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@@ -1,6 +1,6 @@ |
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# Define the default target now so that it is always the first target |
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BUILD_TARGETS = \ |
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- main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ |
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+ main repeng quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ |
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simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \ |
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speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o |
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@@ -744,6 +744,10 @@ server: examples/server/server.cpp examples/server/utils.hpp examples/server/htt |
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) |
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$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) |
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+repeng: examples/repeng/repeng.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) |
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+ $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) |
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+ $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) |
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+ |
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gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) |
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$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) |
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$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) |
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@@ -32,17 +32,19 @@ endif() |
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# Add a custom command to rebuild build-info.cpp when .git/index changes |
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add_custom_command( |
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- OUTPUT "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp" |
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+ OUTPUT "${CMAKE_CURRENT_BINARY_DIR}/build-info.cpp" |
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COMMENT "Generating build details from Git" |
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COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION} |
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-DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME} |
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- -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake" |
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+ -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} |
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+ -DOUTPUT_DIR=${CMAKE_CURRENT_BINARY_DIR}/.. |
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+ -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake" |
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WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.." |
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DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX} |
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VERBATIM |
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) |
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set(TARGET build_info) |
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-add_library(${TARGET} OBJECT build-info.cpp) |
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+add_library(${TARGET} OBJECT "${CMAKE_CURRENT_BINARY_DIR}/build-info.cpp") |
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if (BUILD_SHARED_LIBS) |
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set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) |
