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	#include "common.h"

	#include "console.h"
	#include "llama.h"

	#include <cassert>
	#include <cinttypes>
	#include <cmath>
	#include <cstdio>
	#include <cstring>
	#include <ctime>
	#include <fstream>
	#include <iostream>
	#include <sstream>
	#include <string>
	#include <vector>

	#if defined (__unix__) \|\| (defined (__APPLE__) && defined (__MACH__))
	#include <signal.h>
	#include <unistd.h>
	#elif defined (_WIN32)
	#define WIN32_LEAN_AND_MEAN
	#ifndef NOMINMAX
	#define NOMINMAX
	#endif
	#include <windows.h>
	#include <signal.h>
	#endif

	#if defined(_MSC_VER)
	#pragma warning(disable: 4244 4267) // possible loss of data
	#endif

	static llama_context ** g_ctx;
	static llama_model ** g_model;
	static gpt_params * g_params;
	static std::vector<llama_token> * g_input_tokens;
	static std::ostringstream * g_output_ss;
	static std::vector<llama_token> * g_output_tokens;
	static bool is_interacting = false;

	static bool file_exists(const std::string &path) {
	std::ifstream f(path.c_str());
	return f.good();
	}

	static bool file_is_empty(const std::string &path) {
	std::ifstream f;
	f.exceptions(std::ifstream::failbit \| std::ifstream::badbit);
	f.open(path.c_str(), std::ios::in \| std::ios::binary \| std::ios::ate);
	return f.tellg() == 0;
	}

	static void write_logfile(
	const llama_context * ctx, const gpt_params & params, const llama_model * model,
	const std::vector<llama_token> & input_tokens, const std::string & output,
	const std::vector<llama_token> & output_tokens
	) {
	if (params.logdir.empty()) {
	return;
	}

	const std::string timestamp = get_sortable_timestamp();

	const bool success = create_directory_with_parents(params.logdir);
	if (!success) {
	fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
	__func__, params.logdir.c_str());
	return;
	}

	const std::string logfile_path = params.logdir + timestamp + ".yml";
	FILE * logfile = fopen(logfile_path.c_str(), "w");

	if (logfile == NULL) {
	fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
	return;
	}

	fprintf(logfile, "binary: main\n");
	char model_desc[128];
	llama_model_desc(model, model_desc, sizeof(model_desc));
	dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);

	fprintf(logfile, "\n");
	fprintf(logfile, "######################\n");
	fprintf(logfile, "# Generation Results #\n");
	fprintf(logfile, "######################\n");
	fprintf(logfile, "\n");

	dump_string_yaml_multiline(logfile, "output", output.c_str());
	dump_vector_int_yaml(logfile, "output_tokens", output_tokens);

	llama_dump_timing_info_yaml(logfile, ctx);
	fclose(logfile);
	}

	#if defined (__unix__) \|\| (defined (__APPLE__) && defined (__MACH__)) \|\| defined (_WIN32)
	static void sigint_handler(int signo) {
	if (signo == SIGINT) {
	if (!is_interacting && g_params->interactive) {
	is_interacting = true;
	} else {
	console::cleanup();
	printf("\n");
	// llama_print_timings(*g_ctx);
	write_logfile(g_ctx, g_params, g_model, g_input_tokens, g_output_ss->str(), *g_output_tokens);
	_exit(130);
	}
	}
	}
	#endif

	static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
	(void) level;
	(void) user_data;
	// LOG_TEE("%s", text);
	}

	int main(int argc, char ** argv) {
	gpt_params params;
	g_params = &params;

	if (!gpt_params_parse(argc, argv, params)) {
	return 1;
	}
	llama_sampling_params & sparams = params.sparams;

	#ifndef LOG_DISABLE_LOGS
	log_set_target(log_filename_generator("main", "log"));
	// LOG_TEE("Log start\n");
	log_dump_cmdline(argc, argv);
	llama_log_set(llama_log_callback_logTee, nullptr);
	#endif // LOG_DISABLE_LOGS

