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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	.github/workflows/build.yml
#	ci/run.sh
#	docs/function-calling.md
#	examples/gritlm/gritlm.cpp
#	ggml/CMakeLists.txt
#	ggml/cmake/common.cmake
#	ggml/src/CMakeLists.txt
#	ggml/src/ggml-cpu/CMakeLists.txt
#	ggml/src/ggml-cpu/ggml-cpu.c
#	ggml/src/ggml-hip/CMakeLists.txt
#	ggml/src/ggml-vulkan/CMakeLists.txt
#	ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt
#	requirements/requirements-compare-llama-bench.txt
#	scripts/compare-llama-bench.py
#	tests/CMakeLists.txt
This commit is contained in:
Concedo 2025-06-18 00:16:54 +08:00
commit 4356a00f4a
47 changed files with 1526 additions and 301 deletions
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38
src/llama-context.cpp
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@ -29,8 +29,8 @@ llama_context::llama_context(
const auto & hparams = model.hparams;
cparams.n_seq_max = std::max(1u, params.n_seq_max);
if (cparams.n_seq_max > LLAMA_MAX_PARALLEL_SEQUENCES) {
throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_PARALLEL_SEQUENCES));
if (cparams.n_seq_max > LLAMA_MAX_SEQ) {
throw std::runtime_error("n_seq_max must be <= " + std::to_string(LLAMA_MAX_SEQ));
}
cparams.n_threads = params.n_threads;
@ -727,9 +727,8 @@ int llama_context::encode(const llama_batch & batch_inp) {
return -1;
}
// temporary allocate memory for the input batch if needed
// note: during encode, we always pass the full sequence starting from pos = 0
if (!batch_allocr->init(batch_inp, model.vocab, batch_inp.pos ? -1 : 0)) {
if (!batch_allocr->init(batch_inp, model.vocab, nullptr, true)) {
LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
return -1;
}
@ -895,8 +894,10 @@ int llama_context::decode(const llama_batch & batch_inp) {
return -1;
}
// temporary allocate memory for the input batch if needed
if (!batch_allocr->init(batch_inp, model.vocab, batch_inp.pos ? -1 : memory->seq_pos_max(0) + 1)) {
// when computing embeddings, all tokens are output
const bool embd_all = cparams.embeddings;
if (!batch_allocr->init(batch_inp, model.vocab, memory.get(), embd_all)) {
LLAMA_LOG_ERROR("%s: failed to initialize batch\n", __func__);
return -1;
}
@ -913,12 +914,9 @@ int llama_context::decode(const llama_batch & batch_inp) {
GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
// this indicates we are doing pooled embedding
const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
const uint32_t n_outputs_all = batch_allocr->get_n_outputs();
if (embd_pooled) {
if (embd_all) {
// require that all tokens are output
if (n_outputs_all != n_tokens_all) {
LLAMA_LOG_ERROR("%s: pooled embedding requires that all tokens are output (n_outputs_all = %d, n_tokens_all = %d)\n",
@ -947,7 +945,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
llama_memory_state_ptr mstate;
while (true) {
mstate = memory->init_batch(batch, cparams.n_ubatch, embd_pooled);
mstate = memory->init_batch(batch, cparams.n_ubatch, embd_all);
if (!mstate) {
return -2;
}
@ -1025,8 +1023,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
if (!res) {
// the last ubatch failed or was aborted -> remove all positions of that ubatch from the KV cache
llama_pos pos_min[LLAMA_MAX_PARALLEL_SEQUENCES];
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
llama_pos pos_min[LLAMA_MAX_SEQ];
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
pos_min[s] = std::numeric_limits<llama_pos>::max();
}
@ -1037,7 +1035,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
pos_min[seq_id] = std::min(pos_min[seq_id], ubatch.pos[i]);
}
for (int s = 0; s < LLAMA_MAX_PARALLEL_SEQUENCES; ++s) {
for (int s = 0; s < LLAMA_MAX_SEQ; ++s) {
if (pos_min[s] == std::numeric_limits<llama_pos>::max()) {
continue;
}
@ -1060,7 +1058,7 @@ int llama_context::decode(const llama_batch & batch_inp) {
// ggml_graph_dump_dot(gf, NULL, "llama.dot");
//}
auto * t_logits = cparams.embeddings ? nullptr : res->get_logits();
auto * t_logits = res->get_logits();
auto * t_embd = cparams.embeddings ? res->get_embd() : nullptr;
if (t_embd && res->get_embd_pooled()) {
@ -1224,9 +1222,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
const auto n_vocab = vocab.n_tokens();
const auto n_embd = hparams.n_embd;
// TODO: use a per-batch flag for logits presence instead
bool has_logits = !cparams.embeddings;
bool has_embd = cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
bool has_logits = true;
bool has_embd = cparams.embeddings;
// TODO: hacky enc-dec support
if (model.arch == LLM_ARCH_T5) {
@ -2046,14 +2043,11 @@ void llama_context::opt_epoch_iter(
n_queued_tokens += n_tokens_all;
// this indicates we are doing pooled embedding
const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
embd_seq.clear();
uint32_t n_outputs_all = n_tokens_all;
auto mstate = memory->init_batch(batch, cparams.n_ubatch, embd_pooled);
auto mstate = memory->init_batch(batch, cparams.n_ubatch, true);
if (!mstate || mstate->get_status() != LLAMA_MEMORY_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: could not initialize batch\n", __func__);
break;