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memory : rename interface to llama_memory_context_i (#14296)

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* memory : rename interface to llama_memory_context_i

ggml-ci

* cont : fix comments

* cont : use "mctx" for referencing a memory context

ggml-ci
This commit is contained in:
Georgi Gerganov
2025-06-21 08:03:46 +03:00
committed by GitHub
parent b23fa0b3f4
commit 692e3cdd0a
14 changed files with 339 additions and 341 deletions
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70
src/llama-context.cpp
View File

@ -280,8 +280,8 @@ llama_context::llama_context(
// simulate full KV cache
const auto mstate = memory->init_full();
if (!mstate) {
const auto mctx = memory->init_full();
if (!mctx) {
throw std::runtime_error("failed to initialize KV cache");
}
@ -289,7 +289,7 @@ llama_context::llama_context(
// reserve pp graph first so that buffers are only allocated once
{
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
@ -300,7 +300,7 @@ llama_context::llama_context(
// reserve with tg graph to get the number of splits and nodes
{
auto * gf = graph_reserve(1, 1, 1, mstate.get());
auto * gf = graph_reserve(1, 1, 1, mctx.get());
if (!gf) {
throw std::runtime_error("failed to allocate compute tg buffers");
}
@ -311,7 +311,7 @@ llama_context::llama_context(
// reserve again with pp graph to avoid ggml-alloc reallocations during inference
{
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
throw std::runtime_error("failed to allocate compute pp buffers");
}
@ -444,8 +444,8 @@ bool llama_context::kv_self_update(bool optimize) {
optimize |= memory_force_optimize;
memory_force_optimize = false;
const auto mstate = memory->init_update(this, optimize);
switch (mstate->get_status()) {
const auto mctx = memory->init_update(this, optimize);
switch (mctx->get_status()) {
case LLAMA_MEMORY_STATUS_SUCCESS:
{
// noop
@ -463,22 +463,22 @@ bool llama_context::kv_self_update(bool optimize) {
}
}
if (!mstate->apply()) {
if (!mctx->apply()) {
LLAMA_LOG_ERROR("%s: failed to apply memory update\n", __func__);
}
}
// if the memory module did any computation, we have to reserve a new worst-case graph
{
const auto mstate = memory->init_full();
if (!mstate) {
throw std::runtime_error("failed to initialize memory state");
const auto mctx = memory->init_full();
if (!mctx) {
throw std::runtime_error("failed to initialize memory context");
}
const uint32_t n_seqs = cparams.n_seq_max;
const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mstate.get());
auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
if (!gf) {
LLAMA_LOG_ERROR("%s: failed to reserve graph after the memory update\n", __func__);
}
@ -678,9 +678,9 @@ bool llama_context::apply_adapter_cvec(
return cvec.apply(model, data, len, n_embd, il_start, il_end);
}
llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_state_i * mstate, ggml_status & ret) {
if (mstate && !mstate->apply()) {
LLAMA_LOG_ERROR("%s: failed to apply memory state\n", __func__);
llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch, llm_graph_type gtype, llama_memory_context_i * mctx, ggml_status & ret) {
if (mctx && !mctx->apply()) {
LLAMA_LOG_ERROR("%s: failed to apply memory context\n", __func__);
ret = GGML_STATUS_FAILED;
return nullptr;
}
@ -692,7 +692,7 @@ llm_graph_result_ptr llama_context::process_ubatch(const llama_ubatch & ubatch,
return nullptr;
}
auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mstate);
auto res = graph_build(ctx_compute.get(), gf, ubatch, gtype, mctx);
if (!res) {
LLAMA_LOG_ERROR("%s: failed to build graph\n", __func__);
ret = GGML_STATUS_FAILED;
@ -933,21 +933,21 @@ int llama_context::decode(const llama_batch & batch_inp) {
// handle any pending defrags/shifts
kv_self_update(false);
llama_memory_state_ptr mstate;
llama_memory_context_ptr mctx;
while (true) {
mstate = memory->init_batch(*balloc, cparams.n_ubatch, output_all);
if (!mstate) {
mctx = memory->init_batch(*balloc, cparams.n_ubatch, output_all);
if (!mctx) {
return -2;
}
switch (mstate->get_status()) {
switch (mctx->get_status()) {
case LLAMA_MEMORY_STATUS_SUCCESS:
{
} break;
case LLAMA_MEMORY_STATUS_NO_UPDATE:
{
LLAMA_LOG_ERROR("%s: unexpected memory state status: %d\n", __func__, mstate->get_status());
LLAMA_LOG_ERROR("%s: unexpected memory context status: %d\n", __func__, mctx->get_status());
return -2;
}
@ -987,7 +987,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
int64_t n_outputs_prev = 0;
do {
const auto & ubatch = mstate->get_ubatch();
const auto & ubatch = mctx->get_ubatch();
// count the outputs in this ubatch
{
@ -1009,7 +1009,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
ggml_backend_sched_set_eval_callback(sched.get(), cparams.cb_eval, cparams.cb_eval_user_data);
ggml_status status;
const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mstate.get(), status);
const auto res = process_ubatch(ubatch, LLM_GRAPH_TYPE_DECODER, mctx.get(), status);
if (!res) {
// the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache
@ -1126,7 +1126,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
}
n_outputs_prev += n_outputs;
} while (mstate->next());
} while (mctx->next());
// set to total number of outputs in the batch, for use in llama_get_logits_ith
n_outputs = n_outputs_all;
@ -1292,7 +1292,7 @@ ggml_cgraph * llama_context::graph_init() {
return ggml_new_graph_custom(ctx_compute.get(), graph_max_nodes(), false);
}
ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate) {
ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx) {
LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
if (n_tokens % n_seqs != 0) {
@ -1312,7 +1312,7 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u
llama_ubatch ubatch = balloc.ubatch_reserve(n_tokens/n_seqs, n_seqs);
auto * gf = graph_init();
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mstate);
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mctx);
this->n_outputs = save_n_outputs;
@ -1337,7 +1337,7 @@ llm_graph_result_ptr llama_context::graph_build(
ggml_cgraph * gf,
const llama_ubatch & ubatch,
llm_graph_type gtype,
const llama_memory_state_i * mstate) {
const llama_memory_context_i * mctx) {
return model.build_graph(
{
/*.ctx =*/ ctx,
@ -1349,7 +1349,7 @@ llm_graph_result_ptr llama_context::graph_build(
/*.backend_cpu =*/ backend_cpu,
/*.cvec =*/ &cvec,
/*.loras =*/ &loras,
/*.mstate =*/ mstate,
/*.mctx =*/ mctx,
/*.cross =*/ &cross,
/*.n_outputs =*/ n_outputs,
/*.cb =*/ graph_get_cb(),
@ -2042,8 +2042,8 @@ void llama_context::opt_epoch_iter(
uint32_t n_outputs_all = n_tokens_all;
auto mstate = memory->init_batch(*balloc, cparams.n_ubatch, true);
if (!mstate || mstate->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
auto mctx = memory->init_batch(*balloc, cparams.n_ubatch, true);
if (!mctx || mctx->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: could not initialize batch\n", __func__);
break;
}
@ -2056,17 +2056,17 @@ void llama_context::opt_epoch_iter(
uint32_t pos_batch = 0;
do {
const auto & ubatch = mstate->get_ubatch();
const auto & ubatch = mctx->get_ubatch();
n_outputs = ubatch.n_tokens;
if (!mstate->apply()) {
LLAMA_LOG_ERROR("%s: failed to update the memory state\n", __func__);
if (!mctx->apply()) {
LLAMA_LOG_ERROR("%s: failed to update the memory context\n", __func__);
break;
}
auto * gf = graph_init();
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mstate.get());
auto res = graph_build(ctx_compute.get(), gf, ubatch, LLM_GRAPH_TYPE_DEFAULT, mctx.get());
struct ggml_context * ctx_compute_opt;
{
@ -2101,7 +2101,7 @@ void llama_context::opt_epoch_iter(
ggml_free(ctx_compute_opt);
pos_batch += ubatch.n_tokens;
} while (mstate->next());
} while (mctx->next());
}
}

