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llama : propagate the results of graph_compute
(#9525)
* llama: propagating the results of `graph_compute` to the user interface * llama: reverting kv_cache in case of failed compute * llama: `llama_kv_cache_state` was removed, only the result of `llama_graph_compute` is returned * llama: restore a kv_cache in case of failed computation * llama: correct reverting of the entire batch. also updates `llama_kv_cache_find_slot`, will correctly count the number of `used` cells for recurrent models * llama: updated comments * llama : add comments about KV cache state after error --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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@ -797,7 +797,7 @@ extern "C" {
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// Processes a batch of tokens with the ecoder part of the encoder-decoder model.
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// Stores the encoder output internally for later use by the decoder cross-attention layers.
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// 0 - success
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// < 0 - error
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// < 0 - error. the KV cache state is restored to the state before this call
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LLAMA_API int32_t llama_encode(
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struct llama_context * ctx,
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struct llama_batch batch);
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@ -805,7 +805,7 @@ extern "C" {
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// Positive return values does not mean a fatal error, but rather a warning.
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// 0 - success
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// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
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// < 0 - error
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// < 0 - error. the KV cache state is restored to the state before this call
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LLAMA_API int32_t llama_decode(
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struct llama_context * ctx,
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struct llama_batch batch);
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120
src/llama.cpp
120
src/llama.cpp
@ -3502,11 +3502,24 @@ static bool llama_kv_cache_init(
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return true;
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}
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// a structure holds information about the slot found in llama_kv_cache_find_slot
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struct llama_kv_cache_slot_info {
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std::pair<uint32_t, uint32_t> boundaries; // slot boundaries [begin, end)
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bool found = false; // the slot was found
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explicit llama_kv_cache_slot_info(bool found_) : found{found_} {}
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llama_kv_cache_slot_info(uint32_t begin, uint32_t end) : boundaries{begin, end}, found{true} {}
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operator bool() const { return found; }
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};
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static const llama_kv_cache_slot_info llama_kv_cache_slot_info_failed{false};
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// find an empty slot of size "n_tokens" in the cache
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// updates the cache head
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// returns a structure holding information about the slot found
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// Note: On success, it's important that cache.head points
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// to the first cell of the slot.
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static bool llama_kv_cache_find_slot(
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static struct llama_kv_cache_slot_info llama_kv_cache_find_slot(
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struct llama_kv_cache & cache,
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const struct llama_ubatch & batch) {
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const uint32_t n_tokens = batch.n_tokens;
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@ -3534,7 +3547,7 @@ static bool llama_kv_cache_find_slot(
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// too big seq_id
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// TODO: would it be possible to resize the cache instead?
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LLAMA_LOG_ERROR("%s: seq_id=%d >= n_seq_max=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
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return false;
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return llama_kv_cache_slot_info_failed;
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}
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if (j > 0) {
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llama_kv_cell & seq = cache.cells[seq_id];
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@ -3669,15 +3682,17 @@ static bool llama_kv_cache_find_slot(
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// allow getting the range of used cells, from head to head + n
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cache.head = min;
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cache.n = max - min + 1;
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cache.used = std::count_if(cache.cells.begin(), cache.cells.end(),
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[](const llama_kv_cell& cell){ return !cell.is_empty(); });
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// sanity check
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return cache.n >= n_seqs;
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return llama_kv_cache_slot_info(cache.n >= n_seqs);
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}
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// otherwise, one cell per token.
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if (n_tokens > cache.size) {
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LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size);
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return false;
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return llama_kv_cache_slot_info_failed;
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}
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uint32_t n_tested = 0;
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@ -3705,7 +3720,7 @@ static bool llama_kv_cache_find_slot(
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if (n_tested >= cache.size) {
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//LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
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return false;
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return llama_kv_cache_slot_info_failed;
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}
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}
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@ -3722,7 +3737,7 @@ static bool llama_kv_cache_find_slot(
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cache.used += n_tokens;
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return true;
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return llama_kv_cache_slot_info(cache.head, cache.head + n_tokens);
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}
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// find how many cells are currently in use
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@ -3998,6 +4013,53 @@ static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams)
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return cparams.flash_attn ? 256u : 32u;
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}
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// saves the kv_cache state for future recovery.
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// used to rollback llama_kv_cache_find_slot changes.
