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llama: Fused QKV multiplication #16813

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2 changes: 2 additions & 0 deletions src/llama-arch.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -757,6 +757,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
},
},
{
Expand All @@ -777,6 +778,7 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
{ LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
{ LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
{ LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
{ LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
},
},
{
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69 changes: 69 additions & 0 deletions src/llama-graph.cpp
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Expand Up @@ -3,6 +3,7 @@
#include "llama-impl.h"
#include "llama-batch.h"
#include "llama-cparams.h"
#include "llama-model.h"

#include "llama-kv-cache.h"
#include "llama-kv-cache-iswa.h"
Expand Down Expand Up @@ -632,6 +633,74 @@ ggml_tensor * llm_graph_context::build_lora_mm(
return res;
}

static bool disable_fusion() {
const char * disable_fusion = getenv("LLAMA_GRAPH_DISABLE_FUSION");
return disable_fusion != nullptr && atoi(disable_fusion) != 0;
}


void llm_graph_context::build_qkv(const llama_layer & layer,
ggml_tensor * cur,
int64_t n_embd_head_q,
int64_t n_embd_head_k,
int64_t n_embd_head_v,
int32_t n_head,
int32_t n_head_kv,
ggml_tensor ** q_out,
ggml_tensor ** k_out,
ggml_tensor ** v_out,
int il) const {
if (disable_fusion() || !layer.wqkv || (loras && !loras->empty())) {
*q_out = build_lora_mm(layer.wq, cur);
cb(*q_out, "Qcur", il);

*k_out = build_lora_mm(layer.wk, cur);
cb(*k_out, "Kcur", il);

*v_out = build_lora_mm(layer.wv, cur);
cb(*v_out, "Vcur", il);

*q_out = ggml_reshape_3d(ctx0, *q_out, n_embd_head_q, n_head, n_tokens);
*k_out = ggml_reshape_3d(ctx0, *k_out, n_embd_head_k, n_head_kv, n_tokens);
*v_out = ggml_reshape_3d(ctx0, *v_out, n_embd_head_v, n_head_kv, n_tokens);

return;
}


ggml_tensor * qkv = ggml_mul_mat(ctx0, layer.wqkv, cur);
cb(qkv, "wqkv", il);

const int64_t q_offset = 0;
const int64_t k_offset = n_embd_head_q * n_head;
const int64_t v_offset = k_offset + n_embd_head_k * n_head_kv;
const size_t elt_size = ggml_element_size(qkv);

ggml_tensor * Qcur = ggml_view_3d(
ctx0, qkv,
n_embd_head_q, n_head, n_tokens,
n_embd_head_q * elt_size, qkv->nb[1],
q_offset * elt_size);
ggml_tensor * Kcur = ggml_view_3d(
ctx0, qkv,
n_embd_head_k, n_head_kv, n_tokens,
n_embd_head_k * elt_size, qkv->nb[1],
k_offset * elt_size);
ggml_tensor * Vcur = ggml_view_3d(
ctx0, qkv,
n_embd_head_v, n_head_kv, n_tokens,
n_embd_head_v * elt_size, qkv->nb[1],
v_offset * elt_size);

cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);

*q_out = Qcur;
*k_out = Kcur;
*v_out = Vcur;
}

ggml_tensor * llm_graph_context::build_lora_mm_id(
ggml_tensor * w, // ggml_tensor * as
ggml_tensor * cur, // ggml_tensor * b
Expand Down
14 changes: 14 additions & 0 deletions src/llama-graph.h
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Expand Up @@ -24,6 +24,8 @@ class llama_kv_cache_iswa_context;
class llama_memory_recurrent_context;
class llama_memory_hybrid_context;

struct llama_layer;

// certain models (typically multi-modal) can produce different types of graphs
enum llm_graph_type {
LLM_GRAPH_TYPE_DEFAULT,
Expand Down Expand Up @@ -604,6 +606,18 @@ struct llm_graph_context {
ggml_tensor * w,
ggml_tensor * cur) const;

void build_qkv(const llama_layer & layer,
ggml_tensor * cur,
int64_t n_embd_head_q,
int64_t n_embd_head_k,
int64_t n_embd_head_v,
int32_t n_head,
int32_t n_head_kv,
ggml_tensor ** q_out,
ggml_tensor ** k_out,
ggml_tensor ** v_out,
int il) const;