10
src/llama-context.h
View File

@ -18,7 +18,7 @@ class llama_io_read_i;
class llama_io_write_i;
struct llama_memory_i;
struct llama_memory_state_i;
struct llama_memory_context_i;
struct llama_context {
// init scheduler and compute buffers, reserve worst-case graphs
@ -93,13 +93,13 @@ struct llama_context {
int32_t il_end);
// process a single ubatch with a specific graph type
// if memory_state is provided, it will be applied first to the context's memory
// if memory_context is provided, it will be applied first to the context's memory
// ret contains the status of the graph computation
// returns nullptr only if ret != GGML_STATUS_SUCCESS
llm_graph_result_ptr process_ubatch(
const llama_ubatch & ubatch,
llm_graph_type gtype,
llama_memory_state_i * mstate,
llama_memory_context_i * mctx,
ggml_status & ret);
int encode(const llama_batch & batch_inp);
@ -197,7 +197,7 @@ public:
ggml_status graph_compute(ggml_cgraph * gf, bool batched);
// reserve a graph with a dummy ubatch of the specified size
ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_state_i * mstate);
ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx);
private:
llm_graph_result_ptr graph_build(
@ -205,7 +205,7 @@ private:
ggml_cgraph * gf,
const llama_ubatch & ubatch,
llm_graph_type gtype,
const llama_memory_state_i * mstate);
const llama_memory_context_i * mctx);
llm_graph_cb graph_get_cb() const;

104
src/llama-graph.cpp
View File

@ -87,7 +87,7 @@ void llm_graph_input_pos_bucket::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_pos_bucket_kv::set_input(const llama_ubatch * ubatch) {
if (pos_bucket) {
kv_state->set_input_pos_bucket(pos_bucket, ubatch);
mctx->set_input_pos_bucket(pos_bucket, ubatch);
}
}
@ -221,7 +221,7 @@ void llm_graph_input_cls::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
GGML_UNUSED(ubatch);
const int64_t n_rs = mem_state->get_n_rs();
const int64_t n_rs = mctx->get_n_rs();
if (s_copy) {
GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer));
@ -229,7 +229,7 @@ void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
for (uint32_t i = 0; i < n_rs; ++i) {
data[i] = mem_state->s_copy(i);
data[i] = mctx->s_copy(i);
}
}
}
@ -282,17 +282,17 @@ 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) {
if (self_kq_mask) {
kv_state->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
mctx->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
}
void llm_graph_input_attn_kv_unified_iswa::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
kv_state->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
if (self_kq_mask_swa) {
kv_state->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
}
}
@ -334,10 +334,10 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
if (self_kq_mask) {
mem_state->get_state_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
mctx->get_attn()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
}
const int64_t n_rs = mem_state->get_state_recr()->get_n_rs();
const int64_t n_rs = mctx->get_recr()->get_n_rs();
if (s_copy) {
GGML_ASSERT(ggml_backend_buffer_is_host(s_copy->buffer));
@ -345,7 +345,7 @@ void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
// assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
for (uint32_t i = 0; i < n_rs; ++i) {
data[i] = mem_state->get_state_recr()->s_copy(i);
data[i] = mctx->get_recr()->s_copy(i);
}
}
}
@ -389,7 +389,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
backend_cpu (params.backend_cpu),
cvec (params.cvec),
loras (params.loras),
mstate (params.mstate),
mctx (params.mctx),
cross (params.cross),
cb_func (params.cb),
res (std::make_unique<llm_graph_result>()) {
@ -950,11 +950,11 @@ ggml_tensor * llm_graph_context::build_inp_pos_bucket_enc() const {
}
ggml_tensor * llm_graph_context::build_inp_pos_bucket_dec() const {
const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, kv_state);
auto inp = std::make_unique<llm_graph_input_pos_bucket_kv>(hparams, mctx_cur);
const auto n_kv = kv_state->get_n_kv();
const auto n_kv = mctx_cur->get_n_kv();
auto & cur = inp->pos_bucket;
@ -982,14 +982,14 @@ ggml_tensor * llm_graph_context::build_pos_bias(ggml_tensor * pos_bucket, ggml_t
}
llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
const auto * mem_state = static_cast<const llama_memory_hybrid_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_mem_hybrid>(hparams, cparams, mem_state);
auto inp = std::make_unique<llm_graph_input_mem_hybrid>(hparams, cparams, mctx_cur);
{
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Hybrid recurrent is not supported with SWA attention layers");
const auto n_kv = inp->mem_state->get_state_attn()->get_n_kv();
const auto n_kv = inp->mctx->get_attn()->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
@ -999,7 +999,7 @@ llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
}
{
const auto n_rs = mem_state->get_state_recr()->get_n_rs();
const auto n_rs = mctx_cur->get_recr()->get_n_rs();
inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
ggml_set_input(inp->s_copy);
@ -1183,14 +1183,14 @@ ggml_tensor * llm_graph_context::build_attn(
}
llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const {
const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, kv_state);
auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, mctx_cur);
{
GGML_ASSERT(hparams.swa_type == LLAMA_SWA_TYPE_NONE && "Use llama_kv_cache_unified_iswa for SWA");
const auto n_kv = kv_state->get_n_kv();
const auto n_kv = mctx_cur->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
@ -1220,19 +1220,19 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
const auto * kv_state = static_cast<const llama_kv_cache_unified_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_context *>(mctx);
// store to KV cache
{
ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = inp->get_kq_mask();
ggml_tensor * q = q_cur;
ggml_tensor * k = kv_state->get_k(ctx0, il);
ggml_tensor * v = kv_state->get_v(ctx0, il);
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
@ -1270,23 +1270,23 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
const auto * kv_state_iswa = static_cast<const llama_kv_cache_unified_iswa_state *>(mstate);
const auto * mctx_iswa = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
const bool is_swa = hparams.is_swa(il);
const auto * kv_state = is_swa ? kv_state_iswa->get_swa() : kv_state_iswa->get_base();
const auto * mctx_cur = is_swa ? mctx_iswa->get_swa() : mctx_iswa->get_base();
// store to KV cache
{
ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = is_swa ? inp->get_kq_mask_swa() : inp->get_kq_mask();
ggml_tensor * q = q_cur;
ggml_tensor * k = kv_state->get_k(ctx0, il);
ggml_tensor * v = kv_state->get_v(ctx0, il);
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
@ -1379,19 +1379,19 @@ ggml_tensor * llm_graph_context::build_attn(
ggml_build_forward_expand(gf, k_cur);
ggml_build_forward_expand(gf, v_cur);
const auto * kv_state = static_cast<const llama_memory_hybrid_state *>(mstate)->get_state_attn();
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_attn();
// store to KV cache
{
ggml_build_forward_expand(gf, kv_state->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, kv_state->cpy_v(ctx0, v_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_k(ctx0, k_cur, il));
ggml_build_forward_expand(gf, mctx_cur->cpy_v(ctx0, v_cur, il));
}
const auto & kq_mask = inp->get_kq_mask();
ggml_tensor * q = q_cur;
ggml_tensor * k = kv_state->get_k(ctx0, il);
ggml_tensor * v = kv_state->get_v(ctx0, il);
ggml_tensor * k = mctx_cur->get_k(ctx0, il);
ggml_tensor * v = mctx_cur->get_v(ctx0, il);
ggml_tensor * cur = build_attn_mha(gf, q, k, v, kq_b, kq_mask, v_mla, kq_scale);
cb(cur, "kqv_out", il);
@ -1412,12 +1412,12 @@ ggml_tensor * llm_graph_context::build_attn(
}
llm_graph_input_attn_kv_unified_iswa * llm_graph_context::build_attn_inp_kv_unified_iswa() const {
const auto * kv_state = static_cast<const llama_kv_cache_unified_iswa_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_kv_cache_unified_iswa_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, kv_state);
auto inp = std::make_unique<llm_graph_input_attn_kv_unified_iswa>(hparams, cparams, mctx_cur);
{
const auto n_kv = kv_state->get_base()->get_n_kv();
const auto n_kv = mctx_cur->get_base()->get_n_kv();
inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask, "KQ_mask", -1);
@ -1429,7 +1429,7 @@ 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");
const auto n_kv = kv_state->get_swa()->get_n_kv();
const auto n_kv = mctx_cur->get_swa()->get_n_kv();
inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
//cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1);
@ -1485,11 +1485,11 @@ ggml_tensor * llm_graph_context::build_rs(
}
llm_graph_input_rs * llm_graph_context::build_rs_inp() const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
auto inp = std::make_unique<llm_graph_input_rs>(kv_state);
auto inp = std::make_unique<llm_graph_input_rs>(mctx_cur);
const auto n_rs = kv_state->get_n_rs();
const auto n_rs = mctx_cur->get_n_rs();
inp->s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_rs);
ggml_set_input(inp->s_copy);
@ -1504,9 +1504,9 @@ ggml_tensor * llm_graph_context::build_rs(
int32_t state_size,
int32_t n_seqs,
bool avoid_copies) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), avoid_copies);
return build_rs(gf, s, inp->s_copy, state_size, n_seqs, mctx_cur->get_n_rs(), mctx_cur->get_head(), mctx_cur->get_size(), mctx_cur->get_rs_z(), avoid_copies);
}
ggml_tensor * llm_graph_context::build_rs(
@ -1516,9 +1516,9 @@ ggml_tensor * llm_graph_context::build_rs(
int32_t state_size,
int32_t n_seqs,
bool avoid_copies) const {
const auto * kv_state = static_cast<const llama_memory_hybrid_state *>(mstate)->get_state_recr();
const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
return build_rs(gf, s, inp->s_copy, state_size, n_seqs, kv_state->get_n_rs(), kv_state->get_head(), kv_state->get_size(), kv_state->get_rs_z(), avoid_copies);
return build_rs(gf, s, inp->s_copy, state_size, n_seqs, mctx_cur->get_n_rs(), mctx_cur->get_head(), mctx_cur->get_size(), mctx_cur->get_rs_z(), avoid_copies);
}
ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
@ -1526,13 +1526,13 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_load(
ggml_cgraph * gf,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto token_shift_count = hparams.token_shift_count;
const int64_t n_seqs = ubatch.n_seqs;
ggml_tensor * token_shift_all = kv_state->get_r_l(il);
ggml_tensor * token_shift_all = mctx_cur->get_r_l(il);
ggml_tensor * token_shift = build_rs(
inp, gf, token_shift_all,
@ -1547,19 +1547,19 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
ggml_tensor * token_shift,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto token_shift_count = hparams.token_shift_count;
const auto n_embd = hparams.n_embd;
const int64_t n_seqs = ubatch.n_seqs;
const auto kv_head = kv_state->get_head();
const auto kv_head = mctx_cur->get_head();
return ggml_cpy(
ctx0,
ggml_view_1d(ctx0, token_shift, n_embd * n_seqs * token_shift_count, 0),
ggml_view_1d(ctx0, kv_state->get_r_l(il), hparams.n_embd_r()*n_seqs, hparams.n_embd_r()*kv_head*ggml_element_size(kv_state->get_r_l(il)))
ggml_view_1d(ctx0, mctx_cur->get_r_l(il), hparams.n_embd_r()*n_seqs, hparams.n_embd_r()*kv_head*ggml_element_size(mctx_cur->get_r_l(il)))
);
}

