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Merged
merged 1 commit into from
Aug 28, 2025
+20 −138
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kv-cache : remove LLAMA_SET_ROWS checks #15505

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9 changes: 0 additions & 9 deletions ggml/src/ggml-cann/common.h
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Expand Up @@ -374,7 +374,6 @@ struct ggml_backend_cann_context {
#endif
cann_task_queue task_queue;
bool async_mode;
bool support_set_rows;
// Rope Cache
void* rope_init_ptr = nullptr;
void* rope_sin_ptr = nullptr;
Expand All @@ -400,14 +399,6 @@ struct ggml_backend_cann_context {
async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
device, async_mode ? "ON" : "OFF");

support_set_rows = parse_bool(get_env("LLAMA_SET_ROWS").value_or(""));
GGML_LOG_INFO("%s: LLAMA_SET_ROWS is %s\n", __func__, support_set_rows ? "ON" : "OFF");

if (!support_set_rows) {
GGML_LOG_INFO("%s: CANN Graph currently only supports execution when LLAMA_SET_ROWS is ON. "
"Falling back to eager mode.\n", __func__);
}
}

/**
Expand Down
5 changes: 0 additions & 5 deletions ggml/src/ggml-cann/ggml-cann.cpp
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Expand Up @@ -2251,11 +2251,6 @@ static enum ggml_status ggml_backend_cann_graph_compute(
bool use_cann_graph = true;
bool cann_graph_update_required = false;

// check environment LLAMA_SET_ROWS
if (!cann_ctx->support_set_rows) {
use_cann_graph = false;
}

if (use_cann_graph) {
if (cann_ctx->cann_graph == nullptr) {
cann_ctx->cann_graph.reset(new ggml_cann_graph());
Expand Down
22 changes: 0 additions & 22 deletions src/llama-context.cpp
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Expand Up @@ -102,16 +102,6 @@ llama_context::llama_context(
cparams.op_offload = params.op_offload;
cparams.kv_unified = params.kv_unified;

{
const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
supports_set_rows = LLAMA_SET_ROWS ? (atoi(LLAMA_SET_ROWS) != 0) : supports_set_rows;

if (!supports_set_rows && !cparams.kv_unified) {
LLAMA_LOG_WARN("%s: non-unified KV cache requires ggml_set_rows() - forcing unified KV cache\n", __func__);
cparams.kv_unified = true;
}
}

{
const char * LLAMA_GRAPH_REUSE_DISABLE = getenv("LLAMA_GRAPH_REUSE_DISABLE");
graph_reuse_disable = LLAMA_GRAPH_REUSE_DISABLE ? (atoi(LLAMA_GRAPH_REUSE_DISABLE) != 0) : graph_reuse_disable;
Expand Down Expand Up @@ -890,12 +880,6 @@ int llama_context::encode(const llama_batch & batch_inp) {
}
}

if (!supports_set_rows) {
// Reset state for the next token before backend sync, to allow the CPU activities in the reset to
// overlap with device computation.
ggml_backend_sched_reset(sched.get());
}

// TODO: hacky solution
if (model.arch == LLM_ARCH_T5 && t_embd) {
//cross.t_embd = t_embd;
Expand Down Expand Up @@ -1226,12 +1210,6 @@ int llama_context::decode(const llama_batch & batch_inp) {
// wait for the computation to finish (automatically done when obtaining the model output)
//synchronize();

if (!supports_set_rows) {
// Reset state for the next token before backend sync, to allow the CPU activities in the reset to
// overlap with device computation.
ggml_backend_sched_reset(sched.get());
}

return 0;
}

Expand Down
4 changes: 0 additions & 4 deletions src/llama-context.h
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Expand Up @@ -283,10 +283,6 @@ struct llama_context {

bool has_evaluated_once = false;

// env: LLAMA_SET_ROWS (temporary)
// ref: https://github.com/ggml-org/llama.cpp/pull/14285
bool supports_set_rows = true;

// env: LLAMA_GRAPH_REUSE_DISABLE
bool graph_reuse_disable = false;

Expand Down
4 changes: 0 additions & 4 deletions src/llama-graph.cpp
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Expand Up @@ -314,8 +314,6 @@ bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) {
res &= self_kq_mask->ne[0] == mctx->get_n_kv();
res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);

res &= mctx->get_supports_set_rows(); // TODO: tmp

return res;
}

Expand Down Expand Up @@ -350,8 +348,6 @@ bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) {
res &= self_kq_mask_swa->ne[0] == mctx->get_swa()->get_n_kv();
res &= self_kq_mask_swa->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);

res &= mctx->get_base()->get_supports_set_rows(); // TODO: tmp

return res;
}

Expand Down
104 changes: 20 additions & 84 deletions src/llama-kv-cache.cpp
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Expand Up @@ -197,18 +197,6 @@ llama_kv_cache::llama_kv_cache(

