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Fused FFN_UP+FFN_GATE op (#741) 

* Fused up+gate+unary for regular (not MoE) FFN - CPU

* WIP CUDA

* Seems to be working on CUDA

For a dense model we get 2-3% speedup for PP and ~0.6% for TG.

* Add command line option

This time the option is ON by default, and one needs to turn it
off via -no-fug or --no-fused-up-gate

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow 2025-08-31 18:16:36 +03:00 committed by GitHub
parent d55e98519f
commit 8de297b795
10 changed files with 276 additions and 12 deletions
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7
common/common.cpp
View File

@ -1004,6 +1004,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.fused_moe_up_gate = true;
return true;
}
if (arg == "-no-fug" || arg == "--no-fused-up-gate") {
params.fused_up_gate = false;
return true;
}
if (arg == "-ser" || arg == "--smart-expert-reduction") {
CHECK_ARG
auto values = string_split_pairs<int,float>(argv[i], ',');
@ -1760,6 +1764,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
options.push_back({ "*", "-mla, --mla-use", "enable MLA (default: %d)", params.mla_attn });
options.push_back({ "*", "-amb, --attention-max-batch", "max batch size for attention computations (default: %d)", params.attn_max_batch});
options.push_back({ "*", "-fmoe, --fused-moe", "enable fused MoE (default: %s)", params.fused_moe_up_gate ? "enabled" : "disabled" });
options.push_back({ "*", "-no-fug, --no-fused-up-gate", "disaable fused up-gate (default: %s)", params.fused_up_gate ? "enabled" : "disabled" });
options.push_back({ "*", "-ser, --smart-expert-reduction,","experts reduction (default: %d,%g)", params.min_experts, params.thresh_experts});
options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with\n"
"in conversation mode, this will be used as system prompt\n"
@ -2660,6 +2665,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.mla_attn = params.mla_attn;
cparams.attn_max_batch = params.attn_max_batch;
cparams.fused_moe_up_gate = params.fused_moe_up_gate;
cparams.fused_up_gate = params.fused_up_gate;
cparams.min_experts = params.min_experts;
cparams.thresh_experts = params.thresh_experts;
@ -3756,6 +3762,7 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
fprintf(stream, "mla_attn: %d # default: 0\n", params.mla_attn);
fprintf(stream, "attn_max_batch: %d # default: 0\n", params.attn_max_batch);
fprintf(stream, "fused_moe: %s # default: false\n", params.fused_moe_up_gate ? "true" : "false");
fprintf(stream, "fused_up_gate: %s # default: true\n", params.fused_up_gate ? "true" : "false");
fprintf(stream, "ser: %d,%g # defaulr: -1,0\n", params.min_experts, params.thresh_experts);
fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);

1
common/common.h
View File

@ -191,6 +191,7 @@ struct gpt_params {
int mla_attn = 0; // MLA 0: standard attention, 1: MLA with K and transposed V cache, 2: MLA with just K cache
int attn_max_batch = 0; // Max batch size to use when computing attention (only applicable if flash_attn = false)
bool fused_moe_up_gate = false; // fused up*unary(gate) op for MoE models
bool fused_up_gate = true; // fused up*unary(gate) op
int min_experts = -1;
float thresh_experts = 0;

