multithread tensor loading fixes

conditioner.hpp

@@ -141,7 +141,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
             }
             return true;
         };
-        model_loader.load_tensors(on_load);
+        model_loader.load_tensors(on_load, 1);
         readed_embeddings.push_back(embd_name);
         if (embd) {
             int64_t hidden_size = text_model->model.hidden_size;

control.hpp

@@ -445,7 +445,7 @@ struct ControlNet : public GGMLRunner {
         guided_hint_cached = true;
     }
 
-    bool load_from_file(const std::string& file_path) {
+    bool load_from_file(const std::string& file_path, int n_threads) {
         LOG_INFO("loading control net from '%s'", file_path.c_str());
         alloc_params_buffer();
         std::map<std::string, ggml_tensor*> tensors;
@@ -458,7 +458,7 @@ struct ControlNet : public GGMLRunner {
             return false;
         }
 
-        bool success = model_loader.load_tensors(tensors, ignore_tensors);
+        bool success = model_loader.load_tensors(tensors, ignore_tensors, n_threads);
 
         if (!success) {
             LOG_ERROR("load control net tensors from model loader failed");

esrgan.hpp

@@ -164,7 +164,7 @@ struct ESRGAN : public GGMLRunner {
         return "esrgan";
     }
 
-    bool load_from_file(const std::string& file_path) {
+    bool load_from_file(const std::string& file_path, int n_threads) {
         LOG_INFO("loading esrgan from '%s'", file_path.c_str());
 
         alloc_params_buffer();
@@ -177,7 +177,7 @@ struct ESRGAN : public GGMLRunner {
             return false;
         }
 
-        bool success = model_loader.load_tensors(esrgan_tensors);
+        bool success = model_loader.load_tensors(esrgan_tensors, {}, n_threads);
 
         if (!success) {
             LOG_ERROR("load esrgan tensors from model loader failed");

lora.hpp

@@ -116,7 +116,7 @@ struct LoraModel : public GGMLRunner {
         return "lora";
     }
 
-    bool load_from_file(bool filter_tensor = false, int n_threads = 0) {
+    bool load_from_file(bool filter_tensor, int n_threads) {
         LOG_INFO("loading LoRA from '%s'", file_path.c_str());
 
         if (load_failed) {

model.cpp

@@ -1957,7 +1957,8 @@ bool ModelLoader::load_tensors(on_new_tensor_cb_t on_new_tensor_cb, int n_thread
     std::atomic<int64_t> copy_to_backend_time_ms(0);
     std::atomic<int64_t> convert_time_ms(0);
 
-    int num_threads_to_use = n_threads_p > 0 ? n_threads_p : (int)std::thread::hardware_concurrency();
+    int num_threads_to_use = n_threads_p > 0 ? n_threads_p : get_num_physical_cores();
+    LOG_DEBUG("using %d threads for model loading", num_threads_to_use);
 
     int64_t start_time = ggml_time_ms();
     std::vector<TensorStorage> processed_tensor_storages;

pmid.hpp

@@ -591,7 +591,7 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
         return "id_embeds";
     }
 
-    bool load_from_file(bool filter_tensor = false) {
+    bool load_from_file(bool filter_tensor, int n_threads) {
         LOG_INFO("loading PhotoMaker ID Embeds from '%s'", file_path.c_str());
 
         if (load_failed) {
@@ -623,11 +623,11 @@ struct PhotoMakerIDEmbed : public GGMLRunner {
             return true;
         };
 
-        model_loader->load_tensors(on_new_tensor_cb);
+        model_loader->load_tensors(on_new_tensor_cb, n_threads);
         alloc_params_buffer();
 
         dry_run = false;
-        model_loader->load_tensors(on_new_tensor_cb);
+        model_loader->load_tensors(on_new_tensor_cb, n_threads);
 