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endif() |
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@@ -46,4 +46,5 @@ else() |
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add_subdirectory(server) |
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endif() |
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add_subdirectory(export-lora) |
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+ add_subdirectory(repeng) |
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endif() |
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new file mode 100644 |
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@@ -0,0 +1,5 @@ |
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+set(TARGET repeng) |
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+add_executable(${TARGET} repeng.cpp) |
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+install(TARGETS ${TARGET} RUNTIME) |
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+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) |
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+target_compile_features(${TARGET} PRIVATE cxx_std_11) |
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|
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new file mode 100644 |
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@@ -0,0 +1,892 @@ |
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+#include "common.h" |
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+ |
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+#include "console.h" |
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+#include "llama.h" |
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+ |
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+#include <cassert> |
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+#include <cinttypes> |
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+#include <cmath> |
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+#include <cstdio> |
|
+#include <cstring> |
|
+#include <ctime> |
|
+#include <fstream> |
|
+#include <iostream> |
|
+#include <map> |
|
+#include <memory> |
|
+#include <sstream> |
|
+#include <string> |
|
+#include <vector> |
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+#include <deque> |
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+ |
|
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) |
|
+#include <signal.h> |
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+#include <unistd.h> |
|
+#elif defined (_WIN32) |
|
+#define WIN32_LEAN_AND_MEAN |
|
+#ifndef NOMINMAX |
|
+#define NOMINMAX |
|
+#endif |
|
+#include <windows.h> |
|
+#include <signal.h> |
|
+#endif |
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+ |
|
+#if defined(_MSC_VER) |
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+#pragma warning(disable: 4244 4267) // possible loss of data |
|
+#endif |
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+ |
|
+static llama_context ** g_ctx; |
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+static llama_model ** g_model; |
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+static gpt_params * g_params; |
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+static std::vector<llama_token> * g_input_tokens; |
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+static std::ostringstream * g_output_ss; |
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+static std::vector<llama_token> * g_output_tokens; |
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+static bool is_interacting = false; |
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+ |
|
+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; |
|
+} |
|
+ |
|
+#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 { |
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+ console::cleanup(); |
|
+ printf("\n"); |
|
+ llama_print_timings(*g_ctx); |
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+ //write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens); |