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struct llama_kv_slot_restorer {
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struct llama_kv_cache_state {
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uint32_t head = 0;
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uint32_t n = 0;
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} old_state;
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// for non-recurrent models only
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// list of slots to restore
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std::vector<std::pair<uint32_t, uint32_t>> slot_boundaries;
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bool do_restore = false;
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explicit llama_kv_slot_restorer(const struct llama_kv_cache & cache) {
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old_state.head = cache.head;
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old_state.n = cache.n;
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}
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// saves a slot information for future restoration
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void save(const struct llama_kv_cache_slot_info & slot) {
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if (slot) {
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do_restore = true;
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if (slot.boundaries.first != slot.boundaries.second) {
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slot_boundaries.push_back(slot.boundaries);
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}
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}
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}
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// must be explicitly called to restore the kv_cache state
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// and rollback changes from all llama_kv_cache_find_slot calls
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void restore(struct llama_kv_cache & cache) {
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if (do_restore) {
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cache.head = old_state.head;
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cache.n = old_state.n;
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if (cache.recurrent) { // recurrent models like Mamba or RWKV can't have a state partially erased
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llama_kv_cache_seq_rm(cache, -1, -1, -1);
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} else {
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for (auto & slot : slot_boundaries) {
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llama_kv_cache_seq_rm(cache, -1, slot.first, slot.second);
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}
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}
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}
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}
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};
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//
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// model loading and saving
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//
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@ -17181,7 +17243,8 @@ static void llama_output_reorder(struct llama_context * ctx) {
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}
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}
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static void llama_graph_compute(
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// returns the result of ggml_backend_sched_graph_compute_async execution
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static enum ggml_status llama_graph_compute(
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llama_context & lctx,
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ggml_cgraph * gf,
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int n_threads,
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@ -17196,15 +17259,20 @@ static void llama_graph_compute(
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set_n_threads_fn.second(set_n_threads_fn.first, n_threads);
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}
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auto err = ggml_backend_sched_graph_compute_async(lctx.sched.get(), gf);
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if (err != GGML_STATUS_SUCCESS) {
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LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, err);
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auto status = ggml_backend_sched_graph_compute_async(lctx.sched.get(), gf);
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if (status != GGML_STATUS_SUCCESS) {
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LLAMA_LOG_ERROR("%s: ggml_backend_sched_graph_compute_async failed with error %d\n", __func__, status);
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}
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// fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
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return status;
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}
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// decode a batch of tokens by evaluating the transformer
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// in case of unsuccessful decoding (error or warning),
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// the kv_cache state will be returned to its original state
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// (for non-recurrent models) or cleaned (for recurrent models)
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//
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// - lctx: llama context
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// - batch: batch to evaluate
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@ -17254,6 +17322,7 @@ static int llama_decode_internal(
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lctx.n_queued_tokens += n_tokens_all;
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auto & kv_self = lctx.kv_self;
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llama_kv_slot_restorer kv_slot_restorer(kv_self);
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_vocab = hparams.n_vocab;
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@ -17338,9 +17407,11 @@ static int llama_decode_internal(
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kv_self.head = 0;
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}
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if (!llama_kv_cache_find_slot(kv_self, ubatch)) {
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const auto slot = llama_kv_cache_find_slot(kv_self, ubatch);
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if (!slot) {
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return 1;
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}
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kv_slot_restorer.save(slot);
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if (!kv_self.recurrent) {
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// a heuristic, to avoid attending the full cache if it is not yet utilized
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@ -17387,7 +17458,19 @@ static int llama_decode_internal(
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llama_set_inputs(lctx, ubatch);
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llama_graph_compute(lctx, gf, n_threads, threadpool);
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const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool);
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if (compute_status != GGML_STATUS_SUCCESS) {
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kv_slot_restorer.restore(kv_self);
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switch (compute_status) {
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case GGML_STATUS_ABORTED:
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return 2;
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case GGML_STATUS_ALLOC_FAILED:
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return -2;
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case GGML_STATUS_FAILED:
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default:
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return -3;
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}
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}
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// update the kv ring buffer
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{
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@ -17624,7 +17707,18 @@ static int llama_encode_internal(
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llama_set_inputs(lctx, ubatch);
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llama_graph_compute(lctx, gf, n_threads, threadpool);
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const auto compute_status = llama_graph_compute(lctx, gf, n_threads, threadpool);
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switch (compute_status) {
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case GGML_STATUS_SUCCESS:
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break;
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case GGML_STATUS_ABORTED:
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return 2;
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case GGML_STATUS_ALLOC_FAILED:
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return -2;
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case GGML_STATUS_FAILED:
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default:
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return -3;
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}
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// extract embeddings
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if (embd) {
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