41
src/llama-graph.h
View File

@ -17,12 +17,12 @@ struct ggml_tensor;
struct llama_ubatch;
struct llama_cparams;
struct llama_memory_state_i;
struct llama_memory_context_i;
class llama_kv_cache_unified_state;
class llama_kv_cache_unified_iswa_state;
class llama_memory_recurrent_state;
class llama_memory_hybrid_state;
class llama_kv_cache_unified_context;
class llama_kv_cache_unified_iswa_context;
class llama_memory_recurrent_context;
class llama_memory_hybrid_context;
// certain models (typically multi-modal) can produce different types of graphs
enum llm_graph_type {
@ -136,7 +136,7 @@ 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_state * kv_state) : hparams(hparams), kv_state(kv_state) {}
const llama_kv_cache_unified_context * mctx) : hparams(hparams), mctx(mctx) {}
virtual ~llm_graph_input_pos_bucket_kv() = default;
void set_input(const llama_ubatch * ubatch) override;
@ -144,7 +144,8 @@ public:
ggml_tensor * pos_bucket = nullptr; // I32 [n_kv, n_batch]
const llama_hparams & hparams;
const llama_kv_cache_unified_state * kv_state;
const llama_kv_cache_unified_context * mctx;
};
class llm_graph_input_out_ids : public llm_graph_input_i {
@ -191,14 +192,14 @@ public:
class llm_graph_input_rs : public llm_graph_input_i {
public:
llm_graph_input_rs(const llama_memory_recurrent_state * mem_state) : mem_state(mem_state) {}
llm_graph_input_rs(const llama_memory_recurrent_context * mctx) : mctx(mctx) {}
virtual ~llm_graph_input_rs() = default;
void set_input(const llama_ubatch * ubatch) override;
ggml_tensor * s_copy; // I32 [kv_size]
const llama_memory_recurrent_state * mem_state;
const llama_memory_recurrent_context * mctx;
};
class llm_graph_input_cross_embd : public llm_graph_input_i {
@ -238,10 +239,10 @@ public:
llm_graph_input_attn_kv_unified(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_unified_state * kv_state) :
const llama_kv_cache_unified_context * mctx) :
hparams(hparams),
cparams(cparams),
kv_state(kv_state) {
mctx(mctx) {
}
~llm_graph_input_attn_kv_unified() = default;
@ -255,7 +256,7 @@ public:
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_kv_cache_unified_state * kv_state;
const llama_kv_cache_unified_context * mctx;
};
class llm_graph_input_attn_kv_unified_iswa : public llm_graph_input_i {
@ -263,10 +264,10 @@ public:
llm_graph_input_attn_kv_unified_iswa(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_kv_cache_unified_iswa_state * kv_state) :
const llama_kv_cache_unified_iswa_context * mctx) :
hparams(hparams),
cparams(cparams),
kv_state(kv_state) {
mctx(mctx) {
}
~llm_graph_input_attn_kv_unified_iswa() = default;
@ -283,7 +284,7 @@ public:
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_kv_cache_unified_iswa_state * kv_state;
const llama_kv_cache_unified_iswa_context * mctx;
};
class llm_graph_input_attn_cross : public llm_graph_input_i {
@ -306,10 +307,10 @@ public:
llm_graph_input_mem_hybrid(
const llama_hparams & hparams,
const llama_cparams & cparams,
const llama_memory_hybrid_state * mem_state) :
const llama_memory_hybrid_context * mctx) :
hparams(hparams),
cparams(cparams),
mem_state(mem_state) {
mctx(mctx) {
}
virtual ~llm_graph_input_mem_hybrid() = default;
@ -325,7 +326,7 @@ public:
const llama_hparams & hparams;
const llama_cparams & cparams;
const llama_memory_hybrid_state * mem_state;
const llama_memory_hybrid_context * mctx;
};
//
@ -403,7 +404,7 @@ struct llm_graph_params {
const llama_adapter_cvec * cvec;
const llama_adapter_loras * loras;
const llama_memory_state_i * mstate;
const llama_memory_context_i * mctx;
const llama_cross * cross;
uint32_t n_outputs;
@ -455,7 +456,7 @@ struct llm_graph_context {
const llama_adapter_cvec * cvec;
const llama_adapter_loras * loras;
const llama_memory_state_i * mstate;
const llama_memory_context_i * mctx;
const llama_cross * cross;
const llm_graph_cb & cb_func;