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+ _exit(130); |
|
+ } |
|
+ } |
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+} |
|
+#endif |
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+ |
|
+static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) { |
|
+ (void) level; |
|
+ (void) user_data; |
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+ LOG_TEE("%s", text); |
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+} |
|
+ |
|
+static std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params_with_cb_eval( |
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+ gpt_params & params, |
|
+ ggml_backend_sched_eval_callback cb_eval, |
|
+ void * cb_eval_user_data) { |
|
+ auto mparams = llama_model_params_from_gpt_params(params); |
|
+ |
|
+ llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams); |
|
+ if (model == NULL) { |
|
+ fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); |
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+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ |
|
+ auto cparams = llama_context_params_from_gpt_params(params); |
|
+ |
|
+ cparams.cb_eval = cb_eval; |
|
+ cparams.cb_eval_user_data = cb_eval_user_data; |
|
+ |
|
+ llama_context * lctx = llama_new_context_with_model(model, cparams); |
|
+ if (lctx == NULL) { |
|
+ fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ |
|
+ if (!params.control_vectors.empty()) { |
|
+ if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; |
|
+ if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); |
|
+ |
|
+ const auto cvec = llama_control_vector_load(params.control_vectors); |
|
+ if (cvec.n_embd == -1) { |
|
+ llama_free(lctx); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ |
|
+ int err = llama_control_vector_apply(lctx, |
|
+ cvec.data.data(), |
|
+ cvec.data.size(), |
|
+ cvec.n_embd, |
|
+ params.control_vector_layer_start, |
|
+ params.control_vector_layer_end); |
|
+ if (err) { |
|
+ llama_free(lctx); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ } |
|
+ |
|
+ for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { |
|
+ const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]); |
|
+ float lora_scale = std::get<1>(params.lora_adapter[i]); |
|
+ int err = llama_model_apply_lora_from_file(model, |
|
+ lora_adapter.c_str(), |
|
+ lora_scale, |
|
+ ((i > 0) || params.lora_base.empty()) |
|
+ ? NULL |
|
+ : params.lora_base.c_str(), |
|
+ params.n_threads); |
|
+ if (err != 0) { |
|
+ fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); |
|
+ llama_free(lctx); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ } |
|
+ |
|
+ if (params.ignore_eos) { |
|
+ params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; |
|
+ } |
|
+ |
|
+ { |
|
+ LOG("warming up the model with an empty run\n"); |
|
+ |
|
+ std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), }; |
|
+ llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); |
|
+ llama_kv_cache_clear(lctx); |
|
+ llama_synchronize(lctx); |
|
+ llama_reset_timings(lctx); |
|
+ } |
|
+ |
|
+ return std::make_tuple(model, lctx); |
|
+} |
|
+ |
|
+struct eval_callback_state { |
|
+ std::vector<ggml_tensor *> tensors; |
|
+ // For each hidden state tensor, how many tokens have we seen from the |
|
+ // current batch? When n_ubatch < n_batch, we don't see the hidden states |
|
+ // for all tokens at once, but only n_ubatch-sized chunks of tokens. This |
|
+ // keeps track of our progress. |
|
+ std::vector<int> tokens_seen; |
|
+ int first_prompt_idx; |
|
+ std::vector<int> extract_tokens; |
|
+}; |
|
+ |
|
+static bool eval_callback(struct ggml_tensor * t, bool ask, void * user_data) { |
|
+ struct eval_callback_state * eval_state = (eval_callback_state *)user_data; |
|
+ if (ask) { |
|
+ // Report whether we want to observe this tensor. |
|
+ if (strncmp(t->name, "l_out-", 6) == 0) { |
|
+ return true; |
|
+ } else { |
|
+ return false; |
|
+ } |
|
+ } else { |
|
+ // Actually observe the tensor data. |