33
examples/llama-bench/llama-bench.cpp
View File

@ -261,6 +261,7 @@ struct cmd_params {
bool warmup;
bool repack = false;
bool fmoe = false;
bool no_fug = false;
bool use_thp = false;
output_formats output_format;
output_formats output_format_stderr;
@ -297,6 +298,7 @@ static const cmd_params cmd_params_defaults = {
/* repack */ false,
/* use_thp */ false,
/* fmoe */ false,
/* no_fug */ false,
/* output_format */ MARKDOWN,
/* output_format_stderr */ NONE,
};
@ -339,6 +341,7 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -thp, --transparent-huge-pages <0|1> (default: %s)\n", cmd_params_defaults.use_thp? "1" : "0");
printf(" -ot, --override-tensor pattern (default: none)\n");
printf(" -fmoe, --fused-moe <0|1> (default: %s)\n", cmd_params_defaults.fmoe? "1" : "0");
printf(" -no-fug, --no-fused-up-gate <0|1> (default: %s)\n", cmd_params_defaults.no_fug? "1" : "0");
printf("\n");
printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
}
@ -736,6 +739,12 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
break;
}
params.fmoe = std::stoi(argv[i]);
} else if (arg == "-no-fug" || arg == "--no-fused-up-gate") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.no_fug = std::stoi(argv[i]);
} else if (arg == "-ot" || arg == "--override-tensor") {
if (++i >= argc) {
invalid_param = true;
@ -820,6 +829,7 @@ struct cmd_params_instance {
bool embeddings;
bool repack = false;
bool fmoe = false;
bool no_fug = false;
bool use_thp = false;
const llama_model_tensor_buft_override* buft_overrides;
@ -866,6 +876,7 @@ struct cmd_params_instance {
cparams.mla_attn = mla_attn;
cparams.attn_max_batch = attn_max_batch;
cparams.fused_moe_up_gate = fmoe;
cparams.fused_up_gate = !no_fug;
cparams.min_experts = ser.first;
cparams.thresh_experts = ser.second;
cparams.embeddings = embeddings;
@ -924,6 +935,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .embeddings = */ embd,
/* .repack = */ params.repack,
/* .fmoe = */ params.fmoe,
/* .no_fug = */ params.no_fug,
/* .use_thp = */ params.use_thp,
/* .buft_overrides=*/ params.buft_overrides.data(),
};
@ -958,6 +970,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .embeddings = */ embd,
/* .repack = */ params.repack,
/* .fmoe = */ params.fmoe,
/* .no_fug = */ params.no_fug,
/* .use_thp = */ params.use_thp,
/* .buft_overrides=*/ params.buft_overrides.data(),
};
@ -992,6 +1005,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .embeddings = */ embd,
/* .repack = */ params.repack,
/* .fmoe = */ params.fmoe,
/* .no_fug = */ params.no_fug,
/* .use_thp = */ params.use_thp,
/* .buft_overrides=*/ params.buft_overrides.data(),
};
@ -1026,6 +1040,7 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .embeddings = */ embd,
/* .repack = */ params.repack,
/* .fmoe = */ params.fmoe,
/* .no_fug = */ params.no_fug,
/* .use_thp = */ params.use_thp,
/* .buft_overrides=*/ params.buft_overrides.data(),
};
@ -1071,6 +1086,7 @@ struct test {
bool embeddings;
bool repack = false;
bool fmoe = false;
bool no_fug = false;
bool use_thp = false;
int n_prompt;
int n_gen;
@ -1104,7 +1120,7 @@ struct test {
use_mmap = inst.use_mmap;
embeddings = inst.embeddings;
repack = inst.repack;
fmoe = inst.fmoe;
no_fug = inst.no_fug;
use_thp = inst.use_thp;
n_prompt = inst.n_prompt;
n_gen = inst.n_gen;
@ -1196,7 +1212,7 @@ struct test {
"n_threads", "type_k", "type_v",
"n_gpu_layers", "split_mode",
"main_gpu", "no_kv_offload", "flash_attn", "mla_attn", "attn_max_batch", "ser",
"tensor_split", "use_mmap", "embeddings", "repack", "fused_moe", "use_thp",
"tensor_split", "use_mmap", "embeddings", "repack", "fused_moe", "fused_up_gate", "use_thp",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
"avg_ts", "stddev_ts", "test",
@ -1218,7 +1234,7 @@ struct test {
if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" ||
field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
field == "flash_attn" || field == "use_mmap" || field == "embeddings" || field == "repack" || field == "use_thp" ||
field == "fused_moe") {
field == "fused_moe" || field == "fused_up_gate") {
return BOOL;
}
if (field == "avg_ts" || field == "stddev_ts") {
@ -1261,7 +1277,7 @@ struct test {
std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn),
std::to_string(mla_attn), std::to_string(attn_max_batch), ser_to_string(ser),
tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
std::to_string(repack), std::to_string(fmoe), std::to_string(use_thp),
std::to_string(repack), std::to_string(fmoe), std::to_string(no_fug), std::to_string(use_thp),
std::to_string(n_prompt), std::to_string(n_gen), test_time,
std::to_string(avg_ns()), std::to_string(stdev_ns()),
std::to_string(avg_ts()), std::to_string(stdev_ts()),
@ -1445,6 +1461,9 @@ struct markdown_printer : public printer {
if (field == "fused_moe") {
return 4;
}
if (field == "fused_up_gate") {
return 6;
}
if (field == "test") {
return 13;
}
@ -1494,6 +1513,9 @@ struct markdown_printer : public printer {
if (field == "fused_moe") {
return "fmoe";
}
if (field == "fused_up_gate") {
return "no-fug";
}
if (field == "embeddings") {
return "embd";
}
@ -1567,6 +1589,9 @@ struct markdown_printer : public printer {
if (params.fmoe != cmd_params_defaults.fmoe) {
fields.emplace_back("fused_moe");
}
if (params.no_fug != cmd_params_defaults.no_fug) {
fields.emplace_back("fused_up_gate");
}
fields.emplace_back("test");
fields.emplace_back("t/s");

