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server : fix incorrect usage of llama_get_embeddings() (#14225)
* server : fix incorrect usage of llama_get_embeddings() ggml-ci * cont : fix the fix ggml-ci
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@ -965,6 +965,7 @@ extern "C" {
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LLAMA_API int32_t llama_n_threads_batch(struct llama_context * ctx);
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// Set whether the context outputs embeddings or not
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// TODO: rename to avoid confusion with llama_get_embeddings()
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LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
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// Set whether to use causal attention or not
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@ -1358,6 +1358,14 @@ struct server_slot {
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return server_task_type_need_logits(task_type);
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}
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// if the context does not have a memory module then all embeddings have to be computed within a single ubatch
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// also we cannot split if the pooling would require any past tokens
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bool can_split() const {
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return
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!need_embd() ||
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(llama_get_memory(ctx) && llama_pooling_type(ctx) == LLAMA_POOLING_TYPE_LAST);
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}
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bool can_batch_with(server_slot & other_slot) const {
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return task_type == other_slot.task_type && are_lora_equal(lora, other_slot.lora);
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}
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@ -1929,14 +1937,6 @@ struct server_context {
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llama_batch_free(batch);
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}
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// if the context does not have a memory module then all embeddings have to be computed within a single ubatch
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// also we cannot split if the pooling would require any past tokens
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bool can_split() const {
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return
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!llama_get_embeddings(ctx) ||
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(llama_get_memory(ctx) && llama_pooling_type(ctx) == LLAMA_POOLING_TYPE_LAST);
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}
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bool load_model(const common_params & params) {
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SRV_INF("loading model '%s'\n", params.model.path.c_str());
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@ -3130,7 +3130,7 @@ struct server_context {
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continue;
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}
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if (!can_split()) {
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if (!slot.can_split()) {
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if (slot.n_prompt_tokens > n_ubatch) {
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slot.release();
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send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER);
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@ -3273,7 +3273,7 @@ struct server_context {
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slot.n_prompt_tokens_processed = 0;
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}
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if (!can_split()) {
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if (!slot.can_split()) {
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// cannot fit the prompt in the current batch - will try next iter
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if (batch.n_tokens + slot.n_prompt_tokens > n_batch) {
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continue;
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