Add CLI arg to llama-run to adjust the number of threads used (#12370)

We default to 4, sometimes we want to manually adjust this

Signed-off-by: Eric Curtin <ecurtin@redhat.com>

common/arg.cpp

@@ -853,6 +853,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_excludes({LLAMA_EXAMPLE_SERVER}));
+    add_opt(common_arg(
+        {"-sysf", "--system-prompt-file"}, "FNAME",
+        "a file containing the system prompt (default: none)",
+        [](common_params & params, const std::string & value) {
+            std::ifstream file(value);
+            if (!file) {
+                throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
+            }
+            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.system_prompt));
+            if (!params.system_prompt.empty() && params.system_prompt.back() == '\n') {
+                params.system_prompt.pop_back();
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"--in-file"}, "FNAME",
         "an input file (repeat to specify multiple files)",

common/common.cpp

@@ -1033,6 +1033,8 @@ struct common_init_result common_init_from_params(common_params & params) {
     if (params.warmup) {
         LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 
+        llama_set_warmup(lctx, true);
+
         std::vector<llama_token> tmp;
         llama_token bos = llama_vocab_bos(vocab);
         llama_token eos = llama_vocab_eos(vocab);
@@ -1063,6 +1065,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         llama_kv_self_clear(lctx);
         llama_synchronize(lctx);
         llama_perf_context_reset(lctx);
+        llama_set_warmup(lctx, false);
     }
 
     iparams.model.reset(model);

examples/run/run.cpp

@@ -79,6 +79,7 @@ class Opt {
         ctx_params           = llama_context_default_params();
         model_params         = llama_model_default_params();
         context_size_default = ctx_params.n_batch;
+        n_threads_default    = ctx_params.n_threads;
         ngl_default          = model_params.n_gpu_layers;
         common_params_sampling sampling;
         temperature_default = sampling.temp;
@@ -104,6 +105,7 @@ class Opt {
 
         ctx_params.n_batch        = context_size >= 0 ? context_size : context_size_default;
         ctx_params.n_ctx          = ctx_params.n_batch;
+        ctx_params.n_threads = ctx_params.n_threads_batch = n_threads >= 0 ? n_threads : n_threads_default;
         model_params.n_gpu_layers = ngl >= 0 ? ngl : ngl_default;
         temperature               = temperature >= 0 ? temperature : temperature_default;
 
@@ -116,12 +118,12 @@ class Opt {
     std::string chat_template_file;
     std::string          user;
     bool                 use_jinja   = false;
-    int                  context_size = -1, ngl = -1;
+    int                  context_size = -1, ngl = -1, n_threads = -1;
     float                temperature = -1;
     bool                 verbose     = false;
 
   private:
-    int   context_size_default = -1, ngl_default = -1;
+    int   context_size_default = -1, ngl_default = -1, n_threads_default = -1;
     float temperature_default = -1;
     bool  help                = false;
 
