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Merge branch 'upstream' into concedo_experimental

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# Conflicts:
#	README.md
#	examples/server/README.md
#	examples/speculative/speculative.cpp
#	flake.lock
#	ggml/src/CMakeLists.txt
#	scripts/sync-ggml.last
#	tests/test-backend-ops.cpp
This commit is contained in:
Concedo 2024-11-14 21:40:52 +08:00
commit df080b074d
41 changed files with 132106 additions and 130341 deletions
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123
src/llama.cpp
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@ -2318,6 +2318,7 @@ enum e_model {
MODEL_1B,
MODEL_1_3B,
MODEL_1_4B,
MODEL_1_5B,
MODEL_1_6B,
MODEL_2B,
MODEL_2_8B,
@ -3521,11 +3522,24 @@ static bool llama_kv_cache_init(
return true;
}
// a structure holds information about the slot found in llama_kv_cache_find_slot
struct llama_kv_cache_slot_info {
std::pair<uint32_t, uint32_t> boundaries; // slot boundaries [begin, end)
bool found = false; // the slot was found
explicit llama_kv_cache_slot_info(bool found_) : found{found_} {}
llama_kv_cache_slot_info(uint32_t begin, uint32_t end) : boundaries{begin, end}, found{true} {}
operator bool() const { return found; }
};
static const llama_kv_cache_slot_info llama_kv_cache_slot_info_failed{false};
// find an empty slot of size "n_tokens" in the cache
// updates the cache head
// returns a structure holding information about the slot found
// Note: On success, it's important that cache.head points
// to the first cell of the slot.
static bool llama_kv_cache_find_slot(
static struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
struct llama_kv_cache & cache,
const struct llama_ubatch & batch) {
const uint32_t n_tokens = batch.n_tokens;
@ -3553,7 +3567,7 @@ static bool llama_kv_cache_find_slot(
// too big seq_id
// TODO: would it be possible to resize the cache instead?
LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
return false;
return llama_kv_cache_slot_info_failed;
}
if (j > 0) {
llama_kv_cell & seq = cache.cells[seq_id];
@ -3688,15 +3702,17 @@ static bool llama_kv_cache_find_slot(
// allow getting the range of used cells, from head to head + n
cache.head = min;
cache.n = max - min + 1;
cache.used = std::count_if(cache.cells.begin(), cache.cells.end(),
[](const llama_kv_cell& cell){ return !cell.is_empty(); });
// sanity check
return cache.n >= n_seqs;
return llama_kv_cache_slot_info(cache.n >= n_seqs);
}
// otherwise, one cell per token.
if (n_tokens > cache.size) {
LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size);
return false;
return llama_kv_cache_slot_info_failed;
}
uint32_t n_tested = 0;
@ -3724,7 +3740,7 @@ static bool llama_kv_cache_find_slot(
if (n_tested >= cache.size) {
//LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
return false;
return llama_kv_cache_slot_info_failed;
}
}
@ -3741,7 +3757,7 @@ static bool llama_kv_cache_find_slot(
cache.used += n_tokens;
return true;
return llama_kv_cache_slot_info(cache.head, cache.head + n_tokens);
}
// find how many cells are currently in use
@ -4017,6 +4033,53 @@ static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams)
return cparams.flash_attn ? 256u : 32u;
}
// saves the kv_cache state for future recovery.
// used to rollback llama_kv_cache_find_slot changes.
