vrr/koboldcpp
2
0
Fork 0
mirror of https://github.com/LostRuins/koboldcpp.git synced 2025-09-11 09:34:37 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Merge branch 'master' into concedo_experimental

Browse source 
# Conflicts:
#	.devops/nix/package.nix
#	README.md
#	ggml-metal.m
#	llama.cpp
#	scripts/sync-ggml.last
#	tests/test-backend-ops.cpp
This commit is contained in:
Concedo 2024-03-05 15:54:05 +08:00
commit ac43e0115c
28 changed files with 1225 additions and 309 deletions
Download patch file Download diff file Expand all files Collapse all files

343
llama.cpp
View file

@ -1693,7 +1693,7 @@ struct llama_hparams {
};
struct llama_cparams {
uint32_t n_ctx; // context size used during inference
uint32_t n_ctx; // context size used during inference
uint32_t n_batch;
uint32_t n_threads; // number of threads to use for generation
uint32_t n_threads_batch; // number of threads to use for batch processing
@ -1710,7 +1710,9 @@ struct llama_cparams {
float yarn_beta_slow;
float defrag_thold;
bool embeddings;
bool offload_kqv;
enum llama_pooling_type pooling_type;
ggml_backend_sched_eval_callback cb_eval;
@ -2004,7 +2006,7 @@ struct llama_context {
int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
int32_t n_eval = 0; // number of eval calls
// decode output (2-dimensional array: [n_tokens][n_vocab])
// logits output (2-dimensional array: [n_tokens][n_vocab])
std::vector<float> logits;
#ifndef NDEBUG
// guard against access to unset logits
@ -2012,8 +2014,13 @@ struct llama_context {
#endif
bool logits_all = false;
// input embedding (1-dimensional array: [n_embd])
std::vector<float> embedding;
// embeddings output (2-dimensional array: [n_tokens][n_embd])
// populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
std::vector<float> embd;
// sequence embeddings output (map of [n_embd] vectors)
// populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
std::map<llama_seq_id, std::vector<float>> embd_seq;
// memory buffers used to evaluate the model
std::vector<uint8_t> buf_compute_meta;
@ -5164,6 +5171,7 @@ static struct ggml_tensor * llm_build_kv(
llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il);
struct ggml_tensor * cur;
cur = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b,
q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il);
cb(cur, "kqv_out", il);
@ -6157,6 +6165,7 @@ struct llm_build_context {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
struct ggml_tensor * cur;
@ -6164,9 +6173,10 @@ struct llm_build_context {
// get input vectors with right size
const size_t stride1 = n_tokens * ggml_type_size(lctx.inp_tokens->type);
struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
struct ggml_tensor * inp_mean = ggml_view_2d(ctx0, lctx.inp_mean, n_tokens, n_tokens, stride1, 0);
struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0);
struct ggml_tensor * inp_cls = ggml_view_1d(ctx0, lctx.inp_cls, n_tokens, 0);
// construct input embeddings (token, type, position)
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
@ -6184,39 +6194,38 @@ struct llm_build_context {
cb(inpL, "inp_norm", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
cb(KQ_mask, "KQ_mask", -1); // [n_kv, n_tokens]
struct ggml_tensor * KQ_mask = ggml_cont(ctx0, ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_tokens, n_tokens, n_tokens*ggml_type_size(lctx.inp_KQ_mask->type), 0));
cb(KQ_mask, "KQ_mask", -1); // [n_tokens, n_tokens]
// iterate layers
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * cur = inpL;
struct ggml_tensor * Qcur;
struct ggml_tensor * Kcur;
struct ggml_tensor * Vcur;
// self-attention
if (model.arch == LLM_ARCH_BERT) {
struct ggml_tensor * Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
cb(Vcur, "Vcur", il);
// seems like we just need to do this for Q?
