initial commit, going through initializations

examples/lookup/CMakeLists.txt

@@ -0,0 +1,5 @@
+set(TARGET lookup)
+add_executable(${TARGET} lookup.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/lookup/lookup.cpp

@@ -0,0 +1,113 @@
+#include "common.h"
+#include "llama.h"
+
+#include <cmath>
+#include <cstdio>
+#include <string>
+#include <vector>
+
+/*
+def find_candidate_pred_tokens(input_ids, max_ngram_size=3, num_pred_tokens=10):
+    input_length = input_ids.size(1)
+
+    for ngram_size in range(max_ngram_size, 0, -1):
+        # Extract the last n tokens as our search ngram
+        ngram = input_ids[0, -ngram_size:].tolist()
+
+        # Create sliding windows of size ngram_size
+        windows = input_ids.unfold(dimension=1, size=ngram_size, step=1)
+
+        # Convert ngram to a tensor for comparison
+        ngram_tensor = torch.tensor(ngram, device=input_ids.device).unsqueeze(0)
+
+        # Find where the windows match the ngram
+        matches = (windows == ngram_tensor).all(dim=2)
+
+        # Get the indices of matches
+        match_indices = matches.nonzero(as_tuple=True)[1]
+
+        # Iterate through match indices to find a valid continuation
+        for idx in match_indices:
+            start_idx = idx + ngram_size
+            end_idx = start_idx + num_pred_tokens
+            # Ensure we don't go beyond the length of input_ids and avoid self-match
+            if end_idx <= input_length and start_idx < input_length - ngram_size:
+                return input_ids[0, start_idx:end_idx]
+
+    # If no match is found, return an empty tensor
+    return torch.tensor([], dtype=torch.long, device=input_ids.device)
+*/
+
+int main(int argc, char ** argv){
+    gpt_params params;
+
+    if(gpt_params_parse(argc, argv, params) == false){
+        return 1;
+    }
+
+    // maximum n-grams to search for in prompt
+    const int max_ngram_size = 3;
+
+    // length of the candidate sequence, if match is found
+    const int num_pred_tokens = 10;
+
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("lookup", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc, argv);
+#endif // LOG_DISABLE_LOGS
+
+    // init llama.cpp
+    llama_backend_init(params.numa);
+
+    llama_model * model = NULL;
+    llama_context * ctx = NULL;
+
+    // load the model
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
+
+    // tokenize the prompt
+    const bool add_bos = llama_should_add_bos_token(model);
+    LOG("add_bos tgt: %d\n", add_bos);
+    
+    std::vector<llama_token> inp;
+    inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
+
+    const int max_context_size     = llama_n_ctx(ctx);
+    const int max_tokens_list_size = max_context_size - 4;
+
+    if ((int) inp.size() > max_tokens_list_size) {
+        fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
+        return 1;
+    }
+
+    fprintf(stderr, "\n\n");
+
+    for (auto id : inp) {
+        fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
+    }
+
+    fflush(stderr);
+
+    const int n_input = inp.size();
+
+    const auto t_enc_start = ggml_time_us();
+
+    llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0,           0));
+    llama_decode(ctx, llama_batch_get_one(&inp.back(),           1, n_input - 1, 0));
+
+    const auto t_enc_end = ggml_time_us();
+
+    int n_accept  = 0;
+    
+    int n_past = inp.size();
+
+    bool has_eos = false;
+
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
+
+    const auto t_dec_start = ggml_time_us();
+
+
+
+}