Updated Swift and Android bindings to use the new llama_sampling_* refactor from #8643

examples/batched.swift/Sources/main.swift

@@ -44,6 +44,8 @@ context_params.n_threads = 8
 context_params.n_threads_batch = 8
 
 let context = llama_new_context_with_model(model, context_params)
+let smpl = llama_get_sampling(context)
+
 guard context != nil else {
     print("Failed to initialize context")
     exit(1)
@@ -144,13 +146,13 @@ while n_cur <= n_len {
         let top_p: Float = 0.9
         let temp: Float = 0.4
 
-        llama_sample_top_k(context, &candidates_p, top_k, 1)
-        llama_sample_top_p(context, &candidates_p, top_p, 1)
-        llama_sample_temp(context, &candidates_p, temp)
+        llama_sampling_top_k(smpl, &candidates_p, top_k, 1)
+        llama_sampling_top_p(smpl, &candidates_p, top_p, 1)
+        llama_sampling_temp(smpl, &candidates_p, temp)
 
-        let new_token_id = llama_sample_token(context, &candidates_p)
+        let new_token_id = llama_sampling_sample(smpl, &candidates_p)
 
-        // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
+        // const llama_token new_token_id = llama_sampling_sample_greedy(smpl, &candidates_p);
 
         // is it an end of stream? -> mark the stream as finished
         if llama_token_is_eog(model, new_token_id) || n_cur == n_len {
@@ -212,7 +214,7 @@ let t_main_end = ggml_time_us()
 
 print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end - t_main_start) / 1_000_000.0)) s, speed: \(String(format: "%.2f", Double(n_decode) / (Double(t_main_end - t_main_start) / 1_000_000.0))) t/s\n")
 
-llama_print_timings(context)
+llama_print_timings(context, smpl, nil)
 
 private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
     let utf8Count = text.utf8.count

examples/llama.android/llama/src/main/cpp/llama-android.cpp

@@ -385,6 +385,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
         jobject intvar_ncur
 ) {
     const auto context = reinterpret_cast<llama_context *>(context_pointer);
+    const auto sampling = reinterpret_cast<llama_sampling *>(llama_get_sampling(context));
     const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
     const auto model = llama_get_model(context);
 
@@ -405,7 +406,7 @@ Java_android_llama_cpp_LLamaAndroid_completion_1loop(
     llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
 
     // sample the most likely token
-    const auto new_token_id = llama_sample_token_greedy(context, &candidates_p);
+    const auto new_token_id = llama_sampling_sample_greedy(sampling, &candidates_p);
 
     const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
     if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) {

examples/llama.swiftui/llama.cpp.swift/LibLlama.swift

@@ -24,6 +24,7 @@ func llama_batch_add(_ batch: inout llama_batch, _ id: llama_token, _ pos: llama
 actor LlamaContext {
     private var model: OpaquePointer
     private var context: OpaquePointer
+    private var sampling: OpaquePointer
     private var batch: llama_batch
     private var tokens_list: [llama_token]
     var is_done: Bool = false
@@ -42,12 +43,14 @@ actor LlamaContext {
         self.tokens_list = []
         self.batch = llama_batch_init(512, 0, 1)
         self.temporary_invalid_cchars = []
+        self.sampling = llama_get_sampling(context)
     }
 
     deinit {
         llama_batch_free(batch)
         llama_free(context)
         llama_free_model(model)
+        llama_sampling_free(sampling)
         llama_backend_free()
     }
 
@@ -156,7 +159,7 @@ actor LlamaContext {
         candidates.withUnsafeMutableBufferPointer() { buffer in
             var candidates_p = llama_token_data_array(data: buffer.baseAddress, size: buffer.count, sorted: false)
 
-            new_token_id = llama_sample_token_greedy(context, &candidates_p)
+            new_token_id = llama_sampling_sample_greedy(sampling, &candidates_p)
         }
 
         if llama_token_is_eog(model, new_token_id) || n_cur == n_len {

include/llama.h

@@ -1144,7 +1144,7 @@ extern "C" {
                             float * mu);
 
     /// @details Selects the token with the highest probability.
-    ///          Does not compute the token probabilities. Use llama_sample_softmax() instead.
+    ///          Does not compute the token probabilities. Use llama_sampling_softmax() instead.
     LLAMA_API llama_token llama_sampling_sample_greedy(
             struct llama_sampling * smpl,
            llama_token_data_array * candidates);