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merged 2 commits into from
Aug 21, 2025
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kv-cache : drop the "unified" prefix #15467

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8a5a43a
kv-cache : drop the "unified" prefix
ggerganov Aug 21, 2025
94188a7
cont : fix comment [no ci]
ggerganov Aug 21, 2025
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4 changes: 0 additions & 4 deletions include/llama.h
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Original file line number Diff line number Diff line change
Expand Up @@ -64,8 +64,6 @@ extern "C" {

typedef struct llama_memory_i * llama_memory_t;

struct llama_kv_cache; // DEPRECATED (use llama_memory instead)

typedef int32_t llama_pos;
typedef int32_t llama_token;
typedef int32_t llama_seq_id;
Expand Down Expand Up @@ -469,8 +467,6 @@ extern "C" {
LLAMA_API llama_memory_t llama_get_memory (const struct llama_context * ctx);
LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); // TODO: rename to llama_get_pooling_type

DEPRECATED(LLAMA_API struct llama_kv_cache * llama_get_kv_self(struct llama_context * ctx), "use llama_get_memory instead");

LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_model_rope_type(const struct llama_model * model);

Expand Down
4 changes: 2 additions & 2 deletions src/CMakeLists.txt
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Expand Up @@ -20,8 +20,8 @@ add_library(llama
llama-hparams.cpp
llama-impl.cpp
llama-io.cpp
llama-kv-cache-unified.cpp
llama-kv-cache-unified-iswa.cpp
llama-kv-cache.cpp
llama-kv-cache-iswa.cpp
llama-memory.cpp
llama-memory-hybrid.cpp
llama-memory-recurrent.cpp
Expand Down
5 changes: 0 additions & 5 deletions src/llama-context.cpp
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Expand Up @@ -2338,11 +2338,6 @@ const llama_model * llama_get_model(const llama_context * ctx) {
return &ctx->get_model();
}

// deprecated
llama_kv_cache * llama_get_kv_self(llama_context * ctx) {
return dynamic_cast<llama_kv_cache *>(ctx->get_memory());
}

// deprecated
void llama_kv_self_update(llama_context * ctx) {
ctx->kv_self_update(false);
Expand Down
54 changes: 27 additions & 27 deletions src/llama-graph.cpp
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Expand Up @@ -4,8 +4,8 @@
#include "llama-batch.h"
#include "llama-cparams.h"

#include "llama-kv-cache-unified.h"
#include "llama-kv-cache-unified-iswa.h"
#include "llama-kv-cache.h"
#include "llama-kv-cache-iswa.h"
#include "llama-memory-hybrid.h"
#include "llama-memory-recurrent.h"

Expand Down Expand Up @@ -277,7 +277,7 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
for (int s = 0; s < ubatch->n_seq_id[i0]; ++s) {
const llama_seq_id s0 = ubatch->seq_id[i0][0];

// TODO: reimplement this like in llama_kv_cache_unified
// TODO: reimplement this like in llama_kv_cache
if (s0 == s1 && (!cparams.causal_attn || ubatch->pos[i0] <= ubatch->pos[i1])) {
if (hparams.use_alibi) {
f = -std::abs(ubatch->pos[i0] - ubatch->pos[i1]);
Expand All @@ -294,15 +294,15 @@ void llm_graph_input_attn_no_cache::set_input(const llama_ubatch * ubatch) {
}
}

void llm_graph_input_attn_kv_unified::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_attn_kv::set_input(const llama_ubatch * ubatch) {
mctx->set_input_k_idxs(self_k_idxs, ubatch);
mctx->set_input_v_idxs(self_v_idxs, ubatch);

mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}

bool llm_graph_input_attn_kv_unified::can_reuse(const llm_graph_params & params) {
const auto * mctx = static_cast<const llama_kv_cache_unified_context *>(params.mctx);
bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) {
const auto * mctx = static_cast<const llama_kv_cache_context *>(params.mctx);

this->mctx = mctx;

