take out attention_type; add in llama_set_embeddings

common/common.cpp

@@ -546,17 +546,6 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         else { invalid_param = true; }
         return true;
     }
-    if (arg == "--attention") {
-        if (++i >= argc) {
-            invalid_param = true;
-            return true;
-        }
-        std::string value(argv[i]);
-        /**/ if (value == "causal") { params.attention_type = LLAMA_ATTENTION_TYPE_CAUSAL; }
-        else if (value == "non-causal") { params.attention_type = LLAMA_ATTENTION_TYPE_NONCAUSAL; }
-        else { invalid_param = true; }
-        return true;
-    }
     if (arg == "--defrag-thold" || arg == "-dt") {
         if (++i >= argc) {
             invalid_param = true;
@@ -2460,7 +2449,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     cparams.yarn_beta_slow    = params.yarn_beta_slow;
     cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
     cparams.pooling_type      = params.pooling_type;
-    cparams.attention_type    = params.attention_type;
     cparams.defrag_thold      = params.defrag_thold;
     cparams.cb_eval           = params.cb_eval;
     cparams.cb_eval_user_data = params.cb_eval_user_data;

common/common.h

@@ -94,7 +94,6 @@ struct gpt_params {
     enum llama_split_mode        split_mode        = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
     enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
     enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
-    enum llama_attention_type    attention_type    = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type
 
     // // sampling parameters
     struct llama_sampling_params sparams;

examples/gritlm/gritlm.cpp

@@ -44,6 +44,8 @@ static std::vector<std::vector<float>> encode(llama_context * ctx, const std::ve
 
         // clear previous kv_cache values (irrelevant for embeddings)
         llama_kv_cache_clear(ctx);
+        llama_set_embeddings(ctx, true);
+        llama_set_causal_attn(ctx, false);
 
         // run model
         llama_decode(ctx, batch);
@@ -97,6 +99,9 @@ static std::string generate(llama_context * ctx, const std::string & prompt, boo
     llama_token eos_token = llama_token_eos(mdl);
 
     llama_kv_cache_clear(ctx);
+    llama_set_embeddings(ctx, false);
+    llama_set_causal_attn(ctx, true);
+
     llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
 
     std::vector<llama_token> inputs = llama_tokenize(mdl, prompt, false, true);
@@ -165,13 +170,7 @@ int main(int argc, char * argv[]) {
     llama_model * mdl = llama_load_model_from_file(params.model.c_str(), mparams);
 
     // create generation context
-    llama_context * ctx_gen = llama_new_context_with_model(mdl, cparams);
-
-    // create embedding context
-    cparams.embeddings = true;
-    cparams.pooling_type = LLAMA_POOLING_TYPE_NONE;
-    cparams.attention_type = LLAMA_ATTENTION_TYPE_NONCAUSAL;
-    llama_context * ctx_emb = llama_new_context_with_model(mdl, cparams);
+    llama_context * ctx = llama_new_context_with_model(mdl, cparams);
 
     // ### Embedding/Representation ###
     // samples taken from: https://github.com/ContextualAI/gritlm#basic
@@ -189,8 +188,8 @@ int main(int argc, char * argv[]) {
         };
 
         // No need to add instruction for retrieval documents
-        const std::vector<std::vector<float>> d_rep = encode(ctx_emb, documents, gritlm_instruction(""));
-        const std::vector<std::vector<float>> q_rep = encode(ctx_emb, queries,   gritlm_instruction(instruction));
+        const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
+        const std::vector<std::vector<float>> q_rep = encode(ctx, queries,   gritlm_instruction(instruction));
 
         const int n_embd = llama_n_embd(mdl);
 
@@ -209,11 +208,10 @@ int main(int argc, char * argv[]) {
     // GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction
     {
         const std::string prompt = "<|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n";
-        std::string response = generate(ctx_gen, prompt, true);
+        std::string response = generate(ctx, prompt, true);
     }
 
-    llama_free(ctx_gen);
-    llama_free(ctx_emb);
+    llama_free(ctx);
     llama_free_model(mdl);
     llama_backend_free();
 

llama.cpp

@@ -15931,7 +15931,6 @@ struct llama_context_params llama_context_default_params() {
         /*.n_threads_batch             =*/ GGML_DEFAULT_N_THREADS,
         /*.rope_scaling_type           =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
         /*.pooling_type                =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
-        /*.attention_type              =*/ LLAMA_ATTENTION_TYPE_UNSPECIFIED,
         /*.rope_freq_base              =*/ 0.0f,
         /*.rope_freq_scale             =*/ 0.0f,
         /*.yarn_ext_factor             =*/ -1.0f,
@@ -16173,12 +16172,7 @@ struct llama_context * llama_new_context_with_model(
     }
 
     cparams.yarn_attn_factor *= hparams.rope_attn_factor;
-
-    if (params.attention_type == LLAMA_ATTENTION_TYPE_UNSPECIFIED) {
-        cparams.causal_attn = hparams.causal_attn;
-    } else {
-        cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL;
-    }
+    cparams.causal_attn = hparams.causal_attn;
 
     if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
         if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
@@ -17914,6 +17908,10 @@ void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)
     ctx->abort_callback_data = abort_callback_data;
 }
 
+void llama_set_embeddings(struct llama_context * ctx, bool embeddings) {
+    ctx->cparams.embeddings = embeddings;
+}
+
 void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
     ctx->cparams.causal_attn = causal_attn;
 }

llama.h

@@ -177,12 +177,6 @@ extern "C" {
         LLAMA_POOLING_TYPE_LAST = 3,
     };
 
-    enum llama_attention_type {
-        LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1,
-        LLAMA_ATTENTION_TYPE_CAUSAL      = 0,
-        LLAMA_ATTENTION_TYPE_NONCAUSAL  = 1,
-    };
-
     enum llama_split_mode {
         LLAMA_SPLIT_MODE_NONE    = 0, // single GPU
         LLAMA_SPLIT_MODE_LAYER   = 1, // split layers and KV across GPUs
@@ -300,7 +294,6 @@ extern "C" {
 
         enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
         enum llama_pooling_type      pooling_type;      // whether to pool (sum) embedding results by sequence id
-        enum llama_attention_type    attention_type;    // causal, non-causal, or unspecified
 
         // ref: https://github.com/ggerganov/llama.cpp/pull/2054
         float    rope_freq_base;   // RoPE base frequency, 0 = from model
@@ -793,6 +786,10 @@ extern "C" {
     // Get the number of threads used for prompt and batch processing (multiple token).
     LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx);
 
+    // Set whether the model is in embeddings model or not
+    // If true, embeddings will be returned but logits will not
+    LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
+
     // Set whether to use causal attention or not
     // If set to true, the model will only attend to the past tokens
     LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);