_offset);
9661+
ggml_vk_host_get(ctx->device, weights->data, d_weights, weights_buf_offset);
9662+
ggml_vk_host_get(ctx->device, ids->data, d_ids, ids_buf_offset);
9663+
logits_uma = d_logits != nullptr;
9664+
weights_uma = d_weights != nullptr;
9665+
ids_uma = d_ids != nullptr;
9666+
}
9667+
9668+
if (!logits_uma) {
9669+
d_logits = logits_buf_ctx->dev_buffer;
9670+
logits_buf_offset = vk_tensor_offset(logits) + logits->view_offs;
9671+
GGML_ASSERT(d_logits != nullptr);
9672+
}
9673+
if (!weights_uma) {
9674+
d_weights = weights_buf_ctx->dev_buffer;
9675+
weights_buf_offset = vk_tensor_offset(weights) + weights->view_offs;
9676+
GGML_ASSERT(d_weights != nullptr);
9677+
}
9678+
if (!ids_uma) {
9679+
d_ids = ids_buf_ctx->dev_buffer;
9680+
ids_buf_offset = vk_tensor_offset(ids) + ids->view_offs;
9681+
GGML_ASSERT(d_ids != nullptr);
9682+
}
9683+
9684+
vk_op_topk_moe_push_constants pc;
9685+
pc.n_rows = n_rows;
9686+
pc.n_expert_used = n_expert_used;
9687+
9688+
GGML_ASSERT(n_expert_used <= n_experts);
9689+
9690+
const uint32_t rows_per_block = 4;
9691+
std::array<uint32_t, 3> elements = { CEIL_DIV(n_rows, rows_per_block), 1, 1 };
9692+
9693+
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline,
9694+
{
9695+
ggml_vk_subbuffer(ctx, d_logits, logits_buf_offset),
9696+
ggml_vk_subbuffer(ctx, d_weights, weights_buf_offset),
9697+
ggml_vk_subbuffer(ctx, d_ids, ids_buf_offset),
9698+
}, pc, elements);
9699+
}
9700+
95929701
static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool backprop, bool dryrun = false) {
95939702
const int n_dims = ((int32_t *) dst->op_params)[1];
95949703
const int mode = ((int32_t *) dst->op_params)[2];
@@ -11174,11 +11283,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
1117411283
ctx->unsynced_nodes_read.clear();
1117511284
ggml_vk_sync_buffers(ctx, compute_ctx);
1117611285
}
11177-
// Add the last fused node and all fused source nodes to the unsynchronized list.
11178-
const ggml_tensor * last_node = cgraph->nodes[node_idx + ctx->num_additional_fused_ops];
11179-
ctx->unsynced_nodes_written.push_back(last_node);
11286+
// Add all fused nodes to the unsynchronized lists.
1118011287
for (int32_t i = 0; i < ctx->num_additional_fused_ops + 1; ++i) {
1118111288
const ggml_tensor *cur_node = cgraph->nodes[node_idx + i];
11289+
// Multiple outputs could be written, e.g. in topk_moe. Add them all to the list.
11290+
ctx->unsynced_nodes_written.push_back(cur_node);
1118211291
for (uint32_t j = 0; j < GGML_MAX_SRC; ++j) {
1118311292
if (!cur_node->src[j]) {
1118411293
continue;
@@ -11345,7 +11454,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
1134511454

1134611455
break;
1134711456
case GGML_OP_SOFT_MAX:
11348-
ggml_vk_soft_max(ctx, compute_ctx, src0, src1, src2, node, dryrun);
11457+
if (ctx->num_additional_fused_ops) {
11458+
ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx, dryrun);
11459+
} else {
11460+
ggml_vk_soft_max(ctx, compute_ctx, src0, src1, src2, node, dryrun);
11461+
}
1134911462

1135011463
break;
1135111464
case GGML_OP_SOFT_MAX_BACK:
@@ -12141,6 +12254,120 @@ static bool ggml_vk_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, st
1214112254
return true;
1214212255
}
1214312256

