examples : do not use common library in simple example

Author: slaren

examples/simple/CMakeLists.txt

@@ -1,5 +1,5 @@
 set(TARGET llama-simple)
 add_executable(${TARGET} simple.cpp)
 install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/simple/simple.cpp

@@ -1,41 +1,38 @@
-#include "arg.h"
-#include "common.h"
-#include "log.h"
 #include "llama.h"
-
+#include <cstdio>
+#include <string>
 #include <vector>
 
 static void print_usage(int, char ** argv) {
-    LOG("\nexample usage:\n");
-    LOG("\n    %s -m model.gguf -p \"Hello my name is\" -n 32\n", argv[0]);
-    LOG("\n");
+    printf("\nexample usage:\n");
+    printf("\n    %s <model.gguf> [prompt]\n", argv[0]);
+    printf("\n");
 }
 
 int main(int argc, char ** argv) {
-    gpt_params params;
-
-    params.prompt = "Hello my name is";
-    params.n_predict = 32;
+    std::string model_path;
+    std::string prompt = "Hello my name is";
+    int n_predict = 32;
 
-    if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) {
+    if (argc < 2) {
+        print_usage(argc, argv);
         return 1;
     }
+    model_path = argv[1];
 
-    gpt_init();
-
-    // total length of the sequence including the prompt
-    const int n_predict = params.n_predict;
-
-    // init LLM
-
-    llama_backend_init();
-    llama_numa_init(params.numa);
+    if (argc > 2) {
+        prompt = argv[2];
+        for (int i = 3; i < argc; i++) {
+            prompt += " ";
+            prompt += argv[i];
+        }
+    }
 
     // initialize the model
 
-    llama_model_params model_params = llama_model_params_from_gpt_params(params);
-
-    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
+    llama_model_params model_params = llama_model_default_params();
+    model_params.n_gpu_layers = 99; // offload all layers to GPU
+    llama_model * model = llama_load_model_from_file(model_path.c_str(), model_params);
 
     if (model == NULL) {
         fprintf(stderr , "%s: error: unable to load model\n" , __func__);
@@ -44,8 +41,9 @@ int main(int argc, char ** argv) {
 
     // initialize the context
 
-    llama_context_params ctx_params = llama_context_params_from_gpt_params(params);
-
+    llama_context_params ctx_params = llama_context_default_params();
+    ctx_params.n_ctx = 512; // maximum context size
+    ctx_params.no_perf = false;
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     if (ctx == NULL) {
@@ -54,54 +52,58 @@ int main(int argc, char ** argv) {
     }
 
     auto sparams = llama_sampler_chain_default_params();
-
     sparams.no_perf = false;
-
     llama_sampler * smpl = llama_sampler_chain_init(sparams);
 
     llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
 
     // tokenize the prompt
 
     std::vector<llama_token> tokens_list;
-    tokens_list = ::llama_tokenize(ctx, params.prompt, true);
+    int n_tokens = llama_tokenize(model, prompt.c_str(), prompt.size(), NULL, 0, true, true);
+    tokens_list.resize(-n_tokens);
+    if (llama_tokenize(model, prompt.c_str(), prompt.size(), tokens_list.data(), tokens_list.size(), true, true) < 0) {
+        fprintf(stderr, "%s: error: failed to tokenize the prompt\n", __func__);
+        return 1;
+    }
 
     const int n_ctx    = llama_n_ctx(ctx);
     const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size());
 
-    LOG("\n");
-    LOG_INF("%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, n_kv_req);
+    fprintf(stderr, "%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, n_kv_req);
+
 
     // make sure the KV cache is big enough to hold all the prompt and generated tokens
     if (n_kv_req > n_ctx) {
-        LOG_ERR("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
-        LOG_ERR("%s:        either reduce n_predict or increase n_ctx\n", __func__);
+        fprintf(stderr, "%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
+        fprintf(stderr, "%s:        either reduce n_predict or increase n_ctx\n", __func__);
         return 1;
     }
 
     // print the prompt token-by-token
 
-    LOG("\n");
+    fprintf(stderr, "\n");
 
     for (auto id : tokens_list) {
-        LOG("%s", llama_token_to_piece(ctx, id).c_str());
+        char buf[128];
+        int n = llama_token_to_piece(model, id, buf, sizeof(buf), 0, true);
+        if (n < 0) {
+            fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
+            return 1;
+        }
+        std::string s(buf, n);
+        printf("%s", s.c_str());
     }
 
     // create a llama_batch with size 512
     // we use this object to submit token data for decoding
 
-    llama_batch batch = llama_batch_init(512, 0, 1);
+    llama_batch batch = llama_batch_get_one(tokens_list.data(), tokens_list.size(), 0, 0);
 
     // evaluate the initial prompt
-    for (size_t i = 0; i < tokens_list.size(); i++) {
-        llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
-    }
-
-    // llama_decode will output logits only for the last token of the prompt
-    batch.logits[batch.n_tokens - 1] = true;
 
     if (llama_decode(ctx, batch) != 0) {
-        LOG("%s: llama_decode() failed\n", __func__);
+        fprintf(stderr, "%s: llama_decode() failed\n", __func__);
         return 1;
     }
 
@@ -114,24 +116,28 @@ int main(int argc, char ** argv) {
 
     while (n_cur <= n_predict) {
         // sample the next token
+        llama_token new_token_id = llama_sampler_sample(smpl, ctx, -1);
         {
-            const llama_token new_token_id = llama_sampler_sample(smpl, ctx, -1);
 
             // is it an end of generation?
             if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) {
-                LOG("\n");
+                fprintf(stderr, "\n");
 
                 break;
             }
 
-            LOG("%s", llama_token_to_piece(ctx, new_token_id).c_str());
+            char buf[128];
+            int n = llama_token_to_piece(model, new_token_id, buf, sizeof(buf), 0, true);
+            if (n < 0) {
+                fprintf(stderr, "%s: error: failed to convert token to piece\n", __func__);
+                return 1;
+            }
+            std::string s(buf, n);
+            printf("%s", s.c_str());
             fflush(stdout);
 
             // prepare the next batch
-            llama_batch_clear(batch);
-
-            // push this new token for next evaluation
-            llama_batch_add(batch, new_token_id, n_cur, { 0 }, true);
+            batch = llama_batch_get_one(&new_token_id, 1, n_cur, 0);
 
             n_decode += 1;
         }
@@ -140,30 +146,26 @@ int main(int argc, char ** argv) {
 
         // evaluate the current batch with the transformer model
         if (llama_decode(ctx, batch)) {
-            LOG_ERR("%s : failed to eval, return code %d\n", __func__, 1);
+            fprintf(stderr, "%s : failed to eval, return code %d\n", __func__, 1);
             return 1;
         }
     }
 
-    LOG("\n");
+    fprintf(stderr, "\n");
 
     const auto t_main_end = ggml_time_us();
 
-    LOG_INF("%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
+    fprintf(stderr, "%s: decoded %d tokens in %.2f s, speed: %.2f t/s\n",
             __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
 
-    LOG("\n");
+    fprintf(stderr, "\n");
     llama_perf_sampler_print(smpl);
     llama_perf_context_print(ctx);
+    fprintf(stderr, "\n");
 
-    LOG("\n");
-
-    llama_batch_free(batch);
     llama_sampler_free(smpl);
     llama_free(ctx);
     llama_free_model(model);
 
-    llama_backend_free();
-
     return 0;
 }