8
ggml/include/ggml.h
View File

@ -611,6 +611,7 @@ extern "C" {
GGML_OP_MUL_MAT,
GGML_OP_MUL_MAT_ID,
GGML_OP_OUT_PROD,
GGML_OP_FUSED_UP_GATE,
GGML_OP_MOE_FUSED_UP_GATE,
GGML_OP_SCALE,
@ -1408,6 +1409,13 @@ extern "C" {
struct ggml_tensor * a_gate_b,
enum ggml_unary_op op);
GGML_API struct ggml_tensor * ggml_fused_up_gate(
struct ggml_context * ctx,
struct ggml_tensor * up,
struct ggml_tensor * gate,
struct ggml_tensor * b,
enum ggml_unary_op op);
// A: m columns, n rows,
// B: p columns, n rows,
// result is m columns, p rows

67
ggml/src/ggml-cuda.cu
View File

@ -2521,7 +2521,7 @@ static bool ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
return false;
}
static bool ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * next) {
static bool ggml_cuda_moe_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * next) {
const ggml_tensor * src0_1 = dst->src[0];
const ggml_tensor * src0_2 = dst->src[1];
const ggml_tensor * src0 = src0_1;
@ -2972,6 +2972,60 @@ static bool ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor
return fuse_down;
}
static void ggml_cuda_up_gate_unary(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0_1 = dst->src[0];
const ggml_tensor * src0_2 = dst->src[1];
const ggml_tensor * src1 = dst->src[2];
GGML_ASSERT(ggml_is_quantized(src0_1->type));
GGML_ASSERT(src0_1->type == src0_2->type);
GGML_ASSERT(src1->ne[2] == 1);
GGML_ASSERT(src1->ne[3] == 1);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0_1->buffer));
GGML_ASSERT(!ggml_backend_buffer_is_cuda_split(src0_2->buffer));
auto stream = ctx.stream();
auto ne10_padded = GGML_PAD(src1->ne[0], MATRIX_ROW_PADDING);
auto nb10_padded = ne10_padded*sizeof(block_q8_1)/QK8_1;
auto quantized_size = nb10_padded*src1->ne[1];
if (src1->ne[1] > 8) {
quantized_size += get_mmq_x_max_host(ggml_cuda_info().devices[ctx.device].cc)*sizeof(block_q8_1_mmq);
}
ggml_cuda_pool_alloc<float> dst_up(ctx.pool(), ggml_nelements(dst));
ggml_cuda_pool_alloc<char> src1_quantized(ctx.pool(), quantized_size);
if (src1->ne[1] <= 8) {
quantize_row_q8_1_cuda((const float *)src1->data, (void *)src1_quantized.get(), src1->ne[0], src1->ne[1], 1, ne10_padded,
src0_1->type, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_vec_q(ctx, src0_1, src1, dst, (const char *)src0_1->data, nullptr, src1_quantized.get(), dst_up.get(),
0, src0_1->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_vec_q(ctx, src0_2, src1, dst, (const char *)src0_2->data, nullptr, src1_quantized.get(), (float *)dst->data,
0, src0_2->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
} else {
quantize_mmq_q8_1_cuda((const float *)src1->data, src1_quantized.get(), src1->ne[0], src1->ne[1], 1, ne10_padded, src0_1->type, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_q(ctx, src0_1, src1, dst, (const char *)src0_1->data, nullptr, src1_quantized.get(), dst_up.get(),
0, src0_1->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
ggml_cuda_op_mul_mat_q(ctx, src0_2, src1, dst, (const char *)src0_2->data, nullptr, src1_quantized.get(), (float *)dst->data,
0, src0_1->ne[1], src1->ne[1], ne10_padded, stream);
CUDA_CHECK(cudaGetLastError());
}
ggml_fused_mul_unary(ctx, (ggml_unary_op)dst->op_params[0], ggml_nelements(dst),
(const float *)dst->data, dst_up.get(), (float *)dst->data);
CUDA_CHECK(cudaGetLastError());
}
static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct ggml_tensor * dst, struct ggml_tensor * next, bool& skip_next) {
// why is this here instead of mul_mat?
if (dst->src[0] != nullptr && ggml_backend_buffer_is_cuda_split(dst->src[0]->buffer)) {
@ -3097,7 +3151,10 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
skip_next = ggml_cuda_mul_mat_id(ctx, dst, next);
break;
case GGML_OP_MOE_FUSED_UP_GATE:
skip_next = ggml_cuda_up_gate_unary(ctx, dst, next);
skip_next = ggml_cuda_moe_up_gate_unary(ctx, dst, next);
break;
case GGML_OP_FUSED_UP_GATE:
ggml_cuda_up_gate_unary(ctx, dst);
break;
case GGML_OP_SCALE:
ggml_cuda_op_scale(ctx, dst);
@ -3950,10 +4007,12 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
case GGML_OP_MOE_FUSED_UP_GATE:
case GGML_OP_FUSED_UP_GATE:
{
bool is_fused_up_gate = op->op == GGML_OP_MOE_FUSED_UP_GATE || op->op == GGML_OP_FUSED_UP_GATE;
struct ggml_tensor * a = op->src[0];
struct ggml_tensor * b = op->op == GGML_OP_MOE_FUSED_UP_GATE ? op->src[2] : op->src[1];
if (op->op == GGML_OP_MOE_FUSED_UP_GATE && a->type != op->src[1]->type) {
struct ggml_tensor * b = is_fused_up_gate ? op->src[2] : op->src[1];
if (is_fused_up_gate && a->type != op->src[1]->type) {
printf("%s: returning false for GGML_OP_MOE_FUSED_UP_GATE because src0->type != src1->type\n", __func__);
return false;
}

