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Merged
merged 15 commits into from
Oct 18, 2024
Merged

llama : remove all_pos_0, all_pos_1, all_seq_id from llama_batch #9745

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b226c5b
refactor llama_batch_get_one
ngxson Oct 11, 2024
1c48616
adapt all examples
ngxson Oct 11, 2024
9970316
Merge branch 'master' into xsn/llama_batch_remove_compat
ngxson Oct 11, 2024
9276950
fix simple.cpp
ngxson Oct 11, 2024
59fd6b6
fix llama_bench
ngxson Oct 11, 2024
7740c96
fix
ngxson Oct 11, 2024
6a9769a
fix context shifting
ngxson Oct 11, 2024
0639ff1
free batch before return
ngxson Oct 12, 2024
b4c9911
Merge branch 'master' into xsn/llama_batch_remove_compat
ngxson Oct 12, 2024
734f9e2
use common_batch_add, reuse llama_batch in loop
ngxson Oct 12, 2024
7264596
null terminated seq_id list
ngxson Oct 12, 2024
6395174
fix save-load-state example
ngxson Oct 12, 2024
4be7ecf
fix perplexity
ngxson Oct 12, 2024
9dd7e77
Merge branch 'master' into xsn/llama_batch_remove_compat
ngxson Oct 18, 2024
5d99ae4
correct token pos in llama_batch_allocr
ngxson Oct 18, 2024
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4 changes: 2 additions & 2 deletions common/common.cpp
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Expand Up @@ -939,7 +939,7 @@ struct common_init_result common_init_from_params(common_params & params) {
}

if (llama_model_has_encoder(model)) {
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size()));
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
decoder_start_token_id = bos;
Expand All @@ -948,7 +948,7 @@ struct common_init_result common_init_from_params(common_params & params) {
tmp.push_back(decoder_start_token_id);
}
if (llama_model_has_decoder(model)) {
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch)));
}
llama_kv_cache_clear(lctx);
llama_synchronize(lctx);
Expand Down
1 change: 0 additions & 1 deletion examples/batched-bench/batched-bench.cpp
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Expand Up @@ -74,7 +74,6 @@ int main(int argc, char ** argv) {
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};

const int ret = llama_decode(ctx, batch_view);
Expand Down
2 changes: 1 addition & 1 deletion examples/cvector-generator/cvector-generator.cpp
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Expand Up @@ -339,7 +339,7 @@ static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {

static bool get_hidden_layers(llama_context * ctx, std::vector<llama_token> & tokens) {
llama_kv_cache_clear(ctx);
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
Expand Down
2 changes: 1 addition & 1 deletion examples/eval-callback/eval-callback.cpp
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Expand Up @@ -131,7 +131,7 @@ static bool run(llama_context * ctx, const common_params & params) {

std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);

if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
}
Expand Down
13 changes: 11 additions & 2 deletions examples/imatrix/imatrix.cpp
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Expand Up @@ -508,12 +508,21 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}

// TODO: use batch.logits to save computations instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
llama_batch batch = llama_batch_init(batch_size, 0, 1);
for (int i = 0; i < batch_size; i++) {
batch. token[i] = tokens[batch_start + i];
batch. pos[i] = j*n_batch + i;
batch.logits[i] = true;
batch.seq_id[i][0] = 0;
}

if (llama_decode(ctx, batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
}

llama_batch_free(batch);

// restore the original token in case it was set to BOS
tokens[batch_start] = token_org;

Expand Down
4 changes: 2 additions & 2 deletions examples/infill/infill.cpp
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Expand Up @@ -376,7 +376,7 @@ int main(int argc, char ** argv) {
n_past, n_left, n_ctx, params.n_keep, n_discard);

llama_kv_cache_seq_rm (ctx, 0, params.n_keep + 1 , params.n_keep + n_discard + 1);
llama_kv_cache_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past, -n_discard);
llama_kv_cache_seq_add(ctx, 0, params.n_keep + 1 + n_discard, n_past + 1, -n_discard);
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@ngxson ngxson Oct 12, 2024 •
edited
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Small explanation for what's happening: We suppose to shift all tokens from n_keep + n_discard + 1, so the end of must be n_past + 1 (or we can simply set it to -1, which means [p0, inf))

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@ggerganov ggerganov Oct 14, 2024 •
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Hm, I don't think n_past + 1 is needed here. There shouldn't be a token with pos == n_past in the KV cache.