	// TODO: Dump params ?
	//LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity));

	// save choice to use color for later
	// (note for later: this is a slightly awkward choice)
	console::init(params.simple_io, params.use_color);
	atexit([]() { console::cleanup(); });

	if (params.logits_all) {
	printf("\n************\n");
	printf("%s: please use the 'perplexity' tool for perplexity calculations\n", __func__);
	printf("************\n\n");

	return 0;
	}

	if (params.embedding) {
	printf("\n************\n");
	printf("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
	printf("************\n\n");

	return 0;
	}

	if (params.n_ctx != 0 && params.n_ctx < 8) {
	LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
	params.n_ctx = 8;
	}

	if (params.rope_freq_base != 0.0) {
	LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
	}

	if (params.rope_freq_scale != 0.0) {
	LOG_TEE("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
	}

	// LOG_TEE("%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);
	// LOG_TEE("%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);

	if (params.seed == LLAMA_DEFAULT_SEED) {
	params.seed = time(NULL);
	}

	// LOG_TEE("%s: seed = %u\n", __func__, params.seed);

	std::mt19937 rng(params.seed);
	if (params.random_prompt) {
	params.prompt = gpt_random_prompt(rng);
	}

	LOG("%s: llama backend init\n", __func__);
	llama_backend_init();
	llama_numa_init(params.numa);

	llama_model * model;
	llama_context * ctx;
	llama_context * ctx_guidance = NULL;
	g_model = &model;
	g_ctx = &ctx;

	// load the model and apply lora adapter, if any
	LOG("%s: load the model and apply lora adapter, if any\n", __func__);
	std::tie(model, ctx) = llama_init_from_gpt_params(params);
	if (sparams.cfg_scale > 1.f) {
	struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
	ctx_guidance = llama_new_context_with_model(model, lparams);
	}

	if (model == NULL) {
	LOG_TEE("%s: error: unable to load model\n", __func__);
	return 1;
	}

	const int n_ctx_train = llama_n_ctx_train(model);
	const int n_ctx = llama_n_ctx(ctx);
	// LOG("n_ctx: %d\n", n_ctx);

	if (n_ctx > n_ctx_train) {
	LOG_TEE("%s: warning: model was trained on only %d context tokens (%d specified)\n",
	__func__, n_ctx_train, n_ctx);
	}

	// print system information
	// {
	// LOG_TEE("\n");
	// LOG_TEE("%s\n", get_system_info(params).c_str());
	// }

	std::string path_session = params.path_prompt_cache;
	std::vector<llama_token> session_tokens;

	if (!path_session.empty()) {
	// LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
	if (!file_exists(path_session)) {
	// LOG_TEE("%s: session file does not exist, will create.\n", __func__);
	} else if (file_is_empty(path_session)) {
	// LOG_TEE("%s: The session file is empty. A new session will be initialized.\n", __func__);
	} else {
	// The file exists and is not empty
	session_tokens.resize(n_ctx);
	size_t n_token_count_out = 0;
	if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
	LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
	return 1;
	}
	session_tokens.resize(n_token_count_out);
	llama_set_rng_seed(ctx, params.seed);
	// LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
	}
	}

	const bool add_bos = llama_should_add_bos_token(model);
	// LOG("add_bos: %d\n", add_bos);

	std::vector<llama_token> embd_inp;

	if (params.interactive_first \|\| params.instruct \|\| params.chatml \|\| !params.prompt.empty() \|\| session_tokens.empty()) {
	LOG("tokenize the prompt\n");
	if (params.chatml) {
	params.prompt = "<\|im_start\|>system\n" + params.prompt + "<\|im_end\|>";
	}
	embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
	} else {
	LOG("use session tokens\n");
	embd_inp = session_tokens;
	}

	LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
	LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());

	// Should not run without any tokens
	if (embd_inp.empty()) {
	embd_inp.push_back(llama_token_bos(model));
	LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
	}