|
+ |
|
+ if (eval_state->first_prompt_idx >= 0) { |
|
+ // Find the tensor collecting hidden states for the current layer. |
|
+ int layer_idx = -1; |
|
+ ggml_tensor * output_tensor = nullptr; |
|
+ for (size_t i = 0; i < eval_state->tensors.size(); ++i) { |
|
+ auto t2 = eval_state->tensors[i]; |
|
+ if (strcmp(t2->name, t->name) == 0) { |
|
+ output_tensor = t2; |
|
+ layer_idx = i; |
|
+ break; |
|
+ } |
|
+ } |
|
+ |
|
+ if (output_tensor != nullptr) { |
|
+ int ubatch_size = t->ne[1]; |
|
+ int ubatch_start = eval_state->tokens_seen[layer_idx]; |
|
+ int ubatch_end = ubatch_start + ubatch_size; |
|
+ eval_state->tokens_seen[layer_idx] += ubatch_size; |
|
+ |
|
+ int output_idx = eval_state->first_prompt_idx; |
|
+ for (int token_idx : eval_state->extract_tokens) { |
|
+ if (token_idx < ubatch_start || token_idx >= ubatch_end) { |
|
+ ++output_idx; |
|
+ continue; |
|
+ } |
|
+ int input_idx = token_idx - ubatch_start; |
|
+ |
|
+ // Copy the hidden states for the current token into the |
|
+ // output buffer. |
|
+ size_t input_offset = t->nb[1] * input_idx; |
|
+ size_t output_offset = output_tensor->nb[1] * output_idx; |
|
+ assert(t->nb[0] == output_tensor->nb[0]); |
|
+ assert(t->ne[0] == output_tensor->ne[0]); |
|
+ ggml_backend_tensor_get(t, |
|
+ (char *)output_tensor->data + output_offset, |
|
+ input_offset, |
|
+ t->nb[0] * t->ne[0]); |
|
+ //memcpy((char *)output_tensor->data + output_offset, |
|
+ // (char *)t->data + input_offset, |
|
+ // t->nb[0] * t->ne[0]); |
|
+ //std::cerr << "saved " << (t->nb[0] * t->ne[0]) << " bytes of tensor data " |
|
+ // << " for " << t->name << " in slot " << output_idx << "\n"; |
|
+ |
|
+ //float * buf = (float *)((char *)t->data + input_offset); |
|
+ //float * buf = (float *)((char *)output_tensor->data + output_offset); |
|
+ //std::cerr << "prompt " << output_idx |
|
+ // << " tensor contents for " << t->name << ": " |
|
+ // << buf[0] << ", " |
|
+ // << buf[1] << ", " |
|
+ // << buf[2] << " ... " |
|
+ // << buf[4093] << ", " |
|
+ // << buf[4094] << ", " |
|
+ // << buf[4095] << "\n"; |
|
+ |
|
+ ++output_idx; |
|
+ } |
|
+ } |
|
+ } |
|
+ |
|
+ // Continue running |
|
+ return true; |
|
+ } |
|
+} |
|
+ |
|
+struct kv_trie_node { |
|
+ int seq; |
|
+ // Flag to indicate whether the current token (that is, the last token of |
|
+ // the current sequence) has already been retained in the KV cache. When a |
|
+ // token from the old batch is reused for several different prompts in the |
|
+ // new batch, we use this flag to ensure that it's only counted once |
|
+ // against the batch's token limit. |
|
+ bool retained; |
|
+ // Map from token ID to the node representing that token appended to the |
|
+ // current sequence. For example, `root.children[3].children[4]` gives the |
|
+ // node for the token `4` in the sequence `3, 4`; its `seq` value indicates |
|
+ // a sequence in the KV cache that has `3, 4` as its prefix. |
|
+ std::map<int, kv_trie_node> children; |
|
+ |
|
+ kv_trie_node(int seq) : seq(seq), retained(false), children() {} |
|
+}; |
|
+ |
|
+kv_trie_node * kv_trie_find(kv_trie_node * node, int token_id); |
|
+kv_trie_node * kv_trie_insert(kv_trie_node * node, int token_id, int seq); |
|
+ |
|
+// Given a `node` representing some sequence, gets the `kv_trie_node` |
|
+// representing that sequence extended with `token_id`. Returns `nullptr` if |
|
+// the extended sequence is not in the trie, or if `node` itself is `nullptr`. |
|
+kv_trie_node * kv_trie_find(kv_trie_node * node, int token_id) { |
|
+ if (node == nullptr) { |
|
+ return nullptr; |
|
+ } |
|
+ auto iter = node->children.find(token_id); |
|
+ if (iter == node->children.end()) { |
|
+ return nullptr; |
|
+ } else { |
|
+ return &iter->second; |
|
+ } |
|
+} |
|
+ |
|
+// Given a `node` representing some sequence, insert a `kv_trie_node` |
|
+// representing that sequence extended with `token_id`, with `seq` as the |
|