Expand All @@ -319,7 +319,7 @@ bool llm_graph_input_attn_kv_unified::can_reuse(const llm_graph_params & params)
return res;
}

void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_attn_kv_iswa::set_input(const llama_ubatch * ubatch) {
mctx->get_base()->set_input_k_idxs(self_k_idxs, ubatch);
mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);

Expand All @@ -331,8 +331,8 @@ void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch
mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
}

bool llm_graph_input_attn_kv_unified_iswa::can_reuse(const llm_graph_params & params) {
const auto * mctx = static_cast<const llama_kv_cache_unified_iswa_context *>(params.mctx);
bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) {
const auto * mctx = static_cast<const llama_kv_cache_iswa_context *>(params.mctx);

this->mctx = mctx;

Expand Down Expand Up @@ -1186,7 +1186,7 @@ ggml_tensor * llm_graph_context::build_inp_pos_bucket_enc() const {
}

ggml_tensor * llm_graph_context::build_inp_pos_bucket_dec() const {
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
const auto * mctx_cur = static_cast<const llama_kv_cache_context *>(mctx);

auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, mctx_cur);

Expand Down Expand Up @@ -1399,17 +1399,17 @@ ggml_tensor * llm_graph_context::build_attn(
return cur;
}

static std::unique_ptr<llm_graph_input_attn_kv_unified> build_attn_inp_kv_unified_impl(
static std::unique_ptr<llm_graph_input_attn_kv> build_attn_inp_kv_impl(
ggml_context * ctx0,
const llama_ubatch & ubatch,
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_unified_context * mctx_cur) {
const llama_kv_cache_context * mctx_cur) {

auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, mctx_cur);
auto inp = std::make_unique<llm_graph_input_attn_kv>(hparams, cparams, mctx_cur);

{
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified_iswa for SWA");
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_iswa for SWA");

const auto n_kv = mctx_cur->get_n_kv();
const auto n_tokens = ubatch.n_tokens;
Expand All @@ -1427,16 +1427,16 @@ static std::unique_ptr<llm_graph_input_attn_kv_unified> build_attn_inp_kv_unifie
return inp;
}

llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const {
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
llm_graph_input_attn_kv * llm_graph_context::build_attn_inp_kv() const {
const auto * mctx_cur = static_cast<const llama_kv_cache_context *>(mctx);

auto inp = build_attn_inp_kv_unified_impl(ctx0, ubatch, hparams, cparams, mctx_cur);
auto inp = build_attn_inp_kv_impl(ctx0, ubatch, hparams, cparams, mctx_cur);

return (llm_graph_input_attn_kv_unified *) res->add_input(std::move(inp));
return (llm_graph_input_attn_kv *) res->add_input(std::move(inp));
}

ggml_tensor * llm_graph_context::build_attn(
llm_graph_input_attn_kv_unified * inp,
llm_graph_input_attn_kv * inp,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur,
Expand Down Expand Up @@ -1488,7 +1488,7 @@ ggml_tensor * llm_graph_context::build_attn(
}

ggml_tensor * llm_graph_context::build_attn(
llm_graph_input_attn_kv_unified_iswa * inp,
llm_graph_input_attn_kv_iswa * inp,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur,
Expand All @@ -1513,7 +1513,7 @@ ggml_tensor * llm_graph_context::build_attn(
}

ggml_tensor * llm_graph_context::build_attn_with_sinks(
llm_graph_input_attn_kv_unified_iswa * inp,
llm_graph_input_attn_kv_iswa * inp,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur,
Expand Down Expand Up @@ -1636,10 +1636,10 @@ ggml_tensor * llm_graph_context::build_attn(
// TODO: maybe separate the inner implementation into a separate function
// like with the non-sliding window equivalent
// once sliding-window hybrid caches are a thing.
llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unified_iswa() const {
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const {
const auto * mctx_cur = static_cast<const llama_kv_cache_iswa_context *>(mctx);

auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, mctx_cur);
auto inp = std::make_unique<llm_graph_input_attn_kv_iswa>(hparams, cparams, mctx_cur);

const auto n_stream = cparams.kv_unified ? 1 : ubatch.n_seqs_unq;