	// Tokenize negative prompt
	std::vector<llama_token> guidance_inp;
	int guidance_offset = 0;
	int original_prompt_len = 0;
	if (ctx_guidance) {
	LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));

	guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);
	LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());

	std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
	LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());

	original_prompt_len = original_inp.size();
	guidance_offset = (int)guidance_inp.size() - original_prompt_len;
	LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
	LOG("guidance_offset: %s", log_tostr(guidance_offset));
	}

	if ((int) embd_inp.size() > n_ctx - 4) {
	LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
	return 1;
	}

	// debug message about similarity of saved session, if applicable
	size_t n_matching_session_tokens = 0;
	if (!session_tokens.empty()) {
	for (llama_token id : session_tokens) {
	if (n_matching_session_tokens >= embd_inp.size() \|\| id != embd_inp[n_matching_session_tokens]) {
	break;
	}
	n_matching_session_tokens++;
	}
	// if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
	// LOG_TEE("%s: using full prompt from session file\n", __func__);
	// } else if (n_matching_session_tokens >= embd_inp.size()) {
	// LOG_TEE("%s: session file has exact match for prompt!\n", __func__);
	// } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
	// LOG_TEE("%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
	// __func__, n_matching_session_tokens, embd_inp.size());
	// } else {
	// LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n",
	// __func__, n_matching_session_tokens, embd_inp.size());
	// }

	// remove any "future" tokens that we might have inherited from the previous session
	llama_kv_cache_seq_rm(ctx, -1, n_matching_session_tokens, -1);
	}

	LOGLN(
	"recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu",
	log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size());

	// if we will use the cache for the full prompt without reaching the end of the cache, force
	// reevaluation of the last token token to recalculate the cached logits
	if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) {
	LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1);

	session_tokens.resize(embd_inp.size() - 1);
	}

	// number of tokens to keep when resetting context
	if (params.n_keep < 0 \|\| params.n_keep > (int) embd_inp.size() \|\| params.instruct \|\| params.chatml) {
	params.n_keep = (int)embd_inp.size();
	} else {
	params.n_keep += add_bos; // always keep the BOS token
	}

	// prefix & suffix for instruct mode
	const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
	const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true);

	// LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
	// LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());

	// chatml prefix & suffix
	const auto cml_pfx = ::llama_tokenize(ctx, "\n<\|im_start\|>user\n", add_bos, true);
	const auto cml_sfx = ::llama_tokenize(ctx, "<\|im_end\|>\n<\|im_start\|>assistant\n", false, true);

	// LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
	// LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str());

	// in instruct mode, we inject a prefix and a suffix to each input by the user
	if (params.instruct) {
	params.interactive_first = true;
	params.antiprompt.emplace_back("### Instruction:\n\n");
	}
	// similar for chatml mode
	else if (params.chatml) {
	params.interactive_first = true;
	params.antiprompt.emplace_back("<\|im_start\|>user\n");
	}

	// enable interactive mode if interactive start is specified
	if (params.interactive_first) {
	params.interactive = true;
	}

	// if (params.verbose_prompt) {
	// LOG_TEE("\n");
	// LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
	// LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
	// for (int i = 0; i < (int) embd_inp.size(); i++) {
	// LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
	// }

	// if (ctx_guidance) {
	// LOG_TEE("\n");
	// LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
	// LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
	// for (int i = 0; i < (int) guidance_inp.size(); i++) {
	// LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
	// }
	// }

	// if (params.n_keep > add_bos) {
	// LOG_TEE("%s: static prompt based on n_keep: '", __func__);
	// for (int i = 0; i < params.n_keep; i++) {
	// LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
	// }
	// LOG_TEE("'\n");
	// }
	// LOG_TEE("\n");
	// }