70
src/llama-kv-cache-unified-iswa.cpp
View File

@ -95,7 +95,7 @@ llama_pos llama_kv_cache_unified_iswa::seq_pos_max(llama_seq_id seq_id) const {
return kv_swa->seq_pos_max(seq_id);
}
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
llama_memory_context_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
GGML_UNUSED(embd_all);
// first try simple split
@ -125,7 +125,7 @@ llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_alloc
assert(heads_base.size() == heads_swa.size());
return std::make_unique<llama_kv_cache_unified_iswa_state>(
return std::make_unique<llama_kv_cache_unified_iswa_context>(
this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
} while (false);
@ -156,22 +156,22 @@ llama_memory_state_ptr llama_kv_cache_unified_iswa::init_batch(llama_batch_alloc
assert(heads_base.size() == heads_swa.size());
return std::make_unique<llama_kv_cache_unified_iswa_state>(
return std::make_unique<llama_kv_cache_unified_iswa_context>(
this, std::move(heads_base), std::move(heads_swa), std::move(ubatches));
} while (false);
// TODO: if we fail again, we should attempt different splitting strategies
// but to do that properly, we first have to refactor the batches to be more flexible
return std::make_unique<llama_kv_cache_unified_iswa_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_kv_cache_unified_iswa_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_full() {
return std::make_unique<llama_kv_cache_unified_iswa_state>(this);
llama_memory_context_ptr llama_kv_cache_unified_iswa::init_full() {
return std::make_unique<llama_kv_cache_unified_iswa_context>(this);
}
llama_memory_state_ptr llama_kv_cache_unified_iswa::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_kv_cache_unified_iswa_state>(this, lctx, optimize);
llama_memory_context_ptr llama_kv_cache_unified_iswa::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_kv_cache_unified_iswa_context>(this, lctx, optimize);
}
bool llama_kv_cache_unified_iswa::get_can_shift() const {
@ -197,46 +197,46 @@ llama_kv_cache_unified * llama_kv_cache_unified_iswa::get_swa() const {
}
//
// llama_kv_cache_unified_iswa_state
// llama_kv_cache_unified_iswa_context
//
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(llama_memory_status status) : status(status) {}
llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(llama_memory_status status) : status(status) {}
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv) :
state_base(kv->get_base()->init_full()),
state_swa (kv->get_swa ()->init_full()),
status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) {
ctx_base(kv->get_base()->init_full()),
ctx_swa (kv->get_swa ()->init_full()),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
llama_context * lctx,
bool optimize) :
state_base(kv->get_base()->init_update(lctx, optimize)),
state_swa (kv->get_swa ()->init_update(lctx, optimize)),
status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) {
ctx_base(kv->get_base()->init_update(lctx, optimize)),
ctx_swa (kv->get_swa ()->init_update(lctx, optimize)),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}
llama_kv_cache_unified_iswa_state::llama_kv_cache_unified_iswa_state(
llama_kv_cache_unified_iswa_context::llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
std::vector<uint32_t> heads_base,
std::vector<uint32_t> heads_swa,
std::vector<llama_ubatch> ubatches) :
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
state_base(new llama_kv_cache_unified_state(kv->get_base(), std::move(heads_base), this->ubatches)),
state_swa (new llama_kv_cache_unified_state(kv->get_swa (), std::move(heads_swa), this->ubatches)),
status(llama_memory_status_combine(state_base->get_status(), state_swa->get_status())) {
ctx_base(new llama_kv_cache_unified_context(kv->get_base(), std::move(heads_base), this->ubatches)),
ctx_swa (new llama_kv_cache_unified_context(kv->get_swa (), std::move(heads_swa), this->ubatches)),
status(llama_memory_status_combine(ctx_base->get_status(), ctx_swa->get_status())) {
}
llama_kv_cache_unified_iswa_state:: ~llama_kv_cache_unified_iswa_state() = default;
llama_kv_cache_unified_iswa_context:: ~llama_kv_cache_unified_iswa_context() = default;
bool llama_kv_cache_unified_iswa_state::next() {
bool llama_kv_cache_unified_iswa_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
state_base->next();
state_swa ->next();
ctx_base->next();
ctx_swa ->next();
if (++i_next >= ubatches.size()) {
return false;
@ -245,35 +245,35 @@ bool llama_kv_cache_unified_iswa_state::next() {
return true;
}
bool llama_kv_cache_unified_iswa_state::apply() {
bool llama_kv_cache_unified_iswa_context::apply() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
bool res = true;
res = res & state_base->apply();
res = res & state_swa ->apply();
res = res & ctx_base->apply();
res = res & ctx_swa ->apply();
return res;
}
llama_memory_status llama_kv_cache_unified_iswa_state::get_status() const {
llama_memory_status llama_kv_cache_unified_iswa_context::get_status() const {
return status;
}
const llama_ubatch & llama_kv_cache_unified_iswa_state::get_ubatch() const {
const llama_ubatch & llama_kv_cache_unified_iswa_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_base() const {
const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_base() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return static_cast<const llama_kv_cache_unified_state *>(state_base.get());
return static_cast<const llama_kv_cache_unified_context *>(ctx_base.get());
}
const llama_kv_cache_unified_state * llama_kv_cache_unified_iswa_state::get_swa() const {
const llama_kv_cache_unified_context * llama_kv_cache_unified_iswa_context::get_swa() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return static_cast<const llama_kv_cache_unified_state *>(state_swa.get());
return static_cast<const llama_kv_cache_unified_context *>(ctx_swa.get());
}