Expand All @@ -1656,7 +1656,7 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
}

{
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified for non-SWA");
GGML_ASSERT(hparams.swa_type != LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache for non-SWA");

const auto n_kv = mctx_cur->get_swa()->get_n_kv();

Expand All @@ -1669,7 +1669,7 @@ llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unif
inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
}

return (llm_graph_input_attn_kv_unified_iswa *) res->add_input(std::move(inp));
return (llm_graph_input_attn_kv_iswa *) res->add_input(std::move(inp));
}

ggml_tensor * llm_graph_context::build_rs(
Expand Down Expand Up @@ -1792,7 +1792,7 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);

auto inp_rs = build_rs_inp_impl(ctx0, ubatch, mctx_cur->get_recr());
auto inp_attn = build_attn_inp_kv_unified_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
auto inp_attn = build_attn_inp_kv_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());

auto inp = std::make_unique<llm_graph_input_mem_hybrid>(std::move(inp_attn), std::move(inp_rs), mctx_cur);

Expand Down
50 changes: 25 additions & 25 deletions src/llama-graph.h
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Expand Up @@ -19,8 +19,8 @@ struct llama_cparams;

struct llama_memory_context_i;

class llama_kv_cache_unified_context;
class llama_kv_cache_unified_iswa_context;
class llama_kv_cache_context;
class llama_kv_cache_iswa_context;
class llama_memory_recurrent_context;
class llama_memory_hybrid_context;

Expand Down Expand Up @@ -152,7 +152,7 @@ class llm_graph_input_pos_bucket_kv : public llm_graph_input_i {
public:
llm_graph_input_pos_bucket_kv(
const llama_hparams & hparams,
const llama_kv_cache_unified_context * mctx) : hparams(hparams), mctx(mctx) {}
const llama_kv_cache_context * mctx) : hparams(hparams), mctx(mctx) {}
virtual ~llm_graph_input_pos_bucket_kv() = default;

void set_input(const llama_ubatch * ubatch) override;
Expand All @@ -161,7 +161,7 @@ class llm_graph_input_pos_bucket_kv : public llm_graph_input_i {

const llama_hparams hparams;

const llama_kv_cache_unified_context * mctx;
const llama_kv_cache_context * mctx;
};

class llm_graph_input_out_ids : public llm_graph_input_i {
Expand Down Expand Up @@ -257,17 +257,17 @@ class llm_graph_input_attn_no_cache : public llm_graph_input_i {
const llama_cparams cparams;
};

class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
class llm_graph_input_attn_kv : public llm_graph_input_i {
public:
llm_graph_input_attn_kv_unified(
llm_graph_input_attn_kv(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_unified_context * mctx) :
const llama_kv_cache_context * mctx) :
hparams(hparams),
cparams(cparams),
mctx(mctx) {
}
~llm_graph_input_attn_kv_unified() = default;
~llm_graph_input_attn_kv() = default;

void set_input(const llama_ubatch * ubatch) override;

Expand All @@ -290,20 +290,20 @@ class llm_graph_input_attn_kv_unified : public llm_graph_input_i {
const llama_hparams hparams;
const llama_cparams cparams;

const llama_kv_cache_unified_context * mctx;
const llama_kv_cache_context * mctx;
};

class llm_graph_input_attn_kv_unified_iswa : public llm_graph_input_i {
class llm_graph_input_attn_kv_iswa : public llm_graph_input_i {
public:
llm_graph_input_attn_kv_unified_iswa(
llm_graph_input_attn_kv_iswa(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_unified_iswa_context * mctx) :
const llama_kv_cache_iswa_context * mctx) :
hparams(hparams),
cparams(cparams),
mctx(mctx) {
}
~llm_graph_input_attn_kv_unified_iswa() = default;
~llm_graph_input_attn_kv_iswa() = default;

void set_input(const llama_ubatch * ubatch) override;