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
} else {
// compute Q and K and RoPE them
cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
@ -6235,13 +6244,41 @@ struct llm_build_context {
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Kcur, "Kcur", il);
cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
model.layers[il].wo, model.layers[il].bo,
Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
cb(kq, "kq", il);
kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
cb(kq, "kq_soft_max_ext", il);
struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
cb(v, "v", il);
struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
cb(kqv, "kqv", il);
struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
cb(kqv_merged, "kqv_merged", il);
cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
cb(cur, "kqv_merged_cont", il);
ggml_build_forward_expand(gf, cur);
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
if (model.layers[il].bo) {
cb(cur, "kqv_wo", il);
}
if (model.layers[il].bo) {
cur = ggml_add(ctx0, cur, model.layers[il].bo);
}
cb(cur, "kqv_out", il);
// re-add the layer input
cur = ggml_add(ctx0, cur, inpL);
@ -6281,16 +6318,29 @@ struct llm_build_context {
// final output
cur = inpL;
cb(cur, "result_embd", -1);
// pooling layer
if (pooling_type == LLAMA_POOLING_TYPE_MEAN) {
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
} else if (pooling_type == LLAMA_POOLING_TYPE_CLS) {
cur = ggml_get_rows(ctx0, cur, inp_cls);
} else {
GGML_ASSERT(pooling_type == LLAMA_POOLING_TYPE_NONE && "Invalid pooling type");
switch (pooling_type) {
case LLAMA_POOLING_TYPE_NONE:
{
// nop
} break;
case LLAMA_POOLING_TYPE_MEAN:
{
cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, cur)), inp_mean);
cb(cur, "result_embd_pooled", -1);
} break;
case LLAMA_POOLING_TYPE_CLS:
{
cur = ggml_get_rows(ctx0, cur, inp_cls);
cb(cur, "result_embd_pooled", -1);
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
{
GGML_ASSERT(false && "Invalid pooling type");
} break;
}
cb(cur, "result_embd", -1);
ggml_build_forward_expand(gf, cur);
@ -8052,7 +8102,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
}
{
if (hparams.causal_attn) {
const int64_t n_kv = kv_self.n;
const int64_t n_tokens = batch.n_tokens;
@ -8067,16 +8117,40 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
for (int i = 0; i < n_kv; ++i) {
float f;
if (!lctx.kv_self.cells[i].has_seq_id(seq_id) ||
(hparams.causal_attn && lctx.kv_self.cells[i].pos > pos)) {
if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
f = -INFINITY;
} else {
f = 0;
f = 0.0f;
}
data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
}
}
}
} else {
// non-causal attention attends only the tokens within the batch (i.e. the KV cache is not used)
const int64_t n_tokens = batch.n_tokens;
assert(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
float * data = (float *) lctx.inp_KQ_mask->data;
for (int h = 0; h < 1; ++h) {
for (int j = 0; j < n_tokens; ++j) {
const llama_seq_id seq_id = batch.seq_id[j][0];
for (int i = 0; i < n_tokens; ++i) {
float f = -INFINITY;
for (int s = 0; s < batch.n_seq_id[i]; ++s) {
if (batch.seq_id[i][s] == seq_id) {
f = 0.0f;
break;
}
}
data[h*(n_tokens*n_tokens) + j*n_tokens + i] = f;
}
}
}
}
if (hparams.need_kq_pos) {
@ -8095,13 +8169,16 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
float * data = (float *) lctx.inp_mean->data;
float * data = (float *) lctx.inp_mean->data;
memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
std::vector<uint64_t> sum(n_tokens, 0);
for (int i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
sum[seq_id] += 1;
}
@ -8123,11 +8200,16 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
const int64_t n_tokens = batch.n_tokens;
GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
uint32_t * data = (uint32_t *) lctx.inp_cls->data;
memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
for (int i = 0; i < n_tokens; ++i) {
const llama_seq_id seq_id = batch.seq_id[i][0];
const llama_pos pos = batch.pos[i];