@@ -159,53 +161,94 @@ class Opt {
         return 0;
     }
 
+    int parse_options_with_value(int argc, const char ** argv, int & i, bool & options_parsing) {
+        if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) {
+            if (handle_option_with_value(argc, argv, i, context_size) == 1) {
+                return 1;
+            }
+        } else if (options_parsing &&
+                   (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "-ngl") == 0 || strcmp(argv[i], "--ngl") == 0)) {
+            if (handle_option_with_value(argc, argv, i, ngl) == 1) {
+                return 1;
+            }
+        } else if (options_parsing && (strcmp(argv[i], "-t") == 0 || strcmp(argv[i], "--threads") == 0)) {
+            if (handle_option_with_value(argc, argv, i, n_threads) == 1) {
+                return 1;
+            }
+        } else if (options_parsing && strcmp(argv[i], "--temp") == 0) {
+            if (handle_option_with_value(argc, argv, i, temperature) == 1) {
+                return 1;
+            }
+        } else if (options_parsing && strcmp(argv[i], "--chat-template-file") == 0) {
+            if (handle_option_with_value(argc, argv, i, chat_template_file) == 1) {
+                return 1;
+            }
+            use_jinja = true;
+        } else {
+            return 2;
+        }
+
+        return 0;
+    }
+
+    int parse_options(const char ** argv, int & i, bool & options_parsing) {
+        if (options_parsing && (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) {
+            verbose = true;
+        } else if (options_parsing && strcmp(argv[i], "--jinja") == 0) {
+            use_jinja = true;
+        } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) {
+            help = true;
+            return 0;
+        } else if (options_parsing && strcmp(argv[i], "--") == 0) {
+            options_parsing = false;
+        } else {
+            return 2;
+        }
+
+        return 0;
+    }
+
+    int parse_positional_args(const char ** argv, int & i, int & positional_args_i) {
+        if (positional_args_i == 0) {
+            if (!argv[i][0] || argv[i][0] == '-') {
+                return 1;
+            }
+
+            ++positional_args_i;
+            model_ = argv[i];
+        } else if (positional_args_i == 1) {
+            ++positional_args_i;
+            user = argv[i];
+        } else {
+            user += " " + std::string(argv[i]);
+        }
+
+        return 0;
+    }
+
     int parse(int argc, const char ** argv) {
         bool options_parsing   = true;
         for (int i = 1, positional_args_i = 0; i < argc; ++i) {
-            if (options_parsing && (strcmp(argv[i], "-c") == 0 || strcmp(argv[i], "--context-size") == 0)) {
-                if (handle_option_with_value(argc, argv, i, context_size) == 1) {
-                    return 1;
-                }
-            } else if (options_parsing &&
-                       (strcmp(argv[i], "-n") == 0 || strcmp(argv[i], "-ngl") == 0 || strcmp(argv[i], "--ngl") == 0)) {
-                if (handle_option_with_value(argc, argv, i, ngl) == 1) {
-                    return 1;
-                }
-            } else if (options_parsing && strcmp(argv[i], "--temp") == 0) {
-                if (handle_option_with_value(argc, argv, i, temperature) == 1) {
-                    return 1;
-                }
-            } else if (options_parsing &&
-                       (parse_flag(argv, i, "-v", "--verbose") || parse_flag(argv, i, "-v", "--log-verbose"))) {
-                verbose = true;
-            } else if (options_parsing && strcmp(argv[i], "--jinja") == 0) {
-                use_jinja = true;
-            } else if (options_parsing && strcmp(argv[i], "--chat-template-file") == 0){
-                if (handle_option_with_value(argc, argv, i, chat_template_file) == 1) {
-                    return 1;
-                }
-                use_jinja = true;
-            } else if (options_parsing && parse_flag(argv, i, "-h", "--help")) {
-                help = true;
-                return 0;
-            } else if (options_parsing && strcmp(argv[i], "--") == 0) {
-                options_parsing = false;
-            } else if (positional_args_i == 0) {
-                if (!argv[i][0] || argv[i][0] == '-') {
-                    return 1;
-                }
+            int ret = parse_options_with_value(argc, argv, i, options_parsing);
+            if (ret == 0) {
+                continue;
+            } else if (ret == 1) {
+                return ret;
+            }
 
-                ++positional_args_i;
-                model_ = argv[i];
-            } else if (positional_args_i == 1) {
-                ++positional_args_i;
-                user = argv[i];
-            } else {
-                user += " " + std::string(argv[i]);
+            ret = parse_options(argv, i, options_parsing);
+            if (ret == 0) {
+                continue;
+            } else if (ret == 1) {
+                return ret;
+            }
+
+            if (parse_positional_args(argv, i, positional_args_i)) {
+                return 1;
             }
         }
 
-        if (model_.empty()){
+        if (model_.empty()) {
             return 1;
         }
 
@@ -232,6 +275,8 @@ class Opt {
             "      Number of GPU layers (default: %d)\n"
             "  --temp <value>\n"
             "      Temperature (default: %.1f)\n"
+            "  -t, --threads <value>\n"
+            "      Number of threads to use during generation (default: %d)\n"
             "  -v, --verbose, --log-verbose\n"
             "      Set verbosity level to infinity (i.e. log all messages, useful for debugging)\n"
             "  -h, --help\n"
@@ -260,7 +305,7 @@ class Opt {
             "  llama-run file://some-file3.gguf\n"
             "  llama-run --ngl 999 some-file4.gguf\n"
             "  llama-run --ngl 999 some-file5.gguf Hello World\n",
-            context_size_default, ngl_default, temperature_default);
+            context_size_default, ngl_default, temperature_default, n_threads_default);
     }
 };
 