12257+
static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph,
12258+
int node_idx, bool with_norm) {
12259+
12260+
if (with_norm) {
12261+
if (node_idx + (int)topk_moe_norm.size() > cgraph->n_nodes) {
12262+
return false;
12263+
}
12264+
for (size_t i = 0; i < topk_moe_norm.size(); ++i) {
12265+
if (cgraph->nodes[node_idx + i]->op != topk_moe_norm[i]) {
12266+
return false;
12267+
}
12268+
}
12269+
} else {
12270+
if (node_idx + (int)topk_moe.size() > cgraph->n_nodes) {
12271+
return false;
12272+
}
12273+
for (size_t i = 0; i < topk_moe.size(); ++i) {
12274+
if (cgraph->nodes[node_idx + i]->op != topk_moe[i]) {
12275+
return false;
12276+
}
12277+
}
12278+
}
12279+
12280+
const ggml_tensor * softmax = cgraph->nodes[node_idx + 0];
12281+
const ggml_tensor * weights = with_norm ? cgraph->nodes[node_idx + 8] : cgraph->nodes[node_idx + 4];
12282+
12283+
const float * op_params = (const float *)softmax->op_params;
12284+
12285+
float scale = op_params[0];
12286+
float max_bias = op_params[1];
12287+
12288+
if (!ggml_is_contiguous(softmax->src[0]) || !ggml_is_contiguous(weights)) {
12289+
return false;
12290+
}
12291+
12292+
if (scale != 1.0f || max_bias != 0.0f) {
12293+
return false;
12294+
}
12295+
12296+
// don't fuse when masks or sinks are present
12297+
if (softmax->src[1] || softmax->src[2]) {
12298+
return false;
12299+
}
12300+
12301+
const int n_expert = softmax->ne[0];
12302+
// n_expert must be a power of 2
12303+
if (!is_pow2(n_expert) || n_expert > (1 << (num_topk_moe_pipelines-1))) {
12304+
return false;
12305+
}
12306+
12307+
// Check that the nodes don't have any unexpected uses
12308+
const ggml_tensor * reshape1 = cgraph->nodes[node_idx + 1];
12309+
const ggml_tensor * argsort = cgraph->nodes[node_idx + 2];
12310+
const ggml_tensor * view = cgraph->nodes[node_idx + 3];
12311+
const ggml_tensor * get_rows = cgraph->nodes[node_idx + 4];
12312+
const ggml_tensor * reshape5 = with_norm ? cgraph->nodes[node_idx + 5] : nullptr;
12313+
const ggml_tensor * sum_rows = with_norm ? cgraph->nodes[node_idx + 6] : nullptr;
12314+
const ggml_tensor * div = with_norm ? cgraph->nodes[node_idx + 7] : nullptr;
12315+
const ggml_tensor * reshape8 = with_norm ? cgraph->nodes[node_idx + 8] : nullptr;
12316+
12317+
// softmax is used by reshape and argsort
12318+
if (ggml_node_get_use_count(cgraph, node_idx) != 2 ||
12319+
reshape1->src[0] != softmax ||
12320+
argsort->src[0] != softmax) {
12321+
return false;
12322+
}
12323+
// reshape is used by get_rows
12324+
if (ggml_node_get_use_count(cgraph, node_idx + 1) != 1 ||
12325+
get_rows->src[0] != reshape1) {
12326+
return false;
12327+
}
12328+
// argsort is used by view
12329+
if (ggml_node_get_use_count(cgraph, node_idx + 2) != 1 ||
12330+
view->src[0] != argsort) {
12331+
return false;
12332+
}
12333+
// view is written (via argsort), we can skip checking it
12334+
12335+
if (with_norm) {
12336+
// get_rows is used by reshape
12337+
if (ggml_node_get_use_count(cgraph, node_idx + 4) != 1 ||
12338+
reshape5->src[0] != get_rows) {
12339+
return false;
12340+
}
12341+
12342+
// reshape is used by sum_rows and div
12343+
if (ggml_node_get_use_count(cgraph, node_idx + 5) != 2 ||
12344+
sum_rows->src[0] != reshape5 ||
12345+
div->src[0] != reshape5) {
12346+
return false;
12347+
}
12348+
12349+
// sum_rows is used by div
12350+
if (ggml_node_get_use_count(cgraph, node_idx + 6) != 1 ||
12351+
div->src[1] != sum_rows) {
12352+
return false;
12353+
}
12354+
12355+
// div/reshape are written
12356+
if (reshape8->src[0] != div) {
12357+
return false;
12358+
}
12359+
}
12360+
12361+
if (!ctx->device->subgroup_arithmetic ||
12362+
!ctx->device->subgroup_shuffle ||
12363+
!ctx->device->subgroup_require_full_support ||
12364+
ctx->device->disable_fusion) {
12365+
return false;
12366+
}
12367+
12368+
return true;
12369+
}
12370+
1214412371
static uint32_t ggml_vk_fuse_multi_add(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx) {
1214512372