36
src/llama-kv-cache-unified-iswa.h
View File

@ -31,14 +31,14 @@ public:
// llama_memory_i
//
llama_memory_state_ptr init_batch(
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_state_ptr init_full() override;
llama_memory_context_ptr init_full() override;
llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
@ -72,32 +72,32 @@ private:
std::unique_ptr<llama_kv_cache_unified> kv_swa;
};
class llama_kv_cache_unified_iswa_state : public llama_memory_state_i {
class llama_kv_cache_unified_iswa_context : public llama_memory_context_i {
public:
// used for errors
llama_kv_cache_unified_iswa_state(llama_memory_status status);
llama_kv_cache_unified_iswa_context(llama_memory_status status);
// used to create a full-cache state
llama_kv_cache_unified_iswa_state(
// used to create a full-cache context
llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv);
// used to create an update state
llama_kv_cache_unified_iswa_state(
// used to create an update context
llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
llama_context * lctx,
bool optimize);
// used to create a state from a batch
llama_kv_cache_unified_iswa_state(
// used to create a batch processing context from a batch
llama_kv_cache_unified_iswa_context(
llama_kv_cache_unified_iswa * kv,
std::vector<uint32_t> heads_base,
std::vector<uint32_t> heads_swa,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_unified_iswa_state();
virtual ~llama_kv_cache_unified_iswa_context();
//
// llama_memory_state_i
// llama_memory_context_i
//
bool next() override;
@ -107,11 +107,11 @@ public:
const llama_ubatch & get_ubatch() const override;
//
// llama_kv_cache_unified_iswa_state specific API
// llama_kv_cache_unified_iswa_context specific API
//
const llama_kv_cache_unified_state * get_base() const;
const llama_kv_cache_unified_state * get_swa() const;
const llama_kv_cache_unified_context * get_base() const;
const llama_kv_cache_unified_context * get_swa() const;
private:
//llama_kv_cache_unified_iswa * kv;
@ -121,8 +121,8 @@ private:
std::vector<llama_ubatch> ubatches;
const llama_memory_state_ptr state_base;
const llama_memory_state_ptr state_swa;
const llama_memory_context_ptr ctx_base;
const llama_memory_context_ptr ctx_swa;
const llama_memory_status status;
};

50
src/llama-kv-cache-unified.cpp
View File

@ -307,7 +307,7 @@ llama_pos llama_kv_cache_unified::seq_pos_max(llama_seq_id seq_id) const {
return cells.seq_pos_max(seq_id);
}
llama_memory_state_ptr llama_kv_cache_unified::init_batch(
llama_memory_context_ptr llama_kv_cache_unified::init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) {
@ -332,18 +332,18 @@ llama_memory_state_ptr llama_kv_cache_unified::init_batch(
break;
}
return std::make_unique<llama_kv_cache_unified_state>(
return std::make_unique<llama_kv_cache_unified_context>(
this, std::move(heads), std::move(ubatches));
} while (false);
return std::make_unique<llama_kv_cache_unified_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_kv_cache_unified_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
llama_memory_state_ptr llama_kv_cache_unified::init_full() {
return std::make_unique<llama_kv_cache_unified_state>(this);
llama_memory_context_ptr llama_kv_cache_unified::init_full() {
return std::make_unique<llama_kv_cache_unified_context>(this);
}
llama_memory_state_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) {
llama_memory_context_ptr llama_kv_cache_unified::init_update(llama_context * lctx, bool optimize) {
bool do_shift = get_has_shift();
defrag_info dinfo;
@ -373,7 +373,7 @@ llama_memory_state_ptr llama_kv_cache_unified::init_update(llama_context * lctx,
}
}
return std::make_unique<llama_kv_cache_unified_state>(this, lctx, do_shift, std::move(dinfo));
return std::make_unique<llama_kv_cache_unified_context>(this, lctx, do_shift, std::move(dinfo));
}
llama_kv_cache_unified::ubatch_heads llama_kv_cache_unified::prepare(const std::vector<llama_ubatch> & ubatches) {
@ -1710,18 +1710,18 @@ bool llama_kv_cache_unified::state_read_data(llama_io_read_i & io, uint32_t cell
}
//
// llama_kv_cache_unified_state
// llama_kv_cache_unified_context
//
llama_kv_cache_unified_state::llama_kv_cache_unified_state(llama_memory_status status) : status(status) {}
llama_kv_cache_unified_context::llama_kv_cache_unified_context(llama_memory_status status) : status(status) {}
llama_kv_cache_unified_state::llama_kv_cache_unified_state(
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv) {
n_kv = kv->get_size();
head = 0;
}
llama_kv_cache_unified_state::llama_kv_cache_unified_state(
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_context * lctx,
bool do_shift,
@ -1731,15 +1731,15 @@ llama_kv_cache_unified_state::llama_kv_cache_unified_state(
}
}
llama_kv_cache_unified_state::llama_kv_cache_unified_state(
llama_kv_cache_unified_context::llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_kv_cache_unified::ubatch_heads heads,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), kv(kv), heads(std::move(heads)), ubatches(std::move(ubatches)) {
}
llama_kv_cache_unified_state::~llama_kv_cache_unified_state() = default;
llama_kv_cache_unified_context::~llama_kv_cache_unified_context() = default;
bool llama_kv_cache_unified_state::next() {
bool llama_kv_cache_unified_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
if (++i_next >= ubatches.size()) {
@ -1749,7 +1749,7 @@ bool llama_kv_cache_unified_state::next() {
return true;
}
bool llama_kv_cache_unified_state::apply() {
bool llama_kv_cache_unified_context::apply() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
// no ubatches -> this is a KV cache update
@ -1767,45 +1767,45 @@ bool llama_kv_cache_unified_state::apply() {
return true;
}
llama_memory_status llama_kv_cache_unified_state::get_status() const {
llama_memory_status llama_kv_cache_unified_context::get_status() const {
return status;
}
const llama_ubatch & llama_kv_cache_unified_state::get_ubatch() const {
const llama_ubatch & llama_kv_cache_unified_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
uint32_t llama_kv_cache_unified_state::get_n_kv() const {
uint32_t llama_kv_cache_unified_context::get_n_kv() const {
return n_kv;
}
ggml_tensor * llama_kv_cache_unified_state::get_k(ggml_context * ctx, int32_t il) const {
ggml_tensor * llama_kv_cache_unified_context::get_k(ggml_context * ctx, int32_t il) const {
return kv->get_k(ctx, il, n_kv);
}
ggml_tensor * llama_kv_cache_unified_state::get_v(ggml_context * ctx, int32_t il) const {
ggml_tensor * llama_kv_cache_unified_context::get_v(ggml_context * ctx, int32_t il) const {
return kv->get_v(ctx, il, n_kv);
}
ggml_tensor * llama_kv_cache_unified_state::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
ggml_tensor * llama_kv_cache_unified_context::cpy_k(ggml_context * ctx, ggml_tensor * k_cur, int32_t il) const {
return kv->cpy_k(ctx, k_cur, il, head);
}
ggml_tensor * llama_kv_cache_unified_state::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
ggml_tensor * llama_kv_cache_unified_context::cpy_v(ggml_context * ctx, ggml_tensor * v_cur, int32_t il) const {
return kv->cpy_v(ctx, v_cur, il, head);
}
void llama_kv_cache_unified_state::set_input_k_shift(ggml_tensor * dst) const {
void llama_kv_cache_unified_context::set_input_k_shift(ggml_tensor * dst) const {
kv->set_input_k_shift(dst);
}
void llama_kv_cache_unified_state::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
void llama_kv_cache_unified_context::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * ubatch, bool causal_attn) const {
kv->set_input_kq_mask(dst, ubatch, causal_attn);
}
void llama_kv_cache_unified_state::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
void llama_kv_cache_unified_context::set_input_pos_bucket(ggml_tensor * dst, const llama_ubatch * ubatch) const {
kv->set_input_pos_bucket(dst, ubatch);
}