But yes, using either n_past or -1 would achieve the same thing. Think using n_past is more illustrative.

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ok thanks, I figured out that I counted the token from 1, not from 0. I fixed that in 5d99ae4


n_past -= n_discard;

Expand All @@ -396,7 +396,7 @@ int main(int argc, char ** argv) {

LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str());

if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {
if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) {
LOG_ERR("%s : failed to eval\n", __func__);
return 1;
}
Expand Down
16 changes: 8 additions & 8 deletions examples/llama-bench/llama-bench.cpp
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Expand Up @@ -1428,7 +1428,7 @@ struct sql_printer : public printer {
}
};

static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
static void test_prompt(llama_context * ctx, int n_prompt, int n_batch, int n_threads) {
llama_set_n_threads(ctx, n_threads, n_threads);

const llama_model * model = llama_get_model(ctx);
Expand All @@ -1444,14 +1444,14 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat
for (int i = 1; i < n_tokens; i++) {
tokens[i] = std::rand() % n_vocab;
}
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens));
n_processed += n_tokens;
}

llama_synchronize(ctx);
}

static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
static void test_gen(llama_context * ctx, int n_gen, int n_threads) {
llama_set_n_threads(ctx, n_threads, n_threads);

const llama_model * model = llama_get_model(ctx);
Expand All @@ -1460,7 +1460,7 @@ static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads)
llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;

for (int i = 0; i < n_gen; i++) {
llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
llama_decode(ctx, llama_batch_get_one(&token, 1));
llama_synchronize(ctx);
token = std::rand() % n_vocab;
}
Expand Down Expand Up @@ -1596,13 +1596,13 @@ int main(int argc, char ** argv) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup prompt run\n", params_idx, params_count);
}
//test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
}
if (t.n_gen > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: warmup generation run\n", params_idx, params_count);
}
test_gen(ctx, 1, 0, t.n_threads);
test_gen(ctx, 1, t.n_threads);
}

for (int i = 0; i < params.reps; i++) {
Expand All @@ -1614,13 +1614,13 @@ int main(int argc, char ** argv) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: prompt run %d/%d\n", params_idx, params_count, i + 1, params.reps);
}
test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
test_prompt(ctx, t.n_prompt, t.n_batch, t.n_threads);
}
if (t.n_gen > 0) {
if (params.progress) {
fprintf(stderr, "llama-bench: benchmark %d/%ld: generation run %d/%d\n", params_idx, params_count, i + 1, params.reps);
}
test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
test_gen(ctx, t.n_gen, t.n_threads);
}

uint64_t t_ns = get_time_ns() - t_start;
Expand Down
3 changes: 0 additions & 3 deletions examples/llama.android/llama/src/main/cpp/llama-android.cpp
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Expand Up @@ -283,9 +283,6 @@ Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens,
nullptr,
nullptr,
nullptr,
0,
0,
0,
};

if (embd) {
Expand Down
2 changes: 1 addition & 1 deletion examples/llava/llava-cli.cpp
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Expand Up @@ -20,7 +20,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) {
LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
return false;
}
Expand Down
38 changes: 36 additions & 2 deletions examples/llava/llava.cpp
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Expand Up @@ -401,6 +401,39 @@ bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, co
return true;
}