Expand All @@ -330,7 +330,7 @@ class llm_graph_input_attn_kv_unified_iswa : public llm_graph_input_i {
const llama_hparams hparams;
const llama_cparams cparams;

const llama_kv_cache_unified_iswa_context * mctx;
const llama_kv_cache_iswa_context * mctx;
};

class llm_graph_input_attn_cross : public llm_graph_input_i {
Expand All @@ -351,7 +351,7 @@ class llm_graph_input_attn_cross : public llm_graph_input_i {
class llm_graph_input_mem_hybrid : public llm_graph_input_i {
public:
llm_graph_input_mem_hybrid(
std::unique_ptr<llm_graph_input_attn_kv_unified> inp_attn,
std::unique_ptr<llm_graph_input_attn_kv> inp_attn,
std::unique_ptr<llm_graph_input_rs> inp_rs,
const llama_memory_hybrid_context * mctx) :
inp_attn(std::move(inp_attn)),
Expand All @@ -361,11 +361,11 @@ class llm_graph_input_mem_hybrid : public llm_graph_input_i {

void set_input(const llama_ubatch * ubatch) override;

std::unique_ptr<llm_graph_input_attn_kv_unified> inp_attn;
std::unique_ptr<llm_graph_input_rs> inp_rs;
std::unique_ptr<llm_graph_input_attn_kv> inp_attn;
std::unique_ptr<llm_graph_input_rs> inp_rs;

llm_graph_input_attn_kv_unified * get_attn() const { return inp_attn.get(); }
llm_graph_input_rs * get_recr() const { return inp_rs.get(); }
llm_graph_input_attn_kv * get_attn() const { return inp_attn.get(); }
llm_graph_input_rs * get_recr() const { return inp_rs.get(); }

const llama_memory_hybrid_context * mctx;
};
Expand Down Expand Up @@ -703,10 +703,10 @@ struct llm_graph_context {
float kq_scale,
int il) const;

llm_graph_input_attn_kv_unified * build_attn_inp_kv_unified() const;
llm_graph_input_attn_kv * build_attn_inp_kv() const;

ggml_tensor * build_attn(
llm_graph_input_attn_kv_unified * inp,
llm_graph_input_attn_kv * inp,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
Expand All @@ -717,11 +717,11 @@ struct llm_graph_context {
float kq_scale,
int il) const;

llm_graph_input_attn_kv_unified_iswa * build_attn_inp_kv_unified_iswa() const;
llm_graph_input_attn_kv_iswa * build_attn_inp_kv_iswa() const;

// note: if k_cur or v_cur are not provided, they will not be stored in the memory
ggml_tensor * build_attn(
llm_graph_input_attn_kv_unified_iswa * inp,
llm_graph_input_attn_kv_iswa * inp,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
Expand All @@ -734,7 +734,7 @@ struct llm_graph_context {

// TODO: temporary to keep the diff small. after the code is public will refactor to simplify this
ggml_tensor * build_attn_with_sinks(
llm_graph_input_attn_kv_unified_iswa * inp,
llm_graph_input_attn_kv_iswa * inp,
ggml_tensor * wo,
ggml_tensor * wo_b,
ggml_tensor * q_cur, // [n_embd_head_q, n_head_q, n_tokens]
Expand Down Expand Up @@ -765,7 +765,7 @@ struct llm_graph_context {
//

// TODO: move this implementation to llama_memory_recurrent.
// this is analogous to llama_kv_cache_unified::cpy_k / cpy_v
// this is analogous to llama_kv_cache::cpy_k / cpy_v
// when moving, avoid passing `ggml_cgraph` - only pass `ggml_context`. would likely need to split the
// implementation in 2 separate methods. the goal is to avoid calling `ggml_build_forward_expand` in
// `llama_memory_recurrent`
Expand Down
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