// do mat_mul_id, while optionally apply lora
ggml_tensor * build_lora_mm_id(
ggml_tensor * w, // ggml_tensor * as
Expand Down
34 changes: 34 additions & 0 deletions src/llama-model-loader.cpp
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Expand Up @@ -840,6 +840,40 @@ struct ggml_tensor * llama_model_loader::create_tensor_as_view(struct ggml_conte
return tensor;
}

struct ggml_tensor * llama_model_loader::create_contiguous_tensor(struct ggml_context * ctx, const std::string & fused_name, const std::initializer_list<int64_t> & ne
, std::vector<ggml_tensor**> tensors, int flags) {

(void)flags;

if (weights_map.find(fused_name) != weights_map.end()) {
return nullptr;
}

if (ggml_get_tensor(ctx, fused_name.c_str()) != nullptr) {
return nullptr;
}

const ggml_type type = (*tensors[0])->type;

struct ggml_tensor * fused = ggml_new_tensor(ctx, type, ne.size(), ne.begin());

if (!fused) {
return nullptr;
}

ggml_set_name(fused, fused_name.c_str());

size_t offset = 0;
for (ggml_tensor **tensor : tensors) {
std::initializer_list<int64_t> ne = { (*tensor)->ne[0], (*tensor)->ne[1], (*tensor)->ne[2], (*tensor)->ne[3] };
struct ggml_tensor * view = create_tensor_as_view(ctx, fused, ggml_get_name(*tensor), ne, offset, false);
*tensor = view;
offset += ggml_nbytes(*tensor);
}

return fused;
}

void llama_model_loader::done_getting_tensors() const {
if (n_created != n_tensors) {
throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
Expand Down
3 changes: 3 additions & 0 deletions src/llama-model-loader.h
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Expand Up @@ -147,6 +147,9 @@ struct llama_model_loader {

struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::initializer_list<int64_t> & ne, size_t offset, bool required = true);

struct ggml_tensor * create_contiguous_tensor(struct ggml_context * ctx, const std::string & fused_name, const std::initializer_list<int64_t> & ne
, std::vector<ggml_tensor**> tensors, int flags = 0);

void done_getting_tensors() const;

void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr);
Expand Down
176 changes: 150 additions & 26 deletions src/llama-model.cpp
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
#include "llama-model.h"

#include "gguf.h"
#include "llama-impl.h"
#include "llama-mmap.h"
#include "llama-batch.h"
Expand Down Expand Up @@ -2428,6 +2429,115 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
return ml.create_tensor(ctx, tn, ne, flags);
};

struct tensor_def {
LLM_TN_IMPL tn;
std::vector<int64_t> ne;
int flags;
ggml_tensor ** out;
};

auto create_contiguous = [&](const LLM_TN_IMPL & fused_tn,
std::initializer_list<int64_t> ne,
std::initializer_list<tensor_def> reqs) -> ggml_tensor * {
ggml_backend_buffer_type_t fused_buft = nullptr;

std::vector<const ggml_tensor*> tensor_metas;

for (size_t i = 0; i < reqs.size(); ++i) {
const tensor_def & req = reqs.begin()[i];
const bool required = (req.flags & llama_model_loader::TENSOR_NOT_REQUIRED) == 0;
const ggml_tensor * tensor_meta = ml.check_tensor_dims(req.tn.str(), req.ne, required);

if (!tensor_meta) {
return nullptr;
}

tensor_metas.push_back(tensor_meta);

*req.out = const_cast<ggml_tensor*>(tensor_meta);

if (!*req.out) {
return nullptr;
}

llm_tensor tn_tensor = req.tn.tensor;
if (tn_tensor == LLM_TENSOR_TOKEN_EMBD && (req.flags & llama_model_loader::TENSOR_DUPLICATED)) {
tn_tensor = LLM_TENSOR_OUTPUT;
}

llm_tensor_info info;
try {
info = llm_tensor_info_for(tn_tensor);
} catch (const std::out_of_range &) {
throw std::runtime_error(format("missing tensor info mapping for %s", req.tn.str().c_str()));
}