1214612373
const ggml_tensor *first_node = cgraph->nodes[node_idx];
@@ -12216,6 +12443,10 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
1221612443
ctx->num_additional_fused_ops = num_adds - 1;
1221712444
} else if (ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
1221812445
ctx->num_additional_fused_ops = 1;
12446+
} else if (ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, true)) {
12447+
ctx->num_additional_fused_ops = topk_moe_norm.size() - 1;
12448+
} else if (ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, false)) {
12449+
ctx->num_additional_fused_ops = topk_moe.size() - 1;
1221912450
}
1222012451
}
1222112452
ggml_vk_build_graph(ctx, cgraph, i, nullptr, 0, true, false, false, false);
@@ -12313,17 +12544,21 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
1231312544
ctx->num_additional_fused_ops = num_adds - 1;
1231412545
} else if (ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
1231512546
ctx->num_additional_fused_ops = 1;
12547+
} else if (ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, true)) {
12548+
ctx->num_additional_fused_ops = topk_moe_norm.size() - 1;
12549+
} else if (ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, false)) {
12550+
ctx->num_additional_fused_ops = topk_moe.size() - 1;
1231612551
}
1231712552
}
1231812553

1231912554
// Signal the almost_ready fence when the graph is mostly complete (< 20% remaining)
1232012555
bool almost_ready = (cgraph->n_nodes - i) < cgraph->n_nodes / 5;
1232112556
bool submit = (submitted_nodes >= nodes_per_submit) ||
1232212557
(mul_mat_bytes >= mul_mat_bytes_per_submit) ||
12323-
(i + ctx->num_additional_fused_ops == last_node) ||
12558+
(i + ctx->num_additional_fused_ops >= last_node) ||
1232412559
(almost_ready && !ctx->almost_ready_fence_pending);
1232512560

12326-
bool enqueued = ggml_vk_build_graph(ctx, cgraph, i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i + ctx->num_additional_fused_ops == last_node, almost_ready, submit);
12561+
bool enqueued = ggml_vk_build_graph(ctx, cgraph, i, cgraph->nodes[submit_node_idx], submit_node_idx, false, i + ctx->num_additional_fused_ops >= last_node, almost_ready, submit);
1232712562

1232812563
if (vk_perf_logger_enabled) {
1232912564
if (ctx->compute_ctx.expired()) {
@@ -12444,6 +12679,25 @@ static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph *
1244412679
while (first_unused < graph->n_nodes) {
1244512680
std::vector<int> current_set;
1244612681

12682+
// Avoid reordering topk_moe_norm
12683+
if (first_unused + (int)topk_moe_norm.size() <= graph->n_nodes) {
12684+
bool is_topk_moe_norm = true;
12685+
for (size_t j = 0; j < topk_moe_norm.size(); ++j) {
12686+
if (graph->nodes[first_unused + j]->op != topk_moe_norm[j] || used[first_unused + j]) {
12687+
is_topk_moe_norm = false;
12688+
}
12689+
}
12690+
if (is_topk_moe_norm) {
12691+
for (size_t j = 0; j < topk_moe_norm.size(); ++j) {
12692+
new_order.push_back(graph->nodes[first_unused + j]);
12693+
used[first_unused + j] = true;
12694+
}
12695+
while (first_unused < graph->n_nodes && used[first_unused]) {
12696+
first_unused++;
12697+
}
12698+
continue;
12699+
}
12700+
}
1244712701
// First, grab the next unused node.
1244812702
current_set.push_back(first_unused);
1244912703

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