1
ggml/src/ggml-cuda/mmq.cuh
View File

@ -108,6 +108,7 @@ static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
case GGML_TYPE_IQ4_KT:
return MMQ_Q8_1_DS_LAYOUT_D4;
default:
fprintf(stderr, "Unhandled type %s (%d)\n", ggml_type_name(type_x), type_x);
GGML_ABORT("fatal error");
break;
}

132
ggml/src/ggml.c
View File

@ -4054,6 +4054,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"MUL_MAT",
"MUL_MAT_ID",
"OUT_PROD",
"FUSED_UP_GATE",
"MOE_FUSED_UP_GATE",
"SCALE",
@ -4115,7 +4116,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS_BACK",
};
static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 82");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -4151,6 +4152,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"X[i]*Y",
"X*Y",
"X*Y1&X*Y2",
"X*Y1&X*Y2",
"x*v",
"y-\\>view(x)",
@ -4211,7 +4213,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss_back(x,y)",
};
static_assert(GGML_OP_COUNT == 82, "GGML_OP_COUNT != 82");
static_assert(GGML_OP_COUNT == 83, "GGML_OP_COUNT != 82");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
@ -7162,6 +7164,44 @@ struct ggml_tensor * ggml_moe_up_gate_ext(
return result;
}
struct ggml_tensor * ggml_fused_up_gate(
struct ggml_context * ctx,
struct ggml_tensor * up,
struct ggml_tensor * gate,
struct ggml_tensor * b,
enum ggml_unary_op op) {
if (!ggml_is_quantized(up->type) || up->type != gate->type || !ggml_are_same_shape(up, gate)) {
struct ggml_tensor * result_up = ggml_mul_mat(ctx, up, b);
struct ggml_tensor * result_gate = ggml_mul_mat(ctx, gate, b);
return ggml_fused_mul_unary(ctx, result_gate, result_up, op);
}
GGML_ASSERT(!ggml_is_transposed(up));
GGML_ASSERT(!ggml_is_transposed(gate));
GGML_ASSERT(up->ne[2] == 1); // as is 3d (one matrix per expert)
GGML_ASSERT(up->ne[3] == 1); // as is 3d (one matrix per expert)
GGML_ASSERT(b->ne[2] == 1); // b is 3d
GGML_ASSERT(b->ne[3] == 1); // b is 3d
GGML_ASSERT(up->ne[0] == b->ne[0]); // can_mul_mat
const bool is_node = false;
const int64_t ne[4] = { up->ne[1], b->ne[1], 1, 1 };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
result->op = GGML_OP_FUSED_UP_GATE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = up;
result->src[1] = gate;
result->src[2] = b;
result->src[3] = NULL;
result->src[4] = NULL;
ggml_set_op_params_i32(result, 0, (int32_t) op);
return result;
}
// ggml_out_prod
@ -15667,6 +15707,75 @@ static void ggml_compute_forward_mul_mat_id_up_gate(
#undef MMID_MATRIX_ROW
}
static void ggml_compute_forward_mul_mat_up_gate(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
GGML_ASSERT(dst->src[0]->type == dst->src[1]->type);
GGML_ASSERT(ggml_are_same_shape(dst->src[0], dst->src[1]));
GGML_ASSERT(dst->type == GGML_TYPE_F32);
const struct ggml_tensor * src1 = dst->src[2];
const struct ggml_tensor * src0_1 = dst->src[0];
const struct ggml_tensor * src0_2 = dst->src[1];
const struct ggml_tensor * src0 = src0_1; // so GGML_TENSOR_BINARY_OP_LOCALS works