examples/server/utils.hpp

@@ -621,7 +621,9 @@ static json oaicompat_completion_params_parse(
 
     llama_params["chat_format"]      = static_cast<int>(chat_params.format);
     llama_params["prompt"]           = chat_params.prompt;
-    llama_params["grammar"]          = chat_params.grammar;
+    if (!chat_params.grammar.empty()) {
+        llama_params["grammar"] = chat_params.grammar;
+    }
     llama_params["grammar_lazy"]     = chat_params.grammar_lazy;
     auto grammar_triggers = json::array();
     for (const auto & trigger : chat_params.grammar_triggers) {

include/llama.h

@@ -945,6 +945,10 @@ extern "C" {
     // 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);
 
+    // Set whether the model is in warmup mode or not
+    // If true, all model tensors are activated during llama_decode() to load and cache their weights.
+    LLAMA_API void llama_set_warmup(struct llama_context * ctx, bool warmup);
+
     // Set abort callback
     LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
 

src/llama-context.cpp

@@ -39,6 +39,7 @@ llama_context::llama_context(
     cparams.flash_attn       = params.flash_attn;
     cparams.no_perf          = params.no_perf;
     cparams.pooling_type     = params.pooling_type;
+    cparams.warmup           = false;
 
     cparams.n_ctx            = params.n_ctx           == 0    ? hparams.n_ctx_train           : params.n_ctx;
     cparams.rope_freq_base   = params.rope_freq_base  == 0.0f ? hparams.rope_freq_base_train  : params.rope_freq_base;
@@ -948,6 +949,12 @@ void llama_context::set_causal_attn(bool value) {
     cparams.causal_attn = value;
 }
 
+void llama_context::set_warmup(bool value) {
+    LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
+
+    cparams.warmup = value;
+}
+
 void llama_context::set_adapter_lora(
             llama_adapter_lora * adapter,
             float scale) {
@@ -1594,7 +1601,7 @@ void llama_context::output_reorder() {
 //
 
 int32_t llama_context::graph_max_nodes() const {
-    return std::max<int32_t>(8192, 5*model.n_tensors());
+    return std::max<int32_t>(65536, 5*model.n_tensors());
 }
 
 ggml_cgraph * llama_context::graph_init() {
@@ -2372,6 +2379,10 @@ void llama_set_causal_attn(llama_context * ctx, bool causal_attn) {
     ctx->set_causal_attn(causal_attn);
 }
 
+void llama_set_warmup(llama_context * ctx, bool warmup) {
+    ctx->set_warmup(warmup);
+}
+
 void llama_synchronize(llama_context * ctx) {
     ctx->synchronize();
 }

src/llama-context.h

@@ -64,6 +64,7 @@ struct llama_context {
 
     void set_embeddings (bool value);
     void set_causal_attn(bool value);
+    void set_warmup(bool value);
 
     void set_adapter_lora(
             llama_adapter_lora * adapter,

src/llama-cparams.h

@@ -29,6 +29,7 @@ struct llama_cparams {
     bool offload_kqv;
     bool flash_attn;
     bool no_perf;
+    bool warmup;
 
     enum llama_pooling_type pooling_type;
 

src/llama-graph.cpp

@@ -577,7 +577,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     n_embd_head_v    (hparams.n_embd_head_v),
     n_embd_v_gqa     (hparams.n_embd_v_gqa()),
     n_expert         (hparams.n_expert),
-    n_expert_used    (hparams.n_expert_used),
+    n_expert_used    (cparams.warmup ? hparams.n_expert : hparams.n_expert_used),
     freq_base        (cparams.rope_freq_base),
     freq_scale       (cparams.rope_freq_scale),
     ext_factor       (cparams.yarn_ext_factor),
@@ -1311,29 +1311,23 @@ ggml_tensor * llm_graph_context::build_attn(
     return cur;
 }
 
-llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified(
-                bool   causal,
-                bool   swa) const {
+llm_graph_input_attn_kv_unified * llm_graph_context::build_attn_inp_kv_unified() const {
     const llama_kv_cache_unified * kv_self = static_cast<const llama_kv_cache_unified *>(memory);
 
     auto inp = std::make_unique<llm_graph_input_attn_kv_unified>(hparams, cparams, kv_self);
 
     const auto n_kv = kv_self->n;
 
-    inp->self_kq_mask = causal
-        ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
-        : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+    inp->self_kq_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
     //cb(inp->self_kq_mask, "KQ_mask", -1);
     ggml_set_input(inp->self_kq_mask);
 
     inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
 
-    if (swa) {
+    if (hparams.n_swa_pattern > 1) {
         GGML_ASSERT(hparams.n_swa > 0);
 
-        inp->self_kq_mask_swa = causal
-            ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
-            : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        inp->self_kq_mask_swa = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
         //cb(inp->self_kq_mask_swa, "KQ_mask_swa", -1);
         ggml_set_input(inp->self_kq_mask_swa);
 

src/llama-graph.h

@@ -509,9 +509,7 @@ struct llm_graph_context {
                   float   kq_scale,
                     int   il) const;
 
-    llm_graph_input_attn_kv_unified * build_attn_inp_kv_unified(
-            bool causal,
-            bool swa) const;
+    llm_graph_input_attn_kv_unified * build_attn_inp_kv_unified() const;
 
     ggml_tensor * build_attn(
             llm_graph_input_attn_kv_unified * inp,

src/llama-model.cpp

@@ -784,9 +784,11 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     hparams.n_swa = 2047;
                 } else if (hparams.n_layer == 32 && hparams.n_head_kv(0) == 32 && hparams.n_ctx_train == 131072) {
                     // default value for Phi-3-mini-128k-instruct
+                    // note: this seems incorrect because the window is bigger than the train context?
                     hparams.n_swa = 262144;
                 } else if (hparams.n_layer == 40 && hparams.n_ctx_train == 131072) {
                     // default value for Phi-3-medium-128k-instruct
+                    // note: this seems incorrect because the window is equal to the train context?
                     hparams.n_swa = 131072;
                 }
                 bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
@@ -3710,6 +3712,7 @@ void llama_model::print_info() const {
         LLAMA_LOG_INFO("%s: n_head_kv        = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
         LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
         LLAMA_LOG_INFO("%s: n_swa            = %u\n",     __func__, hparams.n_swa);
+        LLAMA_LOG_INFO("%s: n_swa_pattern    = %u\n",     __func__, hparams.n_swa_pattern);
         LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
         LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
         LLAMA_LOG_INFO("%s: n_gqa            = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il);        }, hparams.n_layer).c_str());
@@ -3871,7 +3874,7 @@ struct llm_build_llama : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
         for (int il = 0; il < n_layer; ++il) {
@@ -4034,7 +4037,7 @@ struct llm_build_deci : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
         for (int il = 0; il < n_layer; ++il) {
@@ -4192,7 +4195,7 @@ struct llm_build_baichuan : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = model.type == LLM_TYPE_7B ? build_inp_pos() : nullptr;
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -4310,7 +4313,7 @@ struct llm_build_xverse : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -4418,7 +4421,7 @@ struct llm_build_falcon : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * attn_norm;
@@ -4543,7 +4546,7 @@ struct llm_build_grok : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -4697,7 +4700,7 @@ struct llm_build_dbrx : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -4821,7 +4824,7 @@ struct llm_build_starcoder : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
         cb(pos, "pos_embd", -1);
@@ -4924,7 +4927,7 @@ struct llm_build_refact : public llm_graph_context {
 
         inpL = build_inp_embd(model.tok_embd);
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -5187,7 +5190,7 @@ struct llm_build_bloom : public llm_graph_context {
 
         inpL = build_inp_embd(model.tok_embd);
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         inpL = build_norm(inpL,
                 model.tok_norm,
@@ -5292,7 +5295,7 @@ struct llm_build_mpt : public llm_graph_context {
 
         inpL = build_inp_embd(model.tok_embd);
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         if (model.pos_embd) {
             // inp_pos - contains the positions
@@ -5436,7 +5439,7 @@ struct llm_build_stablelm : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             // norm
@@ -5587,7 +5590,7 @@ struct llm_build_qwen : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -5703,7 +5706,7 @@ struct llm_build_qwen2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -5818,7 +5821,7 @@ struct llm_build_qwen2vl : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         int sections[4];
         std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
@@ -5938,7 +5941,7 @@ struct llm_build_qwen2moe : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -6087,7 +6090,7 @@ struct llm_build_phi2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             attn_norm_output = build_norm(inpL,
@@ -6211,7 +6214,7 @@ struct llm_build_phi3 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, true);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             auto * residual = inpL;
@@ -6357,7 +6360,7 @@ struct llm_build_plamo : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
 