+// sequence ID. If the extended sequence is already present in the trie, this |
|
+// does nothing. |
|
+kv_trie_node * kv_trie_insert(kv_trie_node * node, int token_id, int seq) { |
|
+ auto result = node->children.insert(std::make_pair(token_id, kv_trie_node(seq))); |
|
+ return &result.first->second; |
|
+} |
|
+ |
|
+int main(int argc, char ** argv) { |
|
+ gpt_params params; |
|
+ g_params = ¶ms; |
|
+ |
|
+ bool mf = false; |
|
+ char *farg; |
|
+ std::deque<char *> ff; |
|
+ int findex = -1; |
|
+ for (int i=0; i<argc; i++) { |
|
+ if (strcmp(argv[i], "-f") == 0) { |
|
+ if (++i >= argc) { |
|
+ exit(1337); |
|
+ break; |
|
+ } |
|
+ findex = i; |
|
+ farg = argv[i]; |
|
+ for (int j=0; argv[i][j] != '\0'; j++) { |
|
+ if (argv[i][j] == ',') { |
|
+ argv[i][j] = '\0'; |
|
+ mf = true; |
|
+ ff.push_back(&argv[i][j+1]); |
|
+ } |
|
+ } |
|
+ break; |
|
+ } |
|
+ } |
|
+ |
|
+ |
|
+ if (!gpt_params_parse(argc, argv, params)) { |
|
+ return 1; |
|
+ } |
|
+ llama_sampling_params & sparams = params.sparams; |
|
+ |
|
+ struct llama_sampling_context * ctx_sampling = llama_sampling_init(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_model *preloaded = NULL; |
|
+ llama_context * ctx; |
|
+ llama_context * ctx_guidance = NULL; |
|
+ g_model = &model; |
|
+ g_ctx = &ctx; |
|
+ |
|
+ ggml_context * eval_ctx = nullptr; |
|
+ struct eval_callback_state eval_state; |
|
+ |
|
+ // 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_with_cb_eval( |
|
+ params, |
|
+ eval_callback, |
|
+ (void *)&eval_state); |
|
+ /* |
|
+ 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_TEE("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); |
|
+ } |
|
+//STARTYO |
|
+ do { |
|
+ |
|
+ // print system information |
|
+ { |
|
+ LOG_TEE("\n"); |
|
+ LOG_TEE("%s\n", get_system_info(params).c_str()); |
|
+ } |
|
+ |
|
+ |
|
+ const bool add_bos = llama_should_add_bos_token(model); |
|
+ // LOG_TEE("add_bos: %d\n", add_bos); |
|
+ |
|
+ std::vector<llama_token> embd_inp; |
|
+ |
|
+ |
|
+ /// std::cerr << "tokenize the prompt" << params.prompt << "\n"; |
|
+ if (params.chatml) { |
|
+ params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>"; |
|
+ } |
|
+ // std::cerr << "tokenize bitch\n"; |
|
+ embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); |
|
+ // std::cerr << "yodl"; |
|
+ // LOG_TEE("prompt: \"%s\"\n", log_tostr(params.prompt)); |
|
+ // LOG_TEE("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; |
|
+ } |
|
+ */ |
|
+ |
|
+ |
|
+ // 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 (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 |
|
+ } |
|
+ |
|
+ 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"); |
|
+ |
|
+ bool is_antiprompt = false; |
|
+ bool input_echo = true; |
|
+ bool display = true; |
|
+ |
|
+ int n_past = 0; |
|
+ int n_remain = params.n_predict; |
|
+ unsigned n_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)); |
|
+ } |
|
+ |
|
+ |
|
+ // Tokenized prompt is in embd_inp |
|
+ |
|
+ |
|
+ // Record prompt boundaries |
|
+ const int PROMPT_DELIMITER_TOKEN = 13; |
|
+ |
|
+ // Index of each delimiter token in `embd_inp`. These mark the end of each |
|
+ // prompt. |
|
+ std::vector<size_t> delim_idxs; |
|
+ |
|
+ for (size_t i = 0; i < embd_inp.size(); ++i) { |
|
+ //std::cerr << "TOKEN " << embd_inp[i]; |
|
+ if (embd_inp[i] == PROMPT_DELIMITER_TOKEN) { |
|
+ delim_idxs.push_back(i); |
|
+ } |
|
+ } |
|
+ |
|
+ // If the last prompt is missing an ending delimiter, add it. |
|
+ if (embd_inp.size() > 0 && embd_inp.back() != PROMPT_DELIMITER_TOKEN) { |
|
+ delim_idxs.push_back(embd_inp.size()); |
|
+ embd_inp.push_back(PROMPT_DELIMITER_TOKEN); |
|
+ } |
|
+ |
|
+ size_t num_prompts = delim_idxs.size(); |
|
+ |
|
+LOG_TEE("NIGGER"); |
|
+ // Set up eval_state |
|
+ gguf_context * eval_gguf = gguf_init_empty(); |
|
+ { |
|
+ int n_embd = llama_n_embd(model); |
|
+ int n_layer = llama_n_layer(model); |
|
+ std::cerr << "build eval state: " << num_prompts << " prompts, " |