const llama_pos pos = batch.pos[i];
GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
if (pos == 0) {
data[seq_id] = i;
}
@ -8241,24 +8323,27 @@ static int llama_decode_internal(
batch.seq_id = seq_id_arr.data();
}
llama_kv_cache_update(&lctx);
// non-causal masks do not use the KV cache
if (hparams.causal_attn) {
llama_kv_cache_update(&lctx);
// if we have enough unused cells before the current head ->
// better to start searching from the beginning of the cache, hoping to fill it
if (kv_self.head > kv_self.used + 2*n_tokens) {
kv_self.head = 0;
// if we have enough unused cells before the current head ->
// better to start searching from the beginning of the cache, hoping to fill it
if (kv_self.head > kv_self.used + 2*n_tokens) {
kv_self.head = 0;
}
if (!llama_kv_cache_find_slot(kv_self, batch)) {
return 1;
}
// a heuristic, to avoid attending the full cache if it is not yet utilized
// after enough generations, the benefit from this heuristic disappears
// if we start defragmenting the cache, the benefit from this will be more important
kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
//kv_self.n = llama_kv_cache_cell_max(kv_self);
}
if (!llama_kv_cache_find_slot(kv_self, batch)) {
return 1;
}
// a heuristic, to avoid attending the full cache if it is not yet utilized
// after enough generations, the benefit from this heuristic disappears
// if we start defragmenting the cache, the benefit from this will be more important
kv_self.n = std::min(cparams.n_ctx, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));
//kv_self.n = llama_kv_cache_cell_max(kv_self);
//printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
ggml_backend_sched_reset(lctx.sched);
@ -8267,20 +8352,26 @@ static int llama_decode_internal(
ggml_cgraph * gf = llama_build_graph(lctx, batch, false);
// the output is always the last tensor in the graph
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
struct ggml_tensor * embeddings = gf->nodes[gf->n_nodes - 2];
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
if (strcmp(res->name, "result_output") == 0) {
// the embeddings could be the second to last tensor, or the third to last tensor
if (strcmp(embeddings->name, "result_norm") != 0) {
embeddings = gf->nodes[gf->n_nodes - 3];
GGML_ASSERT(strcmp(embeddings->name, "result_norm") == 0);
}
} else if (strcmp(res->name, "result_embd") == 0) {
embeddings = res;
res = nullptr;
if (!hparams.causal_attn) {
res = nullptr; // do not extract logits for embedding models such as BERT
// token or sequence embeddings
embd = gf->nodes[gf->n_nodes - 1];
GGML_ASSERT(strcmp(embd->name, "result_embd") == 0 || strcmp(embd->name, "result_embd_pooled") == 0);
} else {
GGML_ASSERT(false);
if (strcmp(res->name, "result_output") == 0) {
// the token embeddings could be the second to last tensor, or the third to last tensor
if (strcmp(embd->name, "result_norm") != 0) {
embd = gf->nodes[gf->n_nodes - 3];
GGML_ASSERT(strcmp(embd->name, "result_norm") == 0);
}
} else {
GGML_ASSERT(false && "missing result_output tensor");
}
}
// LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
@ -8347,46 +8438,82 @@ static int llama_decode_internal(
logits_out.clear();
#endif
ggml_backend_t res_backend = ggml_backend_sched_get_node_backend(lctx.sched, res);
GGML_ASSERT(res_backend != nullptr);
ggml_backend_t backend_res = ggml_backend_sched_get_node_backend(lctx.sched, res);
GGML_ASSERT(backend_res != nullptr);
if (batch.logits) {
logits_out.resize(n_vocab * n_tokens);
for (uint32_t i = 0; i < n_tokens; i++) {
if (batch.logits[i] == 0) {
continue;
}
ggml_backend_tensor_get_async(res_backend, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float));
ggml_backend_tensor_get_async(backend_res, res, logits_out.data() + (n_vocab*i), (n_vocab*i)*sizeof(float), n_vocab*sizeof(float));
#ifndef NDEBUG
logits_valid[i] = true;
#endif
}
} else if (lctx.logits_all) {
logits_out.resize(n_vocab * n_tokens);
ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float));
ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), 0, n_vocab*n_tokens*sizeof(float));
#ifndef NDEBUG
std::fill(logits_valid.begin(), logits_valid.end(), true);
#endif
} else {
logits_out.resize(n_vocab);
ggml_backend_tensor_get_async(res_backend, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float));
ggml_backend_tensor_get_async(backend_res, res, logits_out.data(), (n_vocab*(n_tokens - 1))*sizeof(float), n_vocab*sizeof(float));
#ifndef NDEBUG
logits_valid[0] = true;
#endif
}
ggml_backend_synchronize(res_backend);
ggml_backend_synchronize(backend_res);
}
// extract embeddings
if (!lctx.embedding.empty()) {
auto & embedding_out = lctx.embedding;
if (cparams.embeddings && embd) {
ggml_backend_t backend_embd = ggml_backend_sched_get_node_backend(lctx.sched, embd);
GGML_ASSERT(backend_embd != nullptr);
const int64_t embd_pos = res ? n_embd * (n_tokens-1) : 0;
const int64_t embd_size = res ? n_embd : n_embd * n_tokens;
switch (cparams.pooling_type) {
case LLAMA_POOLING_TYPE_NONE:
{
// extract token embeddings
auto & embd_out = lctx.embd;
embedding_out.resize(embd_size);
ggml_backend_t embeddings_backend = ggml_backend_sched_get_node_backend(lctx.sched, embeddings);
ggml_backend_tensor_get_async(embeddings_backend, embeddings, embedding_out.data(), embd_pos*sizeof(float), embd_size*sizeof(float));
ggml_backend_synchronize(embeddings_backend);
if (batch.logits) {
embd_out.resize(n_embd * n_tokens);
for (uint32_t i = 0; i < n_tokens; i++) {
if (batch.logits[i] == 0) {
continue;
}
ggml_backend_tensor_get_async(backend_embd, embd, embd_out.data() + (n_embd*i), (n_embd*i)*sizeof(float), n_embd*sizeof(float));
}
}
} break;
case LLAMA_POOLING_TYPE_CLS:
case LLAMA_POOLING_TYPE_MEAN:
{
GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0);
// extract sequence embeddings
auto & embd_seq_out = lctx.embd_seq;
embd_seq_out.clear();
for (uint32_t i = 0; i < n_tokens; i++) {
const llama_seq_id seq_id = batch.seq_id[i][0];
if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
continue;
}
embd_seq_out[seq_id].resize(n_embd);
ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
}
} break;
case LLAMA_POOLING_TYPE_UNSPECIFIED:
{
GGML_ASSERT(false && "unknown pooling type");
} break;
}
ggml_backend_synchronize(backend_embd);
}
// measure the performance only for the single-token evals
@ -12181,7 +12308,7 @@ struct llama_context_params llama_context_default_params() {
/*.type_k =*/ GGML_TYPE_F16,
/*.type_v =*/ GGML_TYPE_F16,
/*.logits_all =*/ false,
/*.embedding =*/ false,
/*.embeddings =*/ false,
/*.offload_kqv =*/ true,
/*.abort_callback =*/ nullptr,
/*.abort_callback_data =*/ nullptr,
@ -12322,6 +12449,7 @@ struct llama_context * llama_new_context_with_model(
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.defrag_thold = params.defrag_thold;
cparams.embeddings = params.embeddings;
cparams.offload_kqv = params.offload_kqv;
cparams.pooling_type = params.pooling_type;
@ -12499,8 +12627,8 @@ struct llama_context * llama_new_context_with_model(
// resized during inference, reserve maximum
ctx->logits.reserve(hparams.n_vocab*cparams.n_batch);
if (params.embedding) {
ctx->embedding.resize(hparams.n_embd);
if (params.embeddings) {
ctx->embd.reserve(hparams.n_embd*cparams.n_batch);
}
// graph inputs
@ -12935,7 +13063,7 @@ size_t llama_get_state_size(const struct llama_context * ctx) {
// assume worst case for logits although only currently set ones are serialized
const size_t s_logits = ctx->logits.capacity() * sizeof(float);
const size_t s_embedding_size = sizeof(size_t);
const size_t s_embedding = ctx->embedding.size() * sizeof(float);
const size_t s_embedding = ctx->embd.capacity() * sizeof(float);
const size_t s_kv_buf_size = sizeof(size_t);
const size_t s_kv_head = sizeof(uint32_t);
const size_t s_kv_size = sizeof(uint32_t);
@ -13044,12 +13172,12 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
// copy embeddings
{
const size_t embedding_size = ctx->embedding.size();
const size_t embeddings_size = ctx->embd.size();