GGML_ASSERT(ggml_is_quantized(src0_1->type) && src0_1->type == src0_2->type);
GGML_TENSOR_BINARY_OP_LOCALS
const int ith = params->ith;
const int nth = params->nth;
const enum ggml_type type = src0->type;
enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type;
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
GGML_ASSERT(ne13 == 1);
ggml_from_float_t const from_float = type_traits[vec_dot_type].from_float;
char * wdata = params->wdata;
const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
const size_t nbw2 = nbw1*ne11;
const size_t nbw3 = nbw2*ne12;
assert(params->wsize >= ne13*nbw3);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = ith; i11 < ne11; i11 += nth) {
from_float((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11),
(void *) (wdata + i13*nbw3 + i12*nbw2 + i11*nbw1),
ne10);
}
}
}
ggml_barrier(params->shared);
const size_t row_size = ggml_row_size(vec_dot_type, ne10);
if (!iqk_moe_fused_up_gate(ne01, ne11, ne00, ne11, dst->op_params[0],
type, src0_1->data, src0_2->data, nb01,
vec_dot_type, (const char *)wdata, row_size,
NULL, NULL,
(float *)dst->data, nb1, nb2,
NULL, ith, nth)) GGML_ABORT("fatal error");
}
#endif
// ggml_compute_forward_out_prod
@ -20403,6 +20512,10 @@ static bool ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_mul_mat_id_up_gate(params, tensor);
} break;
case GGML_OP_FUSED_UP_GATE:
{
ggml_compute_forward_mul_mat_up_gate(params, tensor);
} break;
case GGML_OP_OUT_PROD:
{
ggml_compute_forward_out_prod(params, tensor);
@ -21172,6 +21285,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ABORT("fatal error"); // TODO: not implemented
}
case GGML_OP_FUSED_UP_GATE:
{
GGML_ABORT("fatal error"); // TODO: not implemented
}
case GGML_OP_OUT_PROD:
{
GGML_ABORT("fatal error"); // TODO: not implemented
@ -22189,6 +22306,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
case GGML_OP_MOE_FUSED_UP_GATE:
case GGML_OP_FUSED_UP_GATE:
case GGML_OP_OUT_PROD:
{
n_tasks = n_threads;
@ -22411,6 +22529,16 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
cur += n_as * sizeof(int64_t); // matrix_row_counts
cur += n_as * src2->ne[2] * sizeof(int64_t); // matrix_rows
} break;
case GGML_OP_FUSED_UP_GATE:
{
cur = 0;
const struct ggml_tensor * src0 = node->src[0];
const struct ggml_tensor * src2 = node->src[2];
const enum ggml_type vec_dot_type = type_traits[src0->type].vec_dot_type;
if (src2->type != vec_dot_type) {
cur += ggml_row_size(vec_dot_type, node->src[1]->ne[0]) * ggml_nrows(node->src[1]);
}
} break;
case GGML_OP_OUT_PROD:
{
if (ggml_is_quantized(node->src[0]->type)) {

2
ggml/src/iqk/iqk_mul_mat.cpp
View File

@ -739,7 +739,7 @@ extern "C" IQK_API bool iqk_moe_fused_up_gate(long Nx, long Ny, long ne00, int n
float * C, long nb1, long nb2, const void * vrow_mapping, int ith, int nth) {
const mmid_row_mapping * row_mapping = (const mmid_row_mapping *)vrow_mapping;
assert(row_mapping != nullptr);
//assert(row_mapping != nullptr);
MulMat mm;