const char * LLAMA_KV_CACHE_DEBUG = getenv("LLAMA_KV_CACHE_DEBUG");
debug = LLAMA_KV_CACHE_DEBUG ? atoi(LLAMA_KV_CACHE_DEBUG) : 0;

const char * LLAMA_SET_ROWS = getenv("LLAMA_SET_ROWS");
supports_set_rows = LLAMA_SET_ROWS ? atoi(LLAMA_SET_ROWS) != 0 : supports_set_rows;

if (!supports_set_rows) {
// ref: https://github.com/ggml-org/llama.cpp/pull/14363
GGML_ASSERT(unified && "cannot use non-unified KV cache without ggml_set_rows() support");
}

if (!supports_set_rows) {
LLAMA_LOG_WARN("%s: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility\n", __func__);
}
}

void llama_kv_cache::clear(bool data) {
Expand Down Expand Up @@ -551,11 +539,8 @@ llama_kv_cache::slot_info_vec_t llama_kv_cache::prepare(const std::vector<llama_
bool success = true;

for (const auto & ubatch : ubatches) {
// non-continuous slots require support for ggml_set_rows()
const bool cont = supports_set_rows ? false : true;

// only find a suitable slot for the ubatch. don't modify the cells yet
const auto sinfo_new = find_slot(ubatch, cont);
const auto sinfo_new = find_slot(ubatch, true);
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This should have been false - fixing ..

if (sinfo_new.empty()) {
success = false;
break;
Expand Down Expand Up @@ -976,10 +961,6 @@ uint32_t llama_kv_cache::get_n_kv(const slot_info & sinfo) const {
return result;
}

bool llama_kv_cache::get_supports_set_rows() const {
return supports_set_rows;
}

ggml_tensor * llama_kv_cache::get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const {
const int32_t ikv = map_layer_ids.at(il);

Expand Down Expand Up @@ -1033,36 +1014,26 @@ ggml_tensor * llama_kv_cache::get_v(ggml_context * ctx, int32_t il, uint32_t n_k
}

ggml_tensor * llama_kv_cache::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, ggml_tensor * k_idxs, int32_t il, const slot_info & sinfo) const {
GGML_UNUSED(sinfo);

const int32_t ikv = map_layer_ids.at(il);

auto * k = layers[ikv].k;

const int64_t n_embd_k_gqa = k->ne[0];
const int64_t n_tokens = k_cur->ne[2];

k_cur = ggml_reshape_2d(ctx, k_cur, k->ne[0], n_tokens);

if (k_idxs && supports_set_rows) {
if (k->ne[2] > 1) {
k = ggml_reshape_2d(ctx, k, k->ne[0], k->ne[1]*k->ne[2]);
}

return ggml_set_rows(ctx, k, k_cur, k_idxs);
if (k->ne[2] > 1) {
k = ggml_reshape_2d(ctx, k, k->ne[0], k->ne[1]*k->ne[2]);
}

// TODO: fallback to old ggml_cpy() method for backwards compatibility
// will be removed when ggml_set_rows() is adopted by all backends

GGML_ASSERT(n_stream == 1 && "n_stream > 1 not supported without LLAMA_SET_ROWS");

ggml_tensor * k_view = ggml_view_1d(ctx, k,
n_tokens*n_embd_k_gqa,
ggml_row_size(k->type, n_embd_k_gqa)*sinfo.head());

return ggml_cpy(ctx, k_cur, k_view);
return ggml_set_rows(ctx, k, k_cur, k_idxs);
}

ggml_tensor * llama_kv_cache::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggml_tensor * v_idxs, int32_t il, const slot_info & sinfo) const {
GGML_UNUSED(sinfo);

const int32_t ikv = map_layer_ids.at(il);

auto * v = layers[ikv].v;
Expand All @@ -1072,48 +1043,25 @@ ggml_tensor * llama_kv_cache::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, ggm

v_cur = ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens);

if (v_idxs && supports_set_rows) {
if (!v_trans) {
if (v->ne[2] > 1) {
v = ggml_reshape_2d(ctx, v, v->ne[0], v->ne[1]*v->ne[2]);
}

return ggml_set_rows(ctx, v, v_cur, v_idxs);
}

// [TAG_V_CACHE_VARIABLE]
if (n_embd_v_gqa < v->ne[0]) {
v_cur = ggml_pad(ctx, v_cur, v->ne[0] - n_embd_v_gqa, 0, 0, 0);
if (!v_trans) {
if (v->ne[2] > 1) {
v = ggml_reshape_2d(ctx, v, v->ne[0], v->ne[1]*v->ne[2]);
}