struct llava_embd_batch {
std::vector<llama_pos> pos;
std::vector<int32_t> n_seq_id;
std::vector<llama_seq_id> seq_id_0;
std::vector<llama_seq_id *> seq_ids;
std::vector<int8_t> logits;
llama_batch batch;
llava_embd_batch(float * embd, int32_t n_tokens, llama_pos pos_0, llama_seq_id seq_id) {
pos .resize(n_tokens);
n_seq_id.resize(n_tokens);
seq_ids .resize(n_tokens + 1);
logits .resize(n_tokens);
seq_id_0.resize(1);
seq_id_0[0] = seq_id;
seq_ids [n_tokens] = nullptr;
batch = {
/*n_tokens =*/ n_tokens,
/*tokens =*/ nullptr,
/*embd =*/ embd,
/*pos =*/ pos.data(),
/*n_seq_id =*/ n_seq_id.data(),
/*seq_id =*/ seq_ids.data(),
/*logits =*/ logits.data(),
};
for (int i = 0; i < n_tokens; i++) {
batch.pos [i] = pos_0 + i;
batch.n_seq_id[i] = 1;
batch.seq_id [i] = seq_id_0.data();
batch.logits [i] = false;
}
}
};

bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_embed * image_embed, int n_batch, int * n_past) {
int n_embd = llama_n_embd(llama_get_model(ctx_llama));

Expand All @@ -409,8 +442,9 @@ bool llava_eval_image_embed(llama_context * ctx_llama, const struct llava_image_
if (n_eval > n_batch) {
n_eval = n_batch;
}
llama_batch batch = {int32_t(n_eval), nullptr, (image_embed->embed+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
if (llama_decode(ctx_llama, batch)) {
float * embd = image_embed->embed+i*n_embd;
llava_embd_batch llava_batch = llava_embd_batch(embd, n_eval, *n_past, 0);
if (llama_decode(ctx_llama, llava_batch.batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return false;
}
Expand Down
2 changes: 1 addition & 1 deletion examples/llava/minicpmv-cli.cpp
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Expand Up @@ -97,7 +97,7 @@ static bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_toke
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval))) {
LOG_ERR("%s : failed to eval. token %d/%d (batch size %d, n_past %d)\n", __func__, i, N, n_batch, *n_past);
return false;
}
Expand Down
4 changes: 2 additions & 2 deletions examples/lookahead/lookahead.cpp
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Expand Up @@ -89,8 +89,8 @@ int main(int argc, char ** argv) {
const auto t_enc_start = ggml_time_us();

// eval the prompt
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1));

for (int s = 1; s < W + G + 1; ++s) {
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
Expand Down
4 changes: 2 additions & 2 deletions examples/lookup/lookup.cpp
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Expand Up @@ -89,8 +89,8 @@ int main(int argc, char ** argv){

const auto t_enc_start = ggml_time_us();

llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1));

const auto t_enc_end = ggml_time_us();

Expand Down
6 changes: 3 additions & 3 deletions examples/main/main.cpp
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Expand Up @@ -528,7 +528,7 @@ int main(int argc, char ** argv) {
int enc_input_size = embd_inp.size();
llama_token * enc_input_buf = embd_inp.data();

if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size, 0, 0))) {
if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size))) {
LOG_ERR("%s : failed to eval\n", __func__);
return 1;
}
Expand Down Expand Up @@ -582,7 +582,7 @@ int main(int argc, char ** argv) {
n_past, n_left, n_ctx, params.n_keep, n_discard);

llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard);
llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);
llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past + 1 , -n_discard);

n_past -= n_discard;

Expand Down Expand Up @@ -648,7 +648,7 @@ int main(int argc, char ** argv) {

LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str());

if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {
if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval))) {
LOG_ERR("%s : failed to eval\n", __func__);
return 1;
}
Expand Down
1 change: 0 additions & 1 deletion examples/parallel/parallel.cpp
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Expand Up @@ -308,7 +308,6 @@ int main(int argc, char ** argv) {
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};

const int ret = llama_decode(ctx, batch_view);
Expand Down
27 changes: 22 additions & 5 deletions examples/perplexity/perplexity.cpp
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Expand Up @@ -412,13 +412,22 @@ static results_perplexity perplexity_v2(llama_context * ctx, const common_params
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);

llama_batch batch = llama_batch_init(batch_size, 0, 1);
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Move the llama_batch outside the loop and reuse it. Maybe utilize the common_batch_ API to make it little less cumbersome.

