Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-25 22:08:46 +01:00
Code Issues Packages Projects Releases Wiki Activity

llama : fix empty batch causing llama_batch_allocr to crash (#9966)

Browse Source 
* llama : fix empty batch cause llama_batch_allocr to crash

* move batch_allocr inside decode/encode_internal

* fix build

* add GGML_ASSERT

* Apply suggestions from code review

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Xuan Son Nguyen 2024-10-22 16:59:02 +02:00 committed by GitHub
parent 19d900a756
commit c8c07d658a
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 67 additions and 61 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

128
src/llama.cpp
View File

@ -5177,6 +5177,57 @@ struct llama_model_loader {
} }
}; };
// temporary allocate memory for the input batch if needed
static const llama_seq_id batch_default_seq_id = 0;
struct llama_batch_allocr {
std::array<llama_seq_id, 1> seq_id_0 = {batch_default_seq_id};
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id *> seq_id;
std::vector<int8_t> logits;
struct llama_batch batch;
// optionally fulfill the batch returned by llama_batch_get_one
llama_batch_allocr(llama_context & ctx, struct llama_batch in_batch) {
batch = in_batch;
GGML_ASSERT(batch.n_tokens > 0);
if (!batch.pos) {
// determine the last position in KV cache
llama_pos last_pos = -1;
for (const auto & cell : ctx.kv_self.cells) {
if (cell.has_seq_id(batch_default_seq_id)) {
last_pos = std::max(last_pos, cell.pos);
}
}
last_pos++; // next position
pos.resize(batch.n_tokens);
for (int32_t i = 0; i < batch.n_tokens; i++) {
pos[i] = i+last_pos;
}
batch.pos = pos.data();
}
if (!batch.n_seq_id) {
n_seq_id.resize(batch.n_tokens);
for (int32_t i = 0; i < batch.n_tokens; i++) {
n_seq_id[i] = seq_id_0.size();
}
batch.n_seq_id = n_seq_id.data();
}
if (!batch.seq_id) {
seq_id.resize(batch.n_tokens + 1);
seq_id[batch.n_tokens] = NULL;
for (int32_t i = 0; i < batch.n_tokens; i++) {
seq_id[i] = seq_id_0.data();
}
batch.seq_id = seq_id.data();
}
if (!batch.logits) {
logits.resize(batch.n_tokens);
logits[logits.size() - 1] = true;
batch.logits = logits.data();
}
}
};
template<> template<>
bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) { bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) {
uint32_t tmp; uint32_t tmp;
@ -17095,16 +17146,20 @@ static void llama_graph_compute(
// //
static int llama_decode_internal( static int llama_decode_internal(
llama_context & lctx, llama_context & lctx,
llama_batch batch) { llama_batch inp_batch) {
lctx.is_encoding = false; lctx.is_encoding = false;
const uint32_t n_tokens_all = batch.n_tokens;
if (n_tokens_all == 0) { if (inp_batch.n_tokens == 0) {
LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
return -1; return -1;
} }
// temporary allocate memory for the input batch if needed
llama_batch_allocr batch_allocr(lctx, inp_batch);
const llama_batch & batch = batch_allocr.batch;
const uint32_t n_tokens_all = batch.n_tokens;
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams; const auto & cparams = lctx.cparams;
@ -17409,17 +17464,20 @@ static int llama_decode_internal(
// //
static int llama_encode_internal( static int llama_encode_internal(
llama_context & lctx, llama_context & lctx,
llama_batch batch) { llama_batch inp_batch) {
lctx.is_encoding = true; lctx.is_encoding = true;
const uint32_t n_tokens = batch.n_tokens; if (inp_batch.n_tokens == 0) {
if (n_tokens == 0) {
LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__); LLAMA_LOG_ERROR("%s: n_tokens == 0\n", __func__);
return -1; return -1;
} }
// temporary allocate memory for the input batch if needed
llama_batch_allocr batch_allocr(lctx, inp_batch);
const llama_batch & batch = batch_allocr.batch;
const uint32_t n_tokens = batch.n_tokens;
const auto & model = lctx.model; const auto & model = lctx.model;
const auto & hparams = model.hparams; const auto & hparams = model.hparams;
const auto & cparams = lctx.cparams; const auto & cparams = lctx.cparams;
@ -21090,61 +21148,10 @@ void llama_batch_free(struct llama_batch batch) {
if (batch.logits) free(batch.logits); if (batch.logits) free(batch.logits);
} }
// temporary allocate memory for the input batch if needed
static const llama_seq_id batch_default_seq_id = 0;
struct llama_batch_allocr {
std::array<llama_seq_id, 1> seq_id_0 = {batch_default_seq_id};
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id *> seq_id;
std::vector<int8_t> logits;
struct llama_batch batch;
// optionally fulfill the batch returned by llama_batch_get_one
llama_batch_allocr(struct llama_context * ctx, struct llama_batch in_batch) {
batch = in_batch;
if (!batch.pos) {
// determine the last position in KV cache
llama_pos last_pos = -1;
for (const auto & cell : ctx->kv_self.cells) {
if (cell.has_seq_id(batch_default_seq_id)) {
last_pos = std::max(last_pos, cell.pos);
}
}
last_pos++; // next position
pos.resize(batch.n_tokens);
for (int32_t i = 0; i < batch.n_tokens; i++) {
pos[i] = i+last_pos;
}
batch.pos = pos.data();
}
if (!batch.n_seq_id) {
n_seq_id.resize(batch.n_tokens);
for (int32_t i = 0; i < batch.n_tokens; i++) {
n_seq_id[i] = seq_id_0.size();
}
batch.n_seq_id = n_seq_id.data();
}
if (!batch.seq_id) {
seq_id.resize(batch.n_tokens + 1);
seq_id[batch.n_tokens] = NULL;
for (int32_t i = 0; i < batch.n_tokens; i++) {
seq_id[i] = seq_id_0.data();
}
batch.seq_id = seq_id.data();
}
if (!batch.logits) {
logits.resize(batch.n_tokens);
logits[logits.size() - 1] = true;
batch.logits = logits.data();
}
}
};
int32_t llama_encode( int32_t llama_encode(
struct llama_context * ctx, struct llama_context * ctx,
struct llama_batch batch) { struct llama_batch batch) {
llama_batch_allocr batch_allocr(ctx, batch); const int ret = llama_encode_internal(*ctx, batch);
const int ret = llama_encode_internal(*ctx, batch_allocr.batch);
if (ret != 0) { if (ret != 0) {
LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret); LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret);
} }
@ -21155,8 +21162,7 @@ int32_t llama_encode(
int32_t llama_decode( int32_t llama_decode(
struct llama_context * ctx, struct llama_context * ctx,
struct llama_batch batch) { struct llama_batch batch) {
llama_batch_allocr batch_allocr(ctx, batch); const int ret = llama_decode_internal(*ctx, batch);
const int ret = llama_decode_internal(*ctx, batch_allocr.batch);
if (ret != 0) { if (ret != 0) {
LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret); LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
} }