32
src/llama-kv-cache-unified.h
View File

@ -56,14 +56,14 @@ public:
// llama_memory_i
//
llama_memory_state_ptr init_batch(
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_state_ptr init_full() override;
llama_memory_context_ptr init_full() override;
llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
@ -208,36 +208,36 @@ private:
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
};
class llama_kv_cache_unified_state : public llama_memory_state_i {
class llama_kv_cache_unified_context : public llama_memory_context_i {
public:
// some shorthands
using ubatch_heads = llama_kv_cache_unified::ubatch_heads;
using defrag_info = llama_kv_cache_unified::defrag_info;
// used for errors
llama_kv_cache_unified_state(llama_memory_status status);
llama_kv_cache_unified_context(llama_memory_status status);
// used to create a full-cache state
llama_kv_cache_unified_state(
// used to create a full-cache context
llama_kv_cache_unified_context(
llama_kv_cache_unified * kv);
// used to create an update state
llama_kv_cache_unified_state(
// used to create an update context
llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
llama_context * lctx,
bool do_shift,
defrag_info dinfo);
// used to create a decode state from a batch
llama_kv_cache_unified_state(
// used to create a batch procesing context from a batch
llama_kv_cache_unified_context(
llama_kv_cache_unified * kv,
ubatch_heads heads,
std::vector<llama_ubatch> ubatches);
virtual ~llama_kv_cache_unified_state();
virtual ~llama_kv_cache_unified_context();
//
// llama_memory_state_i
// llama_memory_context_i
//
bool next() override;
@ -247,7 +247,7 @@ public:
const llama_ubatch & get_ubatch() const override;
//
// llama_kv_cache_unified_state specific API
// llama_kv_cache_unified_context specific API
//
uint32_t get_n_kv() const;
@ -272,7 +272,7 @@ private:
llama_context * lctx;
//
// update state
// update context
//
bool do_shift = false;
@ -280,7 +280,7 @@ private:
defrag_info dinfo;
//
// batch processing state
// batch processing context
//
// the index of the next ubatch to process

70
src/llama-memory-hybrid.cpp
View File

@ -56,7 +56,7 @@ llama_memory_hybrid::llama_memory_hybrid(
n_seq_max
)) {}
llama_memory_state_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
llama_memory_context_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
do {
balloc.split_reset();
@ -82,31 +82,31 @@ llama_memory_state_ptr llama_memory_hybrid::init_batch(llama_batch_allocr & ball
// prepare the recurrent batches first
if (!mem_recr->prepare(ubatches)) {
// TODO: will the recurrent cache be in an undefined state at this point?
// TODO: will the recurrent cache be in an undefined context at this point?
LLAMA_LOG_ERROR("%s: failed to prepare recurrent ubatches\n", __func__);
return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
// prepare the attention cache
auto heads_attn = mem_attn->prepare(ubatches);
if (heads_attn.empty()) {
LLAMA_LOG_ERROR("%s: failed to prepare attention ubatches\n", __func__);
return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
return std::make_unique<llama_memory_hybrid_state>(
return std::make_unique<llama_memory_hybrid_context>(
this, std::move(heads_attn), std::move(ubatches));
} while(false);
return std::make_unique<llama_memory_hybrid_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_memory_hybrid_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
llama_memory_state_ptr llama_memory_hybrid::init_full() {
return std::make_unique<llama_memory_hybrid_state>(this);
llama_memory_context_ptr llama_memory_hybrid::init_full() {
return std::make_unique<llama_memory_hybrid_context>(this);
}
llama_memory_state_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_memory_hybrid_state>(this, lctx, optimize);
llama_memory_context_ptr llama_memory_hybrid::init_update(llama_context * lctx, bool optimize) {
return std::make_unique<llama_memory_hybrid_context>(this, lctx, optimize);
}
bool llama_memory_hybrid::get_can_shift() const {
@ -176,39 +176,39 @@ llama_memory_recurrent * llama_memory_hybrid::get_mem_recr() const {
return mem_recr.get();
}
llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_status status) : status(status) {}
llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_status status) : status(status) {}
llama_memory_hybrid_state::llama_memory_hybrid_state(llama_memory_hybrid * mem) :
state_attn(mem->get_mem_attn()->init_full()),
state_recr(mem->get_mem_recr()->init_full()),
status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
llama_memory_hybrid_context::llama_memory_hybrid_context(llama_memory_hybrid * mem) :
ctx_attn(mem->get_mem_attn()->init_full()),
ctx_recr(mem->get_mem_recr()->init_full()),
status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
llama_memory_hybrid_state::llama_memory_hybrid_state(
llama_memory_hybrid_context::llama_memory_hybrid_context(
llama_memory_hybrid * mem,
llama_context * lctx,
bool optimize) :
state_attn(mem->get_mem_attn()->init_update(lctx, optimize)),
state_recr(mem->get_mem_recr()->init_update(lctx, optimize)),
status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
ctx_attn(mem->get_mem_attn()->init_update(lctx, optimize)),
ctx_recr(mem->get_mem_recr()->init_update(lctx, optimize)),
status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
llama_memory_hybrid_state::llama_memory_hybrid_state(
llama_memory_hybrid_context::llama_memory_hybrid_context(
llama_memory_hybrid * mem,
std::vector<uint32_t> heads_attn,
std::vector<llama_ubatch> ubatches) :
ubatches(std::move(ubatches)),
// note: here we copy the ubatches. not sure if this is ideal
state_attn(new llama_kv_cache_unified_state(mem->get_mem_attn(), std::move(heads_attn), this->ubatches)),
state_recr(new llama_memory_recurrent_state(mem->get_mem_recr(), this->ubatches)),
status(llama_memory_status_combine(state_attn->get_status(), state_recr->get_status())) {
ctx_attn(new llama_kv_cache_unified_context(mem->get_mem_attn(), std::move(heads_attn), this->ubatches)),
ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
}
bool llama_memory_hybrid_state::next() {
bool llama_memory_hybrid_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
state_attn->next();
state_recr->next();
ctx_attn->next();
ctx_recr->next();
if (++i_next >= ubatches.size()) {
return false;
@ -217,30 +217,30 @@ bool llama_memory_hybrid_state::next() {
return true;
}
bool llama_memory_hybrid_state::apply() {
bool llama_memory_hybrid_context::apply() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
bool res = true;
res = res & state_attn->apply();
res = res & state_recr->apply();
res = res & ctx_attn->apply();
res = res & ctx_recr->apply();
return res;
}
llama_memory_status llama_memory_hybrid_state::get_status() const {
llama_memory_status llama_memory_hybrid_context::get_status() const {
return status;
}
const llama_ubatch & llama_memory_hybrid_state::get_ubatch() const {
const llama_ubatch & llama_memory_hybrid_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
const llama_kv_cache_unified_state * llama_memory_hybrid_state::get_state_attn() const {
return static_cast<const llama_kv_cache_unified_state *>(state_attn.get());
const llama_kv_cache_unified_context * llama_memory_hybrid_context::get_attn() const {
return static_cast<const llama_kv_cache_unified_context *>(ctx_attn.get());
}
const llama_memory_recurrent_state * llama_memory_hybrid_state::get_state_recr() const {
return static_cast<const llama_memory_recurrent_state *>(state_recr.get());
const llama_memory_recurrent_context * llama_memory_hybrid_context::get_recr() const {
return static_cast<const llama_memory_recurrent_context *>(ctx_recr.get());
}