	// ctrl+C handling
	{
	#if defined (__unix__) \|\| (defined (__APPLE__) && defined (__MACH__))
	struct sigaction sigint_action;
	sigint_action.sa_handler = sigint_handler;
	sigemptyset (&sigint_action.sa_mask);
	sigint_action.sa_flags = 0;
	sigaction(SIGINT, &sigint_action, NULL);
	#elif defined (_WIN32)
	auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
	return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
	};
	SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
	#endif
	}

	if (params.interactive) {
	// LOG_TEE("%s: interactive mode on.\n", __func__);

	if (!params.antiprompt.empty()) {
	for (const auto & antiprompt : params.antiprompt) {
	// LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
	if (params.verbose_prompt) {
	auto tmp = ::llama_tokenize(ctx, antiprompt, false, true);
	for (int i = 0; i < (int) tmp.size(); i++) {
	// LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
	}
	}
	}
	}

	// if (params.input_prefix_bos) {
	// LOG_TEE("Input prefix with BOS\n");
	// }

	// if (!params.input_prefix.empty()) {
	// LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str());
	// if (params.verbose_prompt) {
	// auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true);
	// for (int i = 0; i < (int) tmp.size(); i++) {
	// LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
	// }
	// }
	// }

	// if (!params.input_suffix.empty()) {
	// LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
	// if (params.verbose_prompt) {
	// auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true);
	// for (int i = 0; i < (int) tmp.size(); i++) {
	// LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
	// }
	// }
	// }
	}
	// LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
	// LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
	// LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);

	// group-attention state
	// number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
	int ga_i = 0;

	const int ga_n = params.grp_attn_n;
	const int ga_w = params.grp_attn_w;

	if (ga_n != 1) {
	GGML_ASSERT(ga_n > 0 && "grp_attn_n must be positive"); // NOLINT
	GGML_ASSERT(ga_w % ga_n == 0 && "grp_attn_w must be a multiple of grp_attn_n"); // NOLINT
	//GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of grp_attn_w"); // NOLINT
	//GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
	// LOG_TEE("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
	}
	// LOG_TEE("\n\n");

	if (params.interactive) {
	const char *control_message;
	if (params.multiline_input) {
	control_message = " - To return control to LLaMa, end your input with '\\'.\n"
	" - To return control without starting a new line, end your input with '/'.\n";
	} else {
	control_message = " - Press Return to return control to LLaMa.\n"
	" - To return control without starting a new line, end your input with '/'.\n"
	" - If you want to submit another line, end your input with '\\'.\n";
	}
	// LOG_TEE("== Running in interactive mode. ==\n");
	// #if defined (__unix__) \|\| (defined (__APPLE__) && defined (__MACH__)) \|\| defined (_WIN32)
	// // LOG_TEE( " - Press Ctrl+C to interject at any time.\n");
	// #endif
	// LOG_TEE( "%s\n", control_message);

	is_interacting = params.interactive_first;
	}

	bool is_antiprompt = false;
	bool input_echo = true;
	bool display = true;
	bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < embd_inp.size();

	int n_past = 0;
	int n_remain = params.n_predict;
	int n_consumed = 0;
	int n_session_consumed = 0;
	int n_past_guidance = 0;

	std::vector<int> input_tokens; g_input_tokens = &input_tokens;
	std::vector<int> output_tokens; g_output_tokens = &output_tokens;
	std::ostringstream output_ss; g_output_ss = &output_ss;

	// the first thing we will do is to output the prompt, so set color accordingly
	console::set_display(console::prompt);
	display = params.display_prompt;

	std::vector<llama_token> embd;
	std::vector<llama_token> embd_guidance;

	// tokenized antiprompts
	std::vector<std::vector<llama_token>> antiprompt_ids;

	antiprompt_ids.reserve(params.antiprompt.size());
	for (const std::string & antiprompt : params.antiprompt) {
	antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
	}

	struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);

	while ((n_remain != 0 && !is_antiprompt) \|\| params.interactive) {
	// predict
	if (!embd.empty()) {
	// Note: (n_ctx - 4) here is to match the logic for commandline prompt handling via
	// --prompt or --file which uses the same value.
	int max_embd_size = n_ctx - 4;