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Fixed in 734f9e2 and 4be7ecf

for (int i = 0; i < batch_size; i++) {
batch. token[i] = tokens[batch_start + i];
batch. pos[i] = j*n_batch + i;
batch.logits[i] = true;
batch.seq_id[i][0] = 0;
}

//LOG_DBG(" Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
// TODO: use llama_batch.logits instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
if (llama_decode(ctx, batch)) {
//LOG_ERR("%s : failed to eval\n", __func__);
return {tokens, -1, logit_history, prob_history};
}

llama_batch_free(batch);

// save original token and restore it after eval
const auto token_org = tokens[batch_start];

Expand Down Expand Up @@ -704,7 +713,6 @@ static bool decode_helper(llama_context * ctx, llama_batch & batch, std::vector<
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};

const int ret = llama_decode(ctx, batch_view);
Expand Down Expand Up @@ -1803,12 +1811,21 @@ static void kl_divergence(llama_context * ctx, const common_params & params) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}

// TODO: use llama_batch.logits instead of relying on logits_all == true
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
llama_batch batch = llama_batch_init(batch_size, 0, 1);
for (int i = 0; i < batch_size; i++) {
batch. token[i] = tokens[batch_start + i];
batch. pos[i] = j*n_batch + i;
batch.logits[i] = true;
batch.seq_id[i][0] = 0;
}

if (llama_decode(ctx, batch)) {
LOG_ERR("%s : failed to eval\n", __func__);
return;
}

llama_batch_free(batch);

// restore the original token in case it was set to BOS
tokens[batch_start] = token_org;

Expand Down
8 changes: 4 additions & 4 deletions examples/save-load-state/save-load-state.cpp
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Expand Up @@ -49,7 +49,7 @@ int main(int argc, char ** argv) {
auto tokens = common_tokenize(ctx, params.prompt, true);

// evaluate prompt
llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0));
llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()));
n_past += tokens.size();

// save state (rng, logits, embedding and kv_cache) to file
Expand Down Expand Up @@ -77,7 +77,7 @@ int main(int argc, char ** argv) {
printf("%s", next_token_str.c_str());
result0 += next_token_str;

if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {
if (llama_decode(ctx, llama_batch_get_one(&next_token, 1))) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx);
llama_free_model(model);
Expand Down Expand Up @@ -133,7 +133,7 @@ int main(int argc, char ** argv) {
printf("%s", next_token_str.c_str());
result1 += next_token_str;

if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) {
if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1))) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx2);
llama_free_model(model);
Expand Down Expand Up @@ -221,7 +221,7 @@ int main(int argc, char ** argv) {
printf("%s", next_token_str.c_str());
result2 += next_token_str;

if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1, n_past, 1))) {
if (llama_decode(ctx3, llama_batch_get_one(&next_token, 1))) {
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This will generate a batch for seq_id == 0 and it needs to be seq_id == 1

make -j && ./llama-save-load-state -m ${some_model}

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@ngxson ngxson Oct 12, 2024 •
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Thanks for spotting that! Fixed in 6395174

fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx3);
llama_free_model(model);
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1 change: 0 additions & 1 deletion examples/server/server.cpp
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Expand Up @@ -2268,7 +2268,6 @@ struct server_context {
batch.n_seq_id + i,
batch.seq_id + i,
batch.logits + i,
0, 0, 0, // unused
};

const int ret = llama_decode(ctx, batch_view);
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