|
+ << n_embd << " embd, " << n_layer << " layers\n"; |
|
+ |
|
+ struct ggml_init_params params = {}; |
|
+ params.mem_size = ((size_t)n_embd * num_prompts * sizeof(float) + 1024) * n_layer; |
|
+ eval_ctx = ggml_init(params); |
|
+ |
|
+ for (int i = 0; i < n_layer; ++i) { |
|
+ ggml_tensor * t = ggml_new_tensor_2d(eval_ctx, GGML_TYPE_F32, n_embd, num_prompts); |
|
+ snprintf(t->name, sizeof(t->name), "l_out-%d", i); |
|
+ eval_state.tensors.push_back(t); |
|
+ gguf_add_tensor(eval_gguf, t); |
|
+ } |
|
+ eval_state.first_prompt_idx = -1; |
|
+ } |
|
+ |
|
+ |
|
+ |
|
+ // Max tokens to include in a single batch. |
|
+ int batch_max_tokens = llama_n_batch(ctx); |
|
+ unsigned batch_max_seq = llama_n_seq_max(ctx); |
|
+ |
|
+ std::cerr << "batch_max_tokens " << batch_max_tokens << "\n"; |
|
+ std::cerr << "batch_max_seq " << batch_max_seq << "\n"; |
|
+ |
|
+ struct llama_batch batch = llama_batch_init(batch_max_tokens, 0, batch_max_seq); |
|
+ |
|
+ size_t prompt_idx = 0; |
|
+ size_t batch_idx = 0; |
|
+ // For each (token_id, pos) pair, these maps record the first sequence to |
|
+ // contain that pair in the old and new states of the KV cache. |
|
+ std::unique_ptr<kv_trie_node> old_kv_trie(new kv_trie_node(-1)); |
|
+ std::unique_ptr<kv_trie_node> new_kv_trie(new kv_trie_node(-1)); |
|
+ auto last = ggml_time_ms(); |
|
+ while (prompt_idx < num_prompts) { |
|
+ std::cerr << "start batch at " << prompt_idx << "\n"; |
|
+ eval_state.first_prompt_idx = prompt_idx; |
|
+ eval_state.extract_tokens.clear(); |
|
+ // Reset `tokens_seen` to zero for all layers. |
|
+ eval_state.tokens_seen.clear(); |
|
+ eval_state.tokens_seen.resize(eval_state.tensors.size(), 0); |
|
+ |
|
+ old_kv_trie = std::move(new_kv_trie); |
|
+ new_kv_trie.reset(new kv_trie_node(-1)); |
|
+ |
|
+ // std::cerr << "WHERE IS OUR OOM? " << prompt_idx << "\n"; |
|
+ // Clear the token batch. |
|
+ batch.n_tokens = 0; |
|
+ size_t context_used = 0; |
|
+ |
|
+ llama_sampling_reset(ctx_sampling); |
|
+ |
|
+ // Add prompts to the batch until it's full. |
|
+ // We alternate between using sequence IDs [0, max/2) and [max/2, max). |
|
+ // This ensures that old sequences and new sequences never collide. |
|
+ unsigned first_seq = batch_idx % 2 == 0 ? 0 : batch_max_seq / 2; |
|
+ unsigned last_seq = first_seq + batch_max_seq / 2; |
|
+ unsigned cur_seq = first_seq; |
|
+ unsigned kv_cache_space = n_ctx; |
|
+ while (prompt_idx < num_prompts && cur_seq < last_seq) { |
|
+ // std::cerr << "WHERE IS OUR OOM now? " << prompt_idx << "\n"; |
|
+ |
|
+ size_t start = prompt_idx == 0 ? 0 : delim_idxs[prompt_idx - 1] + 1; |
|
+ size_t end = delim_idxs[prompt_idx]; |
|
+ GGML_ASSERT(end > start && "empty prompts are not allowed"); |
|
+ std::cerr << "CHECKING BOUNDS\nstart: " << start << "\n" << "end: " << end << "\n" << "kv_cache_space: " << kv_cache_space << "\n"; |
|
+ std::cerr << "adding " << start << " .. " << end |
|
+ << " (" << (end - start) << " tokens); " |
|
+ << context_used << " / " << batch_max_tokens << " context used\n"; |
|
+ |
|
+ if (end - start > kv_cache_space) { |
|
+ |
|
+ std::cerr << "biggernitch!\n"; |
|
+ // Not enough space remaining in the batch, if we hit the worst |
|
+ // case where the prompt consists entirely of new tokens. |
|
+ break; |
|
+ } |
|
+ |
|
+ // std::cerr << "gettheniggernodes!\n"; |
|
+ kv_trie_node * old_node = old_kv_trie.get(); |
|
+ kv_trie_node * new_node = new_kv_trie.get(); |
|
+ // std::cerr << "g0ttheniggernodes!\n"; |
|
+ |
|
+ for (size_t j = start; j < end; ++j) { |
|
+ int id = embd_inp[j]; |
|
+ int pos = j - start; |
|
+ |
|
+ // std::cerr << "lemmetrytofindthatnode!\n"; |
|
+ old_node = kv_trie_find(old_node, id); |
|
+ // Regardless of how we handle the token, it will ultimately be |
|
+ // present at position `j` in sequence `cur_seq`. |
|
+ // std::cerr << "lemmetrytoINSERTthatnode!\n"; |
|
+ new_node = kv_trie_insert(new_node, id, cur_seq); |
|
+ // std::cerr << "node business going on!\n"; |
