ikawrakow/ik_llama.cpp
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Vulkan: Disable multi-add for now (#581)

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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2025-07-03 18:31:48 +02:00
committed by GitHub
parent 8a0c38f496
commit 3e024de1da
2 changed files with 63 additions and 29 deletions
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44
ggml/src/ggml-vulkan.cpp
View File

@@ -5520,6 +5520,9 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
const uint64_t nei0 = ids->ne[0];
const uint64_t nei1 = ids->ne[1];
if (nei0*nei1 > 4096) {
fprintf(stderr, "%s: nei0 = %d, nei1 = %d\n", __func__, (int)nei0, (int)nei1);
}
GGML_ASSERT(nei0 * nei1 <= 4096);
const uint32_t nbi1 = ids->nb[1];
@@ -5915,7 +5918,30 @@ static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx
if (src2->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
} else {
ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
// Split based on number of ids, to fit in shared memory
const uint32_t nei0 = (uint32_t)src2->ne[0];
const uint32_t nei1 = (uint32_t)src2->ne[1];
GGML_ASSERT(nei0 <= 4096);
const uint32_t split_size = std::min(nei1, 4096u / nei0);
ggml_tensor src1_copy = *src1;
ggml_tensor src2_copy = *src2;
ggml_tensor dst_copy = *dst;
for (uint32_t token_start = 0; token_start < nei1; token_start += split_size) {
const uint32_t n_tokens = std::min(split_size, nei1 - token_start);
src1_copy.view_offs = src1->view_offs + token_start * src1_copy.nb[2];
src2_copy.view_offs = src2->view_offs + token_start * src2_copy.nb[1];
dst_copy.view_offs = dst->view_offs + token_start * dst_copy.nb[2];
src1_copy.ne[2] = n_tokens;
src2_copy.ne[1] = n_tokens;
dst_copy.ne[2] = n_tokens;
ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, &src1_copy, &src2_copy, &dst_copy, dryrun);
}
}
}
@@ -9510,9 +9536,15 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
ggml_type src0_type = op->src[0]->type;
ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
const vk_device& device = ctx->device;
if (op->op == GGML_OP_MUL_MAT_ID && !device->mul_mat_id_s[src0_type] && !device->mul_mat_id_m[src0_type] && !device->mul_mat_id_l[src0_type]) {
// If there's not enough shared memory for row_ids and the result tile, fallback to CPU
return false;
if (op->op == GGML_OP_MUL_MAT_ID) {
if (!device->mul_mat_id_s[src0_type] && !device->mul_mat_id_m[src0_type] && !device->mul_mat_id_l[src0_type]) {
// If there's not enough shared memory for row_ids and the result tile, fallback to CPU
return false;
}
// Check against size of shared memory variable
if (op->src[2]->ne[0] > 4096) {
return false;
}
}
switch (src0_type) {
case GGML_TYPE_F32:
@@ -9580,6 +9612,10 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
default:
return false;
}
if (op->src[1]->ne[0] != op->src[2]->ne[0]) {
// different head sizes of K and V are not supported yet
return false;
}
if (op->src[0]->type != GGML_TYPE_F32) {
return false;
}

48
src/llama.cpp
View File

@@ -9870,6 +9870,28 @@ llm_expert_gating_func_type gating_op,
cb(cur, "ffn_moe_weighted", il);
}
#ifdef GGML_USE_VULKAN
// aggregate experts
ggml_tensor * moe_out = nullptr;
//ggml_tensor * first_expert = nullptr;
for (int i = 0; i < n_expert_used; ++i) {
ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
experts->nb[2], i*experts->nb[1]);
if (i == 0) {
moe_out = cur_expert;
} else {
moe_out = ggml_add(ctx, moe_out, cur_expert);
}
}
if (n_expert_used == 1) {
// avoid returning a non-contiguous tensor
moe_out = ggml_cont(ctx, moe_out);
}
return moe_out;
#else
if (n_expert_used == 1) {
return ggml_cont(ctx, ggml_view_2d(ctx, experts, n_embd, n_tokens, experts->nb[2], 0));
}
@@ -9878,32 +9900,8 @@ llm_expert_gating_func_type gating_op,
ggml_view_2d(ctx, experts, n_embd, n_tokens, experts->nb[2], experts->nb[1]));
}
return ggml_multi_add(ctx, ggml_view_2d(ctx, experts, n_embd, n_tokens, experts->nb[2], 0), n_expert_used);
#endif
//// aggregate experts
//ggml_tensor * moe_out = nullptr;
////ggml_tensor * first_expert = nullptr;
//for (int i = 0; i < n_expert_used; ++i) {
// ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
// experts->nb[2], i*experts->nb[1]);
// if (i == 0) {
// moe_out = cur_expert;
// //first_expert = cur_expert;
// //printf("%s: %d: %d x %d x %d x %d | %d x %d x %d x %d\n", __func__, ggml_is_contiguous(first_expert),
// // (int)cur_expert->ne[0], (int)cur_expert->ne[1], (int)cur_expert->ne[2], (int)cur_expert->ne[3],
// // (int)cur_expert->nb[0], (int)cur_expert->nb[1], (int)cur_expert->nb[2], (int)cur_expert->nb[3]);
// } else {
// moe_out = ggml_add(ctx, moe_out, cur_expert);
// //printf("%s: %d %d\n", __func__, ggml_is_contiguous(cur_expert), ggml_are_same_shape(cur_expert, first_expert));
// }
//}
//if (n_expert_used == 1) {
// // avoid returning a non-contiguous tensor
// moe_out = ggml_cont(ctx, moe_out);
//}
//return moe_out;
}
static struct ggml_tensor * llm_build_kqv(