3
include/llama.h
View File

@ -419,7 +419,8 @@ extern "C" {
bool flash_attn; // whether to use flash attention [EXPERIMENTAL]
int mla_attn; // whether to use MLA attention [EXPERIMENTAL]
int attn_max_batch; // maximum batch size for attention computations [EXPERIMENTAL]
bool fused_moe_up_gate; // whether to use fused MoE up/down op [EXPERIMENTAL]
bool fused_moe_up_gate; // whether to use fused MoE up/gate op
bool fused_up_gate; // whether to use fused up/gate op [EXPERIMENTAL]
int min_experts;
float thresh_experts;

34
src/llama.cpp
View File

@ -2072,6 +2072,7 @@ struct llama_cparams {
int mla_attn;
int attn_max_batch;
bool fused_moe_up_gate;
bool fused_up_gate;
int min_experts;
float thresh_experts;
@ -7612,6 +7613,34 @@ static struct ggml_tensor * llm_build_ffn(
llm_ffn_gate_type type_gate,
const llm_build_cb & cb,
int il) {
if (lctx.cparams.fused_up_gate &&
up && gate && !up_b && !up_s && !gate_b && !gate_s && type_gate == LLM_FFN_PAR &&
(type_op == LLM_FFN_SILU || type_op == LLM_FFN_RELU || (type_op == LLM_FFN_GELU && !act_scales))) {
auto unary_op = type_op == LLM_FFN_SILU ? GGML_UNARY_OP_SILU :
type_op == LLM_FFN_RELU ? GGML_UNARY_OP_RELU : GGML_UNARY_OP_GELU;
cur = ggml_fused_up_gate(ctx, up, gate, cur, unary_op);
cb(cur, "ffn_up_gate", il);
if (down) {
cur = llm_build_lora_mm(lctx, ctx, down, cur);
if (lctx.model.arch == LLM_ARCH_GLM4 || lctx.model.arch == LLM_ARCH_GLM4_MOE) {
// GLM4 and GLM4_MOE seem to have numerical issues with half-precision accumulators
ggml_mul_mat_set_prec(cur, GGML_PREC_F32);
}
}
if (down_b) {
cb(cur, "ffn_down", il);
}
if (down_b) {
cur = ggml_add(ctx, cur, down_b);
}
if (down_s) {
cur = ggml_mul(ctx, cur, down_s);
cb(cur, "ffn_down_s", il);
}
return cur;
}
struct ggml_tensor * tmp = up ? llm_build_lora_mm(lctx, ctx, up, cur) : cur;
cb(tmp, "ffn_up", il);
@ -8223,6 +8252,7 @@ struct llm_build_context {
const int mla_attn;
const int attn_max_batch;
const bool fused_moe_up_gate;
const bool fused_up_gate;
const int min_experts;
const float thresh_experts;
@ -8278,6 +8308,7 @@ struct llm_build_context {
mla_attn (cparams.mla_attn),
attn_max_batch (cparams.attn_max_batch),
fused_moe_up_gate(cparams.fused_moe_up_gate),
fused_up_gate (cparams.fused_up_gate),
min_experts (cparams.min_experts),
thresh_experts (cparams.thresh_experts),
pooling_type (cparams.pooling_type),
@ -18923,6 +18954,7 @@ struct llama_context_params llama_context_default_params() {
/*.mla_attn =*/ 0,
/*.attn_max_batch =*/ 0,
/*.fused_moe_up_gate =*/ false,
/*.fused_up_gate =*/ true,
/*.min_experts =*/ -1,
/*.thtesh_experts =*/ 0.0f,
/*.abort_callback =*/ nullptr,
@ -19130,6 +19162,7 @@ struct llama_context * llama_new_context_with_model(
cparams.mla_attn = params.mla_attn;
cparams.attn_max_batch = params.attn_max_batch;
cparams.fused_moe_up_gate= params.fused_moe_up_gate;
cparams.fused_up_gate = params.fused_up_gate;
cparams.min_experts = params.min_experts;
cparams.thresh_experts = params.thresh_experts;
@ -19209,6 +19242,7 @@ struct llama_context * llama_new_context_with_model(
LLAMA_LOG_INFO("%s: mla_attn = %d\n", __func__, cparams.mla_attn);
LLAMA_LOG_INFO("%s: attn_max_b = %d\n", __func__, cparams.attn_max_batch);
LLAMA_LOG_INFO("%s: fused_moe = %d\n", __func__, cparams.fused_moe_up_gate);
LLAMA_LOG_INFO("%s: fused_up_gate = %d\n", __func__, cparams.fused_up_gate);
LLAMA_LOG_INFO("%s: ser = %d, %g\n", __func__, cparams.min_experts, cparams.thresh_experts);
LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);