@@ -6465,7 +6468,7 @@ struct llm_build_gpt2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
         cb(pos, "pos_embd", -1);
@@ -6573,7 +6576,7 @@ struct llm_build_codeshell : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             cur = build_norm(inpL,
@@ -6686,7 +6689,7 @@ struct llm_build_orion : public llm_graph_context {
     // inp_pos - contains the positions
     ggml_tensor * inp_pos = build_inp_pos();
 
-    auto * inp_attn = build_attn_inp_kv_unified(true, false);
+    auto * inp_attn = build_attn_inp_kv_unified();
 
     for (int il = 0; il < n_layer; ++il) {
         ggml_tensor * inpSA = inpL;
@@ -6807,7 +6810,7 @@ struct llm_build_internlm2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -6937,7 +6940,7 @@ struct llm_build_minicpm3 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -7141,7 +7144,7 @@ struct llm_build_gemma : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             // norm
@@ -7251,7 +7254,7 @@ struct llm_build_gemma2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, true);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             // norm
@@ -7386,7 +7389,7 @@ struct llm_build_gemma3 : public llm_graph_context {
         ggml_tensor * inp_pos = build_inp_pos();
 
         // TODO: is causal == true correct? might need some changes
-        auto * inp_attn = build_attn_inp_kv_unified(true, true);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             const bool is_swa = hparams.is_swa(il);
@@ -7515,7 +7518,7 @@ struct llm_build_starcoder2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -7828,7 +7831,7 @@ struct llm_build_command_r : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
 
@@ -7978,7 +7981,7 @@ struct llm_build_cohere2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, true);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             const bool is_swa = hparams.is_swa(il);
@@ -8110,7 +8113,7 @@ struct llm_build_olmo : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -8232,7 +8235,7 @@ struct llm_build_olmo2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -8358,7 +8361,7 @@ struct llm_build_olmoe : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -8481,7 +8484,7 @@ struct llm_build_openelm : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             const int64_t n_head    = hparams.n_head(il);
@@ -8611,7 +8614,7 @@ struct llm_build_gptneox : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             cur = build_norm(inpL,
@@ -8757,7 +8760,7 @@ struct llm_build_arctic : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -8889,7 +8892,7 @@ struct llm_build_deepseek : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
 
@@ -9054,7 +9057,7 @@ struct llm_build_deepseek2 : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -9274,7 +9277,7 @@ struct llm_build_bitnet : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -9532,7 +9535,7 @@ struct llm_build_t5_dec : public llm_graph_context {
 
         const int64_t n_outputs_enc = embd_enc->ne[1];
 
-        auto * inp_attn_self  = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn_self  = build_attn_inp_kv_unified();
         auto * inp_attn_cross = build_attn_inp_cross();
 
         for (int il = 0; il < n_layer; ++il) {
@@ -9698,7 +9701,7 @@ struct llm_build_jais : public llm_graph_context {
 
         inpL = build_inp_embd(model.tok_embd);
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             cur = build_norm(inpL,
@@ -9794,7 +9797,7 @@ struct llm_build_chatglm : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -9926,7 +9929,7 @@ struct llm_build_nemotron : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -10049,7 +10052,7 @@ struct llm_build_exaone : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;
@@ -10565,7 +10568,7 @@ struct llm_build_chameleon : public llm_graph_context {
         // inp_pos - contains the positions
         ggml_tensor * inp_pos = build_inp_pos();
 
-        auto * inp_attn = build_attn_inp_kv_unified(true, false);
+        auto * inp_attn = build_attn_inp_kv_unified();
 
         for (int il = 0; il < n_layer; ++il) {
             ggml_tensor * inpSA = inpL;