	// Ensure the input doesn't exceed the context size by truncating embd if necessary.
	if ((int) embd.size() > max_embd_size) {
	const int skipped_tokens = (int) embd.size() - max_embd_size;
	embd.resize(max_embd_size);

	console::set_display(console::error);
	printf("<<input too long: skipped %d token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
	console::set_display(console::reset);
	fflush(stdout);
	}

	if (ga_n == 1) {
	// infinite text generation via context shifting
	// if we run out of context:
	// - take the n_keep first tokens from the original prompt (via n_past)
	// - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
	if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
	if (params.n_predict == -2) {
	// LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
	break;
	}

	const int n_left = n_past - params.n_keep;
	const int n_discard = n_left/2;

	LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
	n_past, n_left, n_ctx, params.n_keep, n_discard);

	llama_kv_cache_seq_rm (ctx, 0, params.n_keep , params.n_keep + n_discard);
	llama_kv_cache_seq_add(ctx, 0, params.n_keep + n_discard, n_past, -n_discard);

	n_past -= n_discard;

	if (ctx_guidance) {
	n_past_guidance -= n_discard;
	}

	LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);

	LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());

	LOG("clear session path\n");
	path_session.clear();
	}
	} else {
	// context extension via Self-Extend
	while (n_past >= ga_i + ga_w) {
	const int ib = (ga_n*ga_i)/ga_w;
	const int bd = (ga_w/ga_n)*(ga_n - 1);
	const int dd = (ga_w/ga_n) - ib*bd - ga_w;

	LOG("\n");
	LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ibbd, ga_i + ibbd, n_past + ib*bd);
	LOG("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ibbd, ga_i + ibbd + ga_w, ga_n, (ga_i + ibbd)/ga_n, (ga_i + ibbd + ga_w)/ga_n);
	LOG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ibbd + ga_w, n_past + ibbd, dd, ga_i + ibbd + ga_w + dd, n_past + ibbd + dd);

	llama_kv_cache_seq_add(ctx, 0, ga_i, n_past, ib*bd);
	llama_kv_cache_seq_div(ctx, 0, ga_i + ibbd, ga_i + ibbd + ga_w, ga_n);
	llama_kv_cache_seq_add(ctx, 0, ga_i + ibbd + ga_w, n_past + ibbd, dd);

	n_past -= bd;

	ga_i += ga_w/ga_n;

	LOG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i);
	}
	}

	// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
	if (n_session_consumed < (int) session_tokens.size()) {
	size_t i = 0;
	for ( ; i < embd.size(); i++) {
	if (embd[i] != session_tokens[n_session_consumed]) {
	session_tokens.resize(n_session_consumed);
	break;
	}

	n_past++;
	n_session_consumed++;

	if (n_session_consumed >= (int) session_tokens.size()) {
	++i;
	break;
	}
	}
	if (i > 0) {
	embd.erase(embd.begin(), embd.begin() + i);
	}
	}

	// evaluate tokens in batches
	// embd is typically prepared beforehand to fit within a batch, but not always
	if (ctx_guidance) {
	int input_size = 0;
	llama_token * input_buf = NULL;

	if (n_past_guidance < (int) guidance_inp.size()) {
	// Guidance context should have the same data with these modifications:
	//
	// * Replace the initial prompt
	// * Shift everything by guidance_offset
	embd_guidance = guidance_inp;
	if (embd.begin() + original_prompt_len < embd.end()) {
	embd_guidance.insert(
	embd_guidance.end(),
	embd.begin() + original_prompt_len,
	embd.end()
	);
	}

	input_buf = embd_guidance.data();
	input_size = embd_guidance.size();

	LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str());
	} else {
	input_buf = embd.data();
	input_size = embd.size();
	}

	for (int i = 0; i < input_size; i += params.n_batch) {
	int n_eval = std::min(input_size - i, params.n_batch);
	if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) {
	// LOG_TEE("%s : failed to eval\n", __func__);
	return 1;
	}

	n_past_guidance += n_eval;
	}
	}

	for (int i = 0; i < (int) embd.size(); i += params.n_batch) {
	int n_eval = (int) embd.size() - i;
	if (n_eval > params.n_batch) {
	n_eval = params.n_batch;
	}

	LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());

	if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {
	LOG_TEE("%s : failed to eval\n", __func__);
	return 1;
	}

	n_past += n_eval;

	// LOG("n_past = %d\n", n_past);
	// Display total tokens alongside total time
	// if (params.n_print > 0 && n_past % params.n_print == 0) {
	// LOG_TEE("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx);
	// }
	}

	if (!embd.empty() && !path_session.empty()) {
	session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
	n_session_consumed = session_tokens.size();
	}
	}

	embd.clear();
	embd_guidance.clear();

	if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
	// optionally save the session on first sample (for faster prompt loading next time)
	if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
	need_to_save_session = false;
	llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());

	LOG("saved session to %s\n", path_session.c_str());
	}

	const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);

	llama_sampling_accept(ctx_sampling, ctx, id, true);

	LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str());

	embd.push_back(id);

	// echo this to console
	input_echo = true;

	// decrement remaining sampling budget
	--n_remain;

	LOG("n_remain: %d\n", n_remain);
	} else {
	// some user input remains from prompt or interaction, forward it to processing
	LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
	while ((int) embd_inp.size() > n_consumed) {
	embd.push_back(embd_inp[n_consumed]);

	// push the prompt in the sampling context in order to apply repetition penalties later
	// for the prompt, we don't apply grammar rules
	llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false);

	++n_consumed;
	if ((int) embd.size() >= params.n_batch) {
	break;
	}
	}
	}

	// display text
	if (input_echo && display) {
	for (auto id : embd) {
	const std::string token_str = llama_token_to_piece(ctx, id);
	printf("%s", token_str.c_str());

	if (embd.size() > 1) {
	input_tokens.push_back(id);
	} else {
	output_tokens.push_back(id);
	output_ss << token_str;
	}
	}
	fflush(stdout);
	}
	// reset color to default if there is no pending user input
	if (input_echo && (int) embd_inp.size() == n_consumed) {
	console::set_display(console::reset);
	display = true;
	}

	// if not currently processing queued inputs;
	if ((int) embd_inp.size() <= n_consumed) {
	// check for reverse prompt in the last n_prev tokens
	if (!params.antiprompt.empty()) {
	const int n_prev = 32;
	const std::string last_output = llama_sampling_prev_str(ctx_sampling, ctx, n_prev);

	is_antiprompt = false;
	// Check if each of the reverse prompts appears at the end of the output.
	// If we're not running interactively, the reverse prompt might be tokenized with some following characters
	// so we'll compensate for that by widening the search window a bit.
	for (std::string & antiprompt : params.antiprompt) {
	size_t extra_padding = params.interactive ? 0 : 2;
	size_t search_start_pos = last_output.length() > static_cast<size_t>(antiprompt.length() + extra_padding)
	? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding)
	: 0;

	if (last_output.find(antiprompt, search_start_pos) != std::string::npos) {
	if (params.interactive) {
	is_interacting = true;
	}
	is_antiprompt = true;
	break;
	}
	}