struct llama_kv_slot_restorer {
struct llama_kv_cache_state {
uint32_t head = 0;
uint32_t n = 0;
} old_state;
// for non-recurrent models only
// list of slots to restore
std::vector<std::pair<uint32_t, uint32_t>> slot_boundaries;
bool do_restore = false;
explicit llama_kv_slot_restorer(const struct llama_kv_cache & cache) {
old_state.head = cache.head;
old_state.n = cache.n;
}
// saves a slot information for future restoration
void save(const struct llama_kv_cache_slot_info & slot) {
if (slot) {
do_restore = true;
if (slot.boundaries.first != slot.boundaries.second) {
slot_boundaries.push_back(slot.boundaries);
}
}
}
// must be explicitly called to restore the kv_cache state
// and rollback changes from all llama_kv_cache_find_slot calls
void restore(struct llama_kv_cache & cache) {
if (do_restore) {
cache.head = old_state.head;
cache.n = old_state.n;
if (cache.recurrent) { // recurrent models like Mamba or RWKV can't have a state partially erased
llama_kv_cache_seq_rm(cache, -1, -1, -1);
} else {
for (auto & slot : slot_boundaries) {
llama_kv_cache_seq_rm(cache, -1, slot.first, slot.second);
}
}
}
}
};
//
// model loading and saving
//
@ -5258,6 +5321,7 @@ static const char * llama_model_type_name(e_model type) {
case MODEL_1B: return "1B";
case MODEL_1_3B: return "1.3B";
case MODEL_1_4B: return "1.4B";
case MODEL_1_5B: return "1.5B";
case MODEL_1_6B: return "1.6B";
case MODEL_2B: return "2B";
case MODEL_2_8B: return "2.8B";
@ -5629,6 +5693,7 @@ static void llm_load_hparams(
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
case 28: model.type = hparams.n_embd == 1536 ? e_model::MODEL_1_5B : e_model::MODEL_7B; break;
case 32: model.type = e_model::MODEL_7B; break;
case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
case 80: model.type = e_model::MODEL_70B; break;
@ -17258,7 +17323,8 @@ static void llama_output_reorder(struct llama_context * ctx) {
}
}
static void llama_graph_compute(
// returns the result of ggml_backend_sched_graph_compute_async execution
static enum ggml_status llama_graph_compute(
llama_context & lctx,
ggml_cgraph * gf,
int n_threads,
@ -17273,15 +17339,20 @@ static void llama_graph_compute(
set_n_threads_fn.second(set_n_threads_fn.first, n_threads);
}
auto err = ggml_backend_sched_graph_compute_async(lctx.sched.get(), gf);
if (err != GGML_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, err);
auto status = ggml_backend_sched_graph_compute_async(lctx.sched.get(), gf);
if (status != GGML_STATUS_SUCCESS) {
LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, status);
}
// fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
return status;
}
// decode a batch of tokens by evaluating the transformer
// in case of unsuccessful decoding (error or warning),
// the kv_cache state will be returned to its original state
// (for non-recurrent models) or cleaned (for recurrent models)
//
// - lctx: llama context
// - batch: batch to evaluate
@ -17331,6 +17402,7 @@ static int llama_decode_internal(
lctx.n_queued_tokens += n_tokens_all;
auto & kv_self = lctx.kv_self;
llama_kv_slot_restorer kv_slot_restorer(kv_self);
const int64_t n_embd = hparams.n_embd;
const int64_t n_vocab = hparams.n_vocab;
@ -17415,9 +17487,11 @@ static int llama_decode_internal(
kv_self.head = 0;
}
if (!llama_kv_cache_find_slot(kv_self, ubatch)) {
const auto slot = llama_kv_cache_find_slot(kv_self, ubatch);
if (!slot) {
return 1;
}
kv_slot_restorer.save(slot);
if (!kv_self.recurrent) {
// a heuristic, to avoid attending the full cache if it is not yet utilized
@ -17464,7 +17538,19 @@ static int llama_decode_internal(
llama_set_inputs(lctx, ubatch);
llama_graph_compute(lctx, gf, n_threads, threadpool);
const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool);
if (compute_status != GGML_STATUS_SUCCESS) {
kv_slot_restorer.restore(kv_self);
switch (compute_status) {
case GGML_STATUS_ABORTED:
return 2;
case GGML_STATUS_ALLOC_FAILED:
return -2;
case GGML_STATUS_FAILED:
default:
return -3;
}
}
// update the kv ring buffer
{
@ -17701,7 +17787,18 @@ static int llama_encode_internal(
llama_set_inputs(lctx, ubatch);
llama_graph_compute(lctx, gf, n_threads, threadpool);
const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool);
switch (compute_status) {
case GGML_STATUS_SUCCESS:
break;
case GGML_STATUS_ABORTED:
return 2;
case GGML_STATUS_ALLOC_FAILED:
return -2;
case GGML_STATUS_FAILED:
default:
return -3;
}
// extract embeddings
if (embd) {