28
src/llama-memory-hybrid.h
View File

@ -49,14 +49,14 @@ public:
// llama_memory_i
//
llama_memory_state_ptr init_batch(
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_state_ptr init_full() override;
llama_memory_context_ptr init_full() override;
llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
bool get_can_shift() const override;
@ -90,27 +90,27 @@ private:
const std::unique_ptr<llama_memory_recurrent> mem_recr;
};
class llama_memory_hybrid_state : public llama_memory_state_i {
class llama_memory_hybrid_context : public llama_memory_context_i {
public:
// init failure
explicit llama_memory_hybrid_state(llama_memory_status status);
explicit llama_memory_hybrid_context(llama_memory_status status);
// init full
explicit llama_memory_hybrid_state(llama_memory_hybrid * mem);
explicit llama_memory_hybrid_context(llama_memory_hybrid * mem);
// init update
explicit llama_memory_hybrid_state(
explicit llama_memory_hybrid_context(
llama_memory_hybrid * mem,
llama_context * lctx,
bool optimize);
// init success
llama_memory_hybrid_state(
llama_memory_hybrid_context(
llama_memory_hybrid * mem,
std::vector<uint32_t> heads_attn,
std::vector<llama_ubatch> ubatches);
~llama_memory_hybrid_state() = default;
~llama_memory_hybrid_context() = default;
bool next() override;
bool apply() override;
@ -119,11 +119,11 @@ public:
const llama_ubatch & get_ubatch() const override;
//
// llama_memory_hybrid_state
// llama_memory_hybrid_context
//
const llama_kv_cache_unified_state * get_state_attn() const;
const llama_memory_recurrent_state * get_state_recr() const;
const llama_kv_cache_unified_context * get_attn() const;
const llama_memory_recurrent_context * get_recr() const;
private:
// the index of the next ubatch to process
@ -131,8 +131,8 @@ private:
std::vector<llama_ubatch> ubatches;
const llama_memory_state_ptr state_attn;
const llama_memory_state_ptr state_recr;
const llama_memory_context_ptr ctx_attn;
const llama_memory_context_ptr ctx_recr;
const llama_memory_status status;
};

46
src/llama-memory-recurrent.cpp
View File

@ -362,7 +362,7 @@ llama_pos llama_memory_recurrent::seq_pos_max(llama_seq_id seq_id) const {
return result;
}
llama_memory_state_ptr llama_memory_recurrent::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
llama_memory_context_ptr llama_memory_recurrent::init_batch(llama_batch_allocr & balloc, uint32_t n_ubatch, bool embd_all) {
std::vector<llama_ubatch> ubatches;
while (true) {
@ -383,21 +383,21 @@ llama_memory_state_ptr llama_memory_recurrent::init_batch(llama_batch_allocr & b
}
if (!prepare(ubatches)) {
return std::make_unique<llama_memory_recurrent_state>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
return std::make_unique<llama_memory_recurrent_context>(LLAMA_MEMORY_STATUS_FAILED_PREPARE);
}
return std::make_unique<llama_memory_recurrent_state>(this, std::move(ubatches));
return std::make_unique<llama_memory_recurrent_context>(this, std::move(ubatches));
}
llama_memory_state_ptr llama_memory_recurrent::init_full() {
return std::make_unique<llama_memory_recurrent_state>(this);
llama_memory_context_ptr llama_memory_recurrent::init_full() {
return std::make_unique<llama_memory_recurrent_context>(this);
}
llama_memory_state_ptr llama_memory_recurrent::init_update(llama_context * lctx, bool optimize) {
llama_memory_context_ptr llama_memory_recurrent::init_update(llama_context * lctx, bool optimize) {
GGML_UNUSED(lctx);
GGML_UNUSED(optimize);
return std::make_unique<llama_memory_recurrent_state>(LLAMA_MEMORY_STATUS_NO_UPDATE);
return std::make_unique<llama_memory_recurrent_context>(LLAMA_MEMORY_STATUS_NO_UPDATE);
}
bool llama_memory_recurrent::prepare(const std::vector<llama_ubatch> & ubatches) {
@ -1040,22 +1040,22 @@ bool llama_memory_recurrent::state_read_data(llama_io_read_i & io, uint32_t cell
}
//
// llama_memory_recurrent_state
// llama_memory_recurrent_context
//
llama_memory_recurrent_state::llama_memory_recurrent_state(llama_memory_status status) : status(status) {}
llama_memory_recurrent_context::llama_memory_recurrent_context(llama_memory_status status) : status(status) {}
llama_memory_recurrent_state::llama_memory_recurrent_state(
llama_memory_recurrent_context::llama_memory_recurrent_context(
llama_memory_recurrent * mem) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), is_full(true) {
}
llama_memory_recurrent_state::llama_memory_recurrent_state(
llama_memory_recurrent_context::llama_memory_recurrent_context(
llama_memory_recurrent * mem,
std::vector<llama_ubatch> ubatches) : status(LLAMA_MEMORY_STATUS_SUCCESS), mem(mem), ubatches(std::move(ubatches)) {}
llama_memory_recurrent_state::~llama_memory_recurrent_state() = default;
llama_memory_recurrent_context::~llama_memory_recurrent_context() = default;
bool llama_memory_recurrent_state::next() {
bool llama_memory_recurrent_context::next() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
if (++i_next >= ubatches.size()) {
@ -1065,7 +1065,7 @@ bool llama_memory_recurrent_state::next() {
return true;
}
bool llama_memory_recurrent_state::apply() {
bool llama_memory_recurrent_context::apply() {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
mem->find_slot(ubatches[i_next]);
@ -1073,40 +1073,40 @@ bool llama_memory_recurrent_state::apply() {
return true;
}
llama_memory_status llama_memory_recurrent_state::get_status() const {
llama_memory_status llama_memory_recurrent_context::get_status() const {
return status;
}
const llama_ubatch & llama_memory_recurrent_state::get_ubatch() const {
const llama_ubatch & llama_memory_recurrent_context::get_ubatch() const {
assert(status == LLAMA_MEMORY_STATUS_SUCCESS);
return ubatches[i_next];
}
uint32_t llama_memory_recurrent_state::get_n_rs() const {
uint32_t llama_memory_recurrent_context::get_n_rs() const {
return is_full ? mem->size : mem->n;
}
uint32_t llama_memory_recurrent_state::get_head() const {
uint32_t llama_memory_recurrent_context::get_head() const {
return is_full ? 0 : mem->head;
}
int32_t llama_memory_recurrent_state::get_rs_z() const {
int32_t llama_memory_recurrent_context::get_rs_z() const {
return is_full ? 0 : mem->rs_z;
}
uint32_t llama_memory_recurrent_state::get_size() const {
uint32_t llama_memory_recurrent_context::get_size() const {
return mem->size;
}
ggml_tensor * llama_memory_recurrent_state::get_r_l(int32_t il) const {
ggml_tensor * llama_memory_recurrent_context::get_r_l(int32_t il) const {
return mem->r_l[il];
}
ggml_tensor * llama_memory_recurrent_state::get_s_l(int32_t il) const {
ggml_tensor * llama_memory_recurrent_context::get_s_l(int32_t il) const {
return mem->s_l[il];
}
int32_t llama_memory_recurrent_state::s_copy(int i) const {
int32_t llama_memory_recurrent_context::s_copy(int i) const {
return mem->cells[i + mem->head].src0;
}

28
src/llama-memory-recurrent.h
View File

@ -11,8 +11,8 @@
// llama_memory_recurrent
//
// TODO: extract the cache state used for graph computation into llama_memory_recurrent_state_i
// see the implementation of llama_kv_cache_unified_state_i for an example how to do it
// TODO: extract the cache state used for graph computation into llama_memory_recurrent_context_i
// see the implementation of llama_kv_cache_unified_context_i for an example how to do it
class llama_memory_recurrent : public llama_memory_i {
public:
@ -34,14 +34,14 @@ public:
// llama_memory_i
//
llama_memory_state_ptr init_batch(
llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) override;
llama_memory_state_ptr init_full() override;
llama_memory_context_ptr init_full() override;
llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) override;
llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) override;
void clear(bool data) override;
@ -125,24 +125,24 @@ private:
bool state_read_data(llama_io_read_i & io, uint32_t cell_count);
};
class llama_memory_recurrent_state : public llama_memory_state_i {
class llama_memory_recurrent_context : public llama_memory_context_i {
public:
// used for errors
llama_memory_recurrent_state(llama_memory_status status);
llama_memory_recurrent_context(llama_memory_status status);
// used to create a full-cache state
llama_memory_recurrent_state(
// used to create a full-cache or update context
llama_memory_recurrent_context(
llama_memory_recurrent * mem);
// used to create a state from a batch
llama_memory_recurrent_state(
// used to create a batch processing context from a batch
llama_memory_recurrent_context(
llama_memory_recurrent * mem,
std::vector<llama_ubatch> ubatches);
virtual ~llama_memory_recurrent_state();
virtual ~llama_memory_recurrent_context();
//
// llama_memory_state_i
// llama_memory_context_i
//
bool next() override;
@ -152,7 +152,7 @@ public:
const llama_ubatch & get_ubatch() const override;
//
// llama_memory_recurrent_state specific API
// llama_memory_recurrent_context specific API
//
uint32_t get_n_rs() const;