	// check for reverse prompt using special tokens
	llama_token last_token = llama_sampling_last(ctx_sampling);
	for (std::vector<llama_token> ids : antiprompt_ids) {
	if (ids.size() == 1 && last_token == ids[0]) {
	if (params.interactive) {
	is_interacting = true;
	}
	is_antiprompt = true;
	break;
	}
	}

	if (is_antiprompt) {
	LOG("found antiprompt: %s\n", last_output.c_str());
	}
	}

	// deal with end of text token in interactive mode
	if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
	LOG("found EOS token\n");

	if (params.interactive) {
	if (!params.antiprompt.empty()) {
	// tokenize and inject first reverse prompt
	const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false, true);
	embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
	is_antiprompt = true;
	}

	is_interacting = true;
	printf("\n");
	} else if (params.instruct \|\| params.chatml) {
	is_interacting = true;
	}
	}

	if (n_past > 0 && is_interacting) {
	LOG("waiting for user input\n");

	if (params.instruct \|\| params.chatml) {
	printf("\n> ");
	}

	if (params.input_prefix_bos) {
	LOG("adding input prefix BOS token\n");
	embd_inp.push_back(llama_token_bos(model));
	}

	std::string buffer;
	if (!params.input_prefix.empty()) {
	LOG("appending input prefix: '%s'\n", params.input_prefix.c_str());
	printf("%s", params.input_prefix.c_str());
	}

	// color user input only
	console::set_display(console::user_input);
	display = params.display_prompt;

	std::string line;
	bool another_line = true;
	do {
	another_line = console::readline(line, params.multiline_input);
	buffer += line;
	} while (another_line);

	// done taking input, reset color
	console::set_display(console::reset);
	display = true;

	// Add tokens to embd only if the input buffer is non-empty
	// Entering a empty line lets the user pass control back
	if (buffer.length() > 1) {
	// append input suffix if any
	if (!params.input_suffix.empty()) {
	LOG("appending input suffix: '%s'\n", params.input_suffix.c_str());
	printf("%s", params.input_suffix.c_str());
	}

	LOG("buffer: '%s'\n", buffer.c_str());

	const size_t original_size = embd_inp.size();

	// instruct mode: insert instruction prefix
	if (params.instruct && !is_antiprompt) {
	LOG("inserting instruction prefix\n");
	n_consumed = embd_inp.size();
	embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
	}
	// chatml mode: insert user chat prefix
	if (params.chatml && !is_antiprompt) {
	LOG("inserting chatml prefix\n");
	n_consumed = embd_inp.size();
	embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end());
	}
	if (params.escape) {
	process_escapes(buffer);
	}

	const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
	const auto line_inp = ::llama_tokenize(ctx, buffer, false, false);
	const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);

	LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());

	embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());
	embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
	embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end());

	// instruct mode: insert response suffix
	if (params.instruct) {
	LOG("inserting instruction suffix\n");
	embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
	}
	// chatml mode: insert assistant chat suffix
	if (params.chatml) {
	LOG("inserting chatml suffix\n");
	embd_inp.insert(embd_inp.end(), cml_sfx.begin(), cml_sfx.end());
	}

	for (size_t i = original_size; i < embd_inp.size(); ++i) {
	const llama_token token = embd_inp[i];
	output_tokens.push_back(token);
	output_ss << llama_token_to_piece(ctx, token);
	}

	n_remain -= line_inp.size();
	LOG("n_remain: %d\n", n_remain);
	} else {
	LOG("empty line, passing control back\n");
	}

	input_echo = false; // do not echo this again
	}

	if (n_past > 0) {
	if (is_interacting) {
	llama_sampling_reset(ctx_sampling);
	}
	is_interacting = false;
	}
	}

	// end of text token
	if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct \|\| params.interactive \|\| params.chatml)) {
	// LOG_TEE(" [end of text]\n");
	break;
	}

	// In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
	// We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size).
	if (params.interactive && n_remain <= 0 && params.n_predict >= 0) {
	n_remain = params.n_predict;
	is_interacting = true;
	}
	}

	if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
	// LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
	llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
	}

	// llama_print_timings(ctx);
	write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);

	if (ctx_guidance) { llama_free(ctx_guidance); }
	llama_free(ctx);
	llama_free_model(model);

	llama_sampling_free(ctx_sampling);
	llama_backend_free();



	return 0;
	}