data_ctx->write(&embedding_size, sizeof(embedding_size));
data_ctx->write(&embeddings_size, sizeof(embeddings_size));
if (embedding_size) {
data_ctx->write(ctx->embedding.data(), embedding_size * sizeof(float));
if (embeddings_size) {
data_ctx->write(ctx->embd.data(), embeddings_size * sizeof(float));
}
}
@ -13153,15 +13281,17 @@ size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
// set embeddings
{
size_t embedding_size;
size_t embeddings_size;
memcpy(&embedding_size, inp, sizeof(embedding_size)); inp += sizeof(embedding_size);
memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size);
GGML_ASSERT(ctx->embedding.capacity() == embedding_size);
GGML_ASSERT(ctx->embd.capacity() == embeddings_size);
if (embedding_size) {
memcpy(ctx->embedding.data(), inp, embedding_size * sizeof(float));
inp += embedding_size * sizeof(float);
if (embeddings_size) {
ctx->embd.resize(embeddings_size);
memcpy(ctx->embd.data(), inp, embeddings_size * sizeof(float));
inp += embeddings_size * sizeof(float);
}
}
@ -13421,11 +13551,20 @@ float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
}
float * llama_get_embeddings(struct llama_context * ctx) {
return ctx->embedding.data();
return ctx->embd.data();
}
float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
return ctx->embedding.data() + i*ctx->model.hparams.n_embd;
return ctx->embd.data() + i*ctx->model.hparams.n_embd;
}
float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) {
auto it = ctx->embd_seq.find(seq_id);
if (it == ctx->embd_seq.end()) {
return nullptr;
}
return it->second.data();
}
const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
@ -13604,7 +13743,7 @@ static int32_t llama_chat_apply_template_internal(
std::string & dest, bool add_ass) {
// Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
std::stringstream ss;
if (tmpl.find("<|im_start|>") != std::string::npos) {
if (tmpl == "chatml" || tmpl.find("<|im_start|>") != std::string::npos) {
// chatml template
for (auto message : chat) {
ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
@ -13612,7 +13751,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|im_start|>assistant\n";
}
} else if (tmpl.find("[INST]") != std::string::npos) {
} else if (tmpl == "llama2" || tmpl.find("[INST]") != std::string::npos) {
// llama2 template and its variants
// [variant] support system message
bool support_system_message = tmpl.find("<<SYS>>") != std::string::npos;
@ -13647,7 +13786,7 @@ static int32_t llama_chat_apply_template_internal(
}
}
// llama2 templates seem to not care about "add_generation_prompt"
} else if (tmpl.find("<|user|>") != std::string::npos) {
} else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) {
// zephyr template
for (auto message : chat) {
ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
@ -13655,7 +13794,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<|assistant|>\n";
}
} else if (tmpl.find("bos_token + message['role']") != std::string::npos) {
} else if (tmpl == "monarch" || tmpl.find("bos_token + message['role']") != std::string::npos) {
// mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
for (auto message : chat) {
std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
@ -13664,7 +13803,7 @@ static int32_t llama_chat_apply_template_internal(
if (add_ass) {
ss << "<s>assistant\n";
}
} else if (tmpl.find("<start_of_turn>") != std::string::npos) {
} else if (tmpl == "gemma" || tmpl.find("<start_of_turn>") != std::string::npos) {
// google/gemma-7b-it
std::string system_prompt = "";
for (auto message : chat) {
@ -13711,7 +13850,7 @@ LLAMA_API int32_t llama_chat_apply_template(
int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
if (res < 0) {
// worst case: there is no information about template, we will use chatml by default
curr_tmpl = "<|im_start|>"; // see llama_chat_apply_template_internal
curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
} else {
curr_tmpl = std::string(model_template.data(), model_template.size());
}