33
src/llama-memory.h
View File

@ -3,7 +3,6 @@
#include "llama.h"
#include <memory>
#include <vector>
struct llama_ubatch;
@ -28,23 +27,21 @@ enum llama_memory_status {
LLAMA_MEMORY_STATUS_FAILED_COMPUTE,
};
// helper function for combining the status of two memory states
// helper function for combining the status of two memory contexts
// useful for implementing hybrid memory types (e.g. iSWA)
llama_memory_status llama_memory_status_combine(llama_memory_status s0, llama_memory_status s1);
// the interface for managing the memory state during batch processing
// the interface for managing the memory context during batch processing
// this interface is implemented per memory type. see:
// - llama_kv_cache_unified_state
// - llama_kv_cache_unified_iswa_state
// - llama_kv_cache_unified_context
// - llama_kv_cache_unified_iswa_context
// ...
//
// the only method that can mutate the memory and the memory state is llama_memory_i::apply()
//
// TODO: rename to llama_memory_context_i ?
struct llama_memory_state_i {
virtual ~llama_memory_state_i() = default;
// the only method that should mutate the memory and the memory context is llama_memory_i::apply()
struct llama_memory_context_i {
virtual ~llama_memory_context_i() = default;
// consume the current ubatch from the state and proceed to the next one
// consume the current ubatch from the context and proceed to the next one
// return false if we are done
virtual bool next() = 0;
@ -55,11 +52,11 @@ struct llama_memory_state_i {
// get the current ubatch
virtual const llama_ubatch & get_ubatch() const = 0;
// get the status of the memory state - used for error handling and checking if any updates would be applied
// get the status of the memory context - used for error handling and checking if any updates would be applied
virtual llama_memory_status get_status() const = 0;
};
using llama_memory_state_ptr = std::unique_ptr<llama_memory_state_i>;
using llama_memory_context_ptr = std::unique_ptr<llama_memory_context_i>;
// general concept of LLM memory
// the KV cache is a type of LLM memory, but there can be other types
@ -67,19 +64,19 @@ struct llama_memory_i {
virtual ~llama_memory_i() = default;
// split the input batch into a set of ubatches and verify that they can fit into the cache
// return a state object containing the ubatches and KV cache state required to process them
// check the llama_memory_state_i::get_status() for the result
virtual llama_memory_state_ptr init_batch(
// return a context object containing the ubatches and memory state required to process them
// check the llama_memory_context_i::get_status() for the result
virtual llama_memory_context_ptr init_batch(
llama_batch_allocr & balloc,
uint32_t n_ubatch,
bool embd_all) = 0;
// simulate full cache, used for allocating worst-case compute buffers
virtual llama_memory_state_ptr init_full() = 0;
virtual llama_memory_context_ptr init_full() = 0;
// prepare for any pending memory updates, such as shifts, defrags, etc.
// status == LLAMA_MEMORY_STATUS_NO_UPDATE if there is nothing to update
virtual llama_memory_state_ptr init_update(llama_context * lctx, bool optimize) = 0;
virtual llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) = 0;
// getters
virtual bool get_can_shift() const = 0;

28
src/llama-model.cpp
View File

@ -9171,9 +9171,9 @@ struct llm_build_mamba : public llm_graph_context {
ggml_tensor * cur,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto kv_head = kv_state->get_head();
const auto kv_head = mctx_cur->get_head();
const int64_t d_conv = hparams.ssm_d_conv;
const int64_t d_inner = hparams.ssm_d_inner;
@ -9191,8 +9191,8 @@ struct llm_build_mamba : public llm_graph_context {
GGML_ASSERT(ubatch.equal_seqs);
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
ggml_tensor * conv_states_all = kv_state->get_r_l(il);
ggml_tensor * ssm_states_all = kv_state->get_s_l(il);
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
ggml_tensor * ssm_states_all = mctx_cur->get_s_l(il);
// (ab)using the KV cache to store the states
ggml_tensor * conv = build_rs(
@ -11916,7 +11916,7 @@ struct llm_build_rwkv6_base : public llm_graph_context {
ggml_tensor * x_prev,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto n_tokens = ubatch.n_tokens;
const auto n_seqs = ubatch.n_seqs;
@ -11926,7 +11926,7 @@ struct llm_build_rwkv6_base : public llm_graph_context {
const auto n_head = n_embd / head_size;
const auto n_head_kv = hparams.n_head_kv(il);
const auto kv_head = kv_state->get_head();
const auto kv_head = mctx_cur->get_head();
const auto & layer = model.layers[il];
@ -12038,7 +12038,7 @@ struct llm_build_rwkv6_base : public llm_graph_context {
}
ggml_tensor * wkv_state = build_rs(
inp, gf, kv_state->get_s_l(il),
inp, gf, mctx_cur->get_s_l(il),
hparams.n_embd_s(), n_seqs);
ggml_tensor * wkv_output;
@ -12057,9 +12057,9 @@ struct llm_build_rwkv6_base : public llm_graph_context {
wkv_state,
ggml_view_1d(
ctx0,
kv_state->get_s_l(il),
mctx_cur->get_s_l(il),
hparams.n_embd_s() * n_seqs,
hparams.n_embd_s() * kv_head * ggml_element_size(kv_state->get_s_l(il))
hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il))
)
)
);
@ -12313,7 +12313,7 @@ struct llm_build_rwkv7_base : public llm_graph_context {
ggml_tensor *& first_layer_value,
const llama_ubatch & ubatch,
int il) const {
const auto * kv_state = static_cast<const llama_memory_recurrent_state *>(mstate);
const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
const auto n_tokens = ubatch.n_tokens;
const auto n_seqs = ubatch.n_seqs;
@ -12322,7 +12322,7 @@ struct llm_build_rwkv7_base : public llm_graph_context {
const auto head_count = n_embd / head_size;
const auto n_seq_tokens = ubatch.n_seq_tokens;
const auto kv_head = kv_state->get_head();
const auto kv_head = mctx_cur->get_head();
const auto & layer = model.layers[il];
@ -12393,7 +12393,7 @@ struct llm_build_rwkv7_base : public llm_graph_context {
a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens);
ggml_tensor * wkv_state = build_rs(
inp, gf, kv_state->get_s_l(il),
inp, gf, mctx_cur->get_s_l(il),
hparams.n_embd_s(), n_seqs);
ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state);
@ -12407,9 +12407,9 @@ struct llm_build_rwkv7_base : public llm_graph_context {
wkv_state,
ggml_view_1d(
ctx0,
kv_state->get_s_l(il),
mctx_cur->get_s_l(il),
hparams.n_embd_s() * n_seqs,
hparams.n_embd_s() * kv_head * ggml_element_size(kv_state->get_s_l(il))
hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il))
)
)
);