examples : dedup simple

Makefile

@@ -1,5 +1,5 @@
 # Define the default target now so that it is always the first target
-BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf gguf-llama-simple gptneox-main
+BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf gptneox-main
 
 # Binaries only useful for tests
 TEST_TARGETS = tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
@@ -388,9 +388,6 @@ embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-te
 gguf: examples/gguf/gguf.cpp                                  build-info.h ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-gguf-llama-simple: examples/gguf/gguf-llama-simple.cpp        build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-
 gptneox-main: gptneox-main.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 

examples/gguf/gguf-llama-simple.cpp

@@ -1,129 +0,0 @@
-#ifndef _GNU_SOURCE
-#define _GNU_SOURCE
-#endif
-
-#include "common.h"
-#include "llama.h"
-#include "build-info.h"
-
-#include <cmath>
-#include <cstdio>
-#include <string>
-#include <vector>
-
-int main(int argc, char ** argv) {
-    gpt_params params;
-
-    if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
-        return 1 ;
-    }
-
-    if (argc >= 2) {
-        params.model = argv[1];
-    }
-
-    if (argc >= 3) {
-        params.prompt = argv[2];
-    }
-
-    if (params.prompt.empty()) {
-        params.prompt = "Hello my name is";
-    }
-
-    // init LLM
-
-    llama_backend_init(params.numa);
-
-    llama_context_params ctx_params = llama_context_default_params();
-
-    llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
-
-    if (model == NULL) {
-        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
-        return 1;
-    }
-
-    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
-
-    // tokenize the prompt
-
-    std::vector<llama_token> tokens_list;
-    tokens_list = ::llama_tokenize(ctx, params.prompt, true);
-
-    const int max_context_size     = llama_n_ctx(ctx);
-    const int max_tokens_list_size = max_context_size - 4;
-
-    if ((int) tokens_list.size() > max_tokens_list_size) {
-        fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) tokens_list.size(), max_tokens_list_size);
-        return 1;
-    }
-
-    fprintf(stderr, "\n\n");
-
-    for (auto id : tokens_list) {
-        fprintf(stderr, "%s", llama_token_to_str(ctx, id).c_str());
-    }
-
-    fflush(stderr);
-
-    // main loop
-
-    // The LLM keeps a contextual cache memory of previous token evaluation.
-    // Usually, once this cache is full, it is required to recompute a compressed context based on previous
-    // tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
-    // example, we will just stop the loop once this cache is full or once an end of stream is detected.
-
-    const int n_gen = std::min(32, max_context_size);
-
-    while (llama_get_kv_cache_token_count(ctx) < n_gen) {
-        // evaluate the transformer
-
-        if (llama_eval(ctx, tokens_list.data(), int(tokens_list.size()), llama_get_kv_cache_token_count(ctx), params.n_threads)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
-            return 1;
-        }
-
-        tokens_list.clear();
-
-        // sample the next token
-
-        llama_token new_token_id = 0;
-
-        auto logits  = llama_get_logits(ctx);
-        auto n_vocab = llama_n_vocab(ctx);
-
-        std::vector<llama_token_data> candidates;
-        candidates.reserve(n_vocab);
-
-        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-            candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
-        }
-
-        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
-
-        new_token_id = llama_sample_token_greedy(ctx , &candidates_p);
-
-        // is it an end of stream ?
-        if (new_token_id == llama_token_eos()) {
-            fprintf(stderr, " [end of text]\n");
-            break;
-        }
-
-        // print the new token :
-        printf("%s", llama_token_to_str(ctx, new_token_id).c_str());
-        fflush(stdout);
-
-        // push this new token for next evaluation
-        tokens_list.push_back(new_token_id);
-    }
-
-    llama_free(ctx);
-    llama_free_model(model);
-
-    llama_backend_free();
-
-    fprintf(stderr, "\n\n");
-
-    return 0;
-}

examples/simple/simple.cpp

@@ -36,16 +36,17 @@ int main(int argc, char ** argv) {
 
     llama_backend_init(params.numa);
 
-    llama_model * model;
+    llama_context_params ctx_params = llama_context_default_params();
-    llama_context * ctx;
 
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
 
     if (model == NULL) {
         fprintf(stderr , "%s: error: unable to load model\n" , __func__);
         return 1;
     }
 
+    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+
     // tokenize the prompt
 
     std::vector<llama_token> tokens_list;
@@ -74,7 +75,9 @@ int main(int argc, char ** argv) {
     // tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
     // example, we will just stop the loop once this cache is full or once an end of stream is detected.
 
-    while (llama_get_kv_cache_token_count( ctx ) < max_context_size) {
+    const int n_gen = std::min(32, max_context_size);
+
+    while (llama_get_kv_cache_token_count(ctx) < n_gen) {
         // evaluate the transformer
 
         if (llama_eval(ctx, tokens_list.data(), int(tokens_list.size()), llama_get_kv_cache_token_count(ctx), params.n_threads)) {
@@ -114,7 +117,6 @@ int main(int argc, char ** argv) {
 
         // push this new token for next evaluation
         tokens_list.push_back(new_token_id);
-
     }
 
     llama_free(ctx);
@@ -122,5 +124,7 @@ int main(int argc, char ** argv) {
 
     llama_backend_free();
 
+    fprintf(stderr, "\n\n");
+
     return 0;
 }