         LOG_DEBUG("finished loading PhotoMaker ID Embeds ");
         return true;

stable-diffusion.cpp

@@ -531,7 +531,7 @@ class StableDiffusionGGML {
             }
             if (strlen(SAFE_STR(sd_ctx_params->photo_maker_path)) > 0) {
                 pmid_lora = std::make_shared<LoraModel>(backend, sd_ctx_params->photo_maker_path, "");
-                if (!pmid_lora->load_from_file(true)) {
+                if (!pmid_lora->load_from_file(true, n_threads)) {
                     LOG_WARN("load photomaker lora tensors from %s failed", sd_ctx_params->photo_maker_path);
                     return false;
                 }
@@ -599,14 +599,14 @@ class StableDiffusionGGML {
             if (!use_tiny_autoencoder) {
                 vae_params_mem_size = first_stage_model->get_params_buffer_size();
             } else {
-                if (!tae_first_stage->load_from_file(taesd_path)) {
+                if (!tae_first_stage->load_from_file(taesd_path, n_threads)) {
                     return false;
                 }
                 vae_params_mem_size = tae_first_stage->get_params_buffer_size();
             }
             size_t control_net_params_mem_size = 0;
             if (control_net) {
-                if (!control_net->load_from_file(SAFE_STR(sd_ctx_params->control_net_path))) {
+                if (!control_net->load_from_file(SAFE_STR(sd_ctx_params->control_net_path), n_threads)) {
                     return false;
                 }
                 control_net_params_mem_size = control_net->get_params_buffer_size();
@@ -836,7 +836,7 @@ class StableDiffusionGGML {
             return;
         }
         LoraModel lora(backend, file_path, is_high_noise ? "model.high_noise_" : "");
-        if (!lora.load_from_file()) {
+        if (!lora.load_from_file(false, n_threads)) {
             LOG_WARN("load lora tensors from %s failed", file_path.c_str());
             return;
         }

tae.hpp

@@ -222,7 +222,7 @@ struct TinyAutoEncoder : public GGMLRunner {
         return "taesd";
     }
 
-    bool load_from_file(const std::string& file_path) {
+    bool load_from_file(const std::string& file_path, int n_threads) {
         LOG_INFO("loading taesd from '%s', decode_only = %s", file_path.c_str(), decode_only ? "true" : "false");
         alloc_params_buffer();
         std::map<std::string, ggml_tensor*> taesd_tensors;
@@ -238,7 +238,7 @@ struct TinyAutoEncoder : public GGMLRunner {
             return false;
         }
 
-        bool success = model_loader.load_tensors(taesd_tensors, ignore_tensors);
+        bool success = model_loader.load_tensors(taesd_tensors, ignore_tensors, n_threads);
 
         if (!success) {
             LOG_ERROR("load tae tensors from model loader failed");

upscaler.cpp

@@ -18,7 +18,8 @@ struct UpscalerGGML {
     }
 
     bool load_from_file(const std::string& esrgan_path,
-                        bool offload_params_to_cpu) {
+                        bool offload_params_to_cpu,
+                        int n_threads) {
         ggml_log_set(ggml_log_callback_default, nullptr);
 #ifdef SD_USE_CUDA
         LOG_DEBUG("Using CUDA backend");
@@ -54,7 +55,7 @@ struct UpscalerGGML {
         if (direct) {
             esrgan_upscaler->enable_conv2d_direct();
         }
-        if (!esrgan_upscaler->load_from_file(esrgan_path)) {
+        if (!esrgan_upscaler->load_from_file(esrgan_path, n_threads)) {
             return false;
         }
         return true;
@@ -124,7 +125,7 @@ upscaler_ctx_t* new_upscaler_ctx(const char* esrgan_path_c_str,
         return NULL;
     }
 
-    if (!upscaler_ctx->upscaler->load_from_file(esrgan_path, offload_params_to_cpu)) {
+    if (!upscaler_ctx->upscaler->load_from_file(esrgan_path, offload_params_to_cpu, n_threads)) {
         delete upscaler_ctx->upscaler;
         upscaler_ctx->upscaler = NULL;
         free(upscaler_ctx);