// the row becomes a single element
ggml_tensor * v_view = ggml_reshape_2d(ctx, v, 1, v->ne[0]*v->ne[1]*v->ne[2]);

v_cur = ggml_reshape_2d(ctx, v_cur, 1, v_cur->ne[0]*v_cur->ne[1]);

return ggml_set_rows(ctx, v_view, v_cur, v_idxs);
return ggml_set_rows(ctx, v, v_cur, v_idxs);
}

// TODO: fallback to old ggml_cpy() method for backwards compatibility
// will be removed when ggml_set_rows() is adopted by all backends

GGML_ASSERT(n_stream == 1 && "n_stream > 1 not supported without LLAMA_SET_ROWS");
// [TAG_V_CACHE_VARIABLE]
if (n_embd_v_gqa < v->ne[0]) {
v_cur = ggml_pad(ctx, v_cur, v->ne[0] - n_embd_v_gqa, 0, 0, 0);
}

ggml_tensor * v_view = nullptr;
// the row becomes a single element
ggml_tensor * v_view = ggml_reshape_2d(ctx, v, 1, v->ne[0]*v->ne[1]*v->ne[2]);

if (!v_trans) {
v_view = ggml_view_1d(ctx, v,
n_tokens*n_embd_v_gqa,
ggml_row_size(v->type, n_embd_v_gqa)*sinfo.head());
} else {
v_cur = ggml_transpose(ctx, v_cur);
v_cur = ggml_reshape_2d(ctx, v_cur, 1, v_cur->ne[0]*v_cur->ne[1]);

v_view = ggml_view_2d(ctx, v, n_tokens, n_embd_v_gqa,
(v->ne[1] )*ggml_element_size(v),
(sinfo.head())*ggml_element_size(v));
}

return ggml_cpy(ctx, v_cur, v_view);
return ggml_set_rows(ctx, v_view, v_cur, v_idxs);
}

ggml_tensor * llama_kv_cache::build_input_k_idxs(ggml_context * ctx, const llama_ubatch & ubatch) const {
Expand Down Expand Up @@ -1143,10 +1091,6 @@ ggml_tensor * llama_kv_cache::build_input_v_idxs(ggml_context * ctx, const llama
}

void llama_kv_cache::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
if (!supports_set_rows) {
return;
}

const uint32_t n_tokens = ubatch->n_tokens;
GGML_ASSERT(n_tokens == (int64_t) sinfo.size()*sinfo.n_stream());

Expand All @@ -1163,10 +1107,6 @@ void llama_kv_cache::set_input_k_idxs(ggml_tensor * dst, const llama_ubatch * ub
}

void llama_kv_cache::set_input_v_idxs(ggml_tensor * dst, const llama_ubatch * ubatch, const slot_info & sinfo) const {
if (!supports_set_rows) {
return;
}

const uint32_t n_tokens = ubatch->n_tokens;
GGML_ASSERT(n_tokens == (int64_t) sinfo.size()*sinfo.n_stream());

Expand Down Expand Up @@ -2004,10 +1944,6 @@ uint32_t llama_kv_cache_context::get_n_kv() const {
return n_kv;
}

bool llama_kv_cache_context::get_supports_set_rows() const {
return kv->get_supports_set_rows();
}

ggml_tensor * llama_kv_cache_context::get_k(ggml_context * ctx, int32_t il) const {
return kv->get_k(ctx, il, n_kv, sinfos[i_cur]);
}
Expand Down
10 changes: 0 additions & 10 deletions src/llama-kv-cache.h
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Expand Up @@ -141,9 +141,6 @@ class llama_kv_cache : public llama_memory_i {

uint32_t get_n_kv(const slot_info & sinfo) const;

// TODO: temporary
bool get_supports_set_rows() const;

// get views of the current state of the cache
ggml_tensor * get_k(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;
ggml_tensor * get_v(ggml_context * ctx, int32_t il, uint32_t n_kv, const slot_info & sinfo) const;
Expand Down Expand Up @@ -215,10 +212,6 @@ class llama_kv_cache : public llama_memory_i {
// env: LLAMA_KV_CACHE_DEBUG
int debug = 0;

// env: LLAMA_SET_ROWS (temporary)
// ref: https://github.com/ggml-org/llama.cpp/pull/14285
bool supports_set_rows = true;

const llama_swa_type swa_type = LLAMA_SWA_TYPE_NONE;

std::vector<ggml_context_ptr> ctxs;
Expand Down Expand Up @@ -318,9 +311,6 @@ class llama_kv_cache_context : public llama_memory_context_i {

uint32_t get_n_kv() const;

// TODO: temporary
bool get_supports_set_rows() const;

// get views of the current state of the cache
ggml_tensor * get_k(ggml_context * ctx, int32_t il) const;
ggml_tensor * get_v(ggml_context * ctx, int32_t il) const;
Expand Down
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