bool bias = req.tn.suffix != nullptr && strcmp(req.tn.suffix, "bias") == 0;
ggml_op op = bias ? (info.op == GGML_OP_MUL_MAT_ID ? GGML_OP_ADD_ID : GGML_OP_ADD) : info.op;

buft_list_t * buft_list = nullptr;
switch (info.layer) {
case LLM_TENSOR_LAYER_INPUT:
buft_list = pimpl->dev_input.buft_list;
break;
case LLM_TENSOR_LAYER_OUTPUT:
buft_list = pimpl->dev_output.buft_list;
break;
case LLM_TENSOR_LAYER_REPEATING:
buft_list = pimpl->dev_layer.at(req.tn.bid).buft_list;
break;
default:
GGML_ABORT("invalid layer %d for tensor %s", info.layer, req.tn.str().c_str());
}

ggml_backend_buffer_type_t buft = select_weight_buft(hparams, *req.out, op, *buft_list);
if (!buft) {
return nullptr;
}

auto * buft_dev = ggml_backend_buft_get_device(buft);
if (ml.use_mmap && buft_dev && buft == ggml_backend_dev_host_buffer_type(buft_dev)) {
auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
if (!cpu_dev) {
throw std::runtime_error("no CPU backend found");
}
buft = ggml_backend_dev_buffer_type(cpu_dev);
}

//TODO: check buft overrides

if (!fused_buft) {
fused_buft = buft;
} else if (fused_buft != buft) {
return nullptr;
}
}

if (!fused_buft) {
return nullptr;
}

ggml_context * ctx = ctx_for_buft(fused_buft);

std::vector<ggml_tensor**> tensor_req{reqs.size()};

ggml_type type = tensor_metas[0]->type;
for (size_t i = 0; i < reqs.size(); ++i) {

// types are not same
if (tensor_metas[i]->type != type) {
return nullptr;
}

const auto & req = reqs.begin()[i];
tensor_req[i] = req.out;
}

ggml_tensor * fused = ml.create_contiguous_tensor(ctx, fused_tn.str(), ne, tensor_req, 0);

return fused;
};

layers.resize(n_layer);

// TODO: move to a separate function
Expand Down Expand Up @@ -3297,9 +3407,19 @@ bool llama_model::load_tensors(llama_model_loader & ml) {

layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wqkv = create_contiguous(
tn(LLM_TENSOR_ATTN_QKV, "weight", i),
{n_embd, n_embd_head_k * n_head + n_embd_gqa * 2},
{
{ tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0, &layer.wq },
{ tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0, &layer.wk },
{ tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0, &layer.wv },
});
if (!layer.wqkv) {
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
}
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);

layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
Expand Down Expand Up @@ -3328,9 +3448,19 @@ bool llama_model::load_tensors(llama_model_loader & ml) {

layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);

layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wqkv = create_contiguous(
tn(LLM_TENSOR_ATTN_QKV, "weight", i),
{n_embd, n_embd_head_k * n_head + n_embd_gqa * 2},
{
{ tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0, &layer.wq },
{ tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0, &layer.wk },
{ tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0, &layer.wv },
});
if (!layer.wqkv) {
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
}
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);

layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
Expand Down Expand Up @@ -9388,18 +9518,15 @@ struct llm_build_qwen3 : public llm_graph_context {
// self-attention
{
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);

ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);

ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
ggml_tensor * Qcur = nullptr;
ggml_tensor * Kcur = nullptr;
ggml_tensor * Vcur = nullptr;

Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
build_qkv(model.layers[il], cur, n_embd_head,
n_embd_head_k, n_embd_head_v, n_head, n_head_kv,
&Qcur, &Kcur, &Vcur, il
);

Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
Expand Down Expand Up @@ -9509,18 +9636,15 @@ struct llm_build_qwen3moe : public llm_graph_context {
// self_attention
{
// compute Q and K and RoPE them
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
cb(Qcur, "Qcur", il);

ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
cb(Kcur, "Kcur", il);

ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
ggml_tensor * Qcur = nullptr;
ggml_tensor * Kcur = nullptr;
ggml_tensor * Vcur = nullptr;

Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
build_qkv(model.layers[il], cur, n_embd_head,
n_embd_head_k, n_embd_head_v, n_head, n_head_kv,
&Qcur, &Kcur, &Vcur, il
);

Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
cb(Qcur, "Qcur_normed", il);
Expand Down
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