|
+ if (old_node == nullptr) { |
|
+ // Add a new token. |
|
+ // std::cerr << "add that token to the batch y0!\n"; |
|
+ llama_batch_add(batch, id, pos, {(int)cur_seq}, false); |
|
+ --kv_cache_space; |
|
+ //const std::string token_str = llama_token_to_piece(ctx, id); |
|
+ //LOG_TEE("add '%s' (%d), %d to new sequence %d (fresh)\n", |
|
+ // token_str.c_str(), id, pos, cur_seq); |
|
+ } else { |
|
+ // std::cerr << "WE COPY SOME SHIT HERE.\n"; |
|
+ llama_kv_cache_seq_cp(ctx, old_node->seq, cur_seq, pos, pos + 1); |
|
+ //const std::string token_str = llama_token_to_piece(ctx, id); |
|
+ //LOG_TEE("add '%s' (%d), %d to new sequence %d, from old sequence %d\n", |
|
+ // token_str.c_str(), id, pos, cur_seq, old_seq); |
|
+ if (!old_node->retained) { |
|
+ // This token was previously going to be discarded. |
|
+ // Now that it's being kept, we have to count it |
|
+ // against `kv_cache_space`. |
|
+ old_node->retained = true; |
|
+ --kv_cache_space; |
|
+ //LOG_TEE("REUSED '%s' (%d), %d\n", token_str.c_str(), id, pos); |
|
+ } |
|
+ } |
|
+ } |
|
+ |
|
+ // std::cerr << "Push it back, Jamal.\n"; |
|
+ eval_state.extract_tokens.push_back(batch.n_tokens - 1); |
|
+ |
|
+ ++prompt_idx; |
|
+ ++cur_seq; |
|
+ } |
|
+ |
|
+ |
|
+ // std::cerr << "kv cache business going on.\n"; |
|
+ for (unsigned seq = 0; seq < batch_max_seq; ++seq) { |
|
+ if (seq < first_seq || seq >= last_seq) { |
|
+ llama_kv_cache_seq_rm(ctx, seq, -1, -1); |
|
+ } |
|
+ } |
|
+ // std::cerr << "kv cache business went ok.\n"; |
|
+ |
|
+ // Force defragmentation of the KV cache. `llama_decode` needs a |
|
+ // contiguous block of `batch.n_tokens` cache slots, which it won't be |
|
+ // able to find if the cache is too fragmented. Since we build batches |
|
+ // so as to maximize cache/context utilization, any fragmentation at |
|
+ // all will usually cause it to fail. |
|
+ // |
|
+ // FIXME: This sometimes doesn't fully defragment the cache, as shown |
|
+ // by `llama_kv_cache_view` debugging stats: if all free space was |
|
+ // contiguous, then `max_contiguous` should equal the number of free |
|
+ // cells (`n_cells - used_cells`), but often this is not the case. |
|
+ |
|
+ //std::cerr << "defrag kv cache\n"; |
|
+ llama_kv_cache_defrag(ctx); |
|
+ //std::cerr << "UPD0000T kv cache\n"; |
|
+ llama_kv_cache_update(ctx); |
|
+ //std::cerr << "defragGGGED kv cache\n"; |
|
+ |
|
+ for (int i = 0; i < 10; ++i) { |
|
+ // Debug prints to check cache usage and fragmentation: |
|
+ auto view = llama_kv_cache_view_init(ctx, 1); |
|
+ llama_kv_cache_view_update(ctx, &view); |
|
+ // std::cerr << "kv cache cells: " << view.n_cells << "\n"; |
|
+ // std::cerr << "kv cache tokens: " << view.token_count << "\n"; |
|
+ // std::cerr << "kv cache used: " << view.used_cells << "\n"; |
|
+ // std::cerr << "kv cache contiguous free space: " |
|
+ // << view.max_contiguous << " / " |
|
+ // << (view.n_cells - view.used_cells) << "\n"; |
|
+ if (view.max_contiguous < view.n_cells - view.used_cells) { |
|
+ //std::cerr << "defrag didn't work! trying again...\n"; |
|
+ llama_kv_cache_defrag(ctx); |
|
+ llama_kv_cache_update(ctx); |
|
+ } else { |
|
+ break; |
|
+ } |
|
+ } |
|
+ |
|
+ GGML_ASSERT(batch.n_tokens > 0); |
|
+ |
|
+ std::cerr << "batch " << eval_state.first_prompt_idx << ": " |
|
+ << (prompt_idx - eval_state.first_prompt_idx) << " prompts, " |
|
+ << batch.n_tokens << " new tokens, " |
|
+ << context_used << " total tokens\n"; |
|
+ |
|
+ |
|
+ // fprintf(stderr, "Let us OOM now.\n"); |
|
+ if (llama_decode(ctx, batch)) { |
|
+ LOG_TEE("%s : failed to eval\n", __func__); |
|
+ return 1; |
|
+ } |
|
+ // fprintf(stderr, "We OOMed not.\n"); |
|
+ |
|
+ auto now = ggml_time_ms(); |
|
+ auto timedelta = now - last; |
|
+ last = now; |
|
+ std::cerr << "time delta: " << timedelta << "ms\n"; |
|
+ // fprintf(stderr, "We OOMed still not.\n"); |
|
+ ++batch_idx; |
|
+ } |
|
+ |
|
+ |
|
+ //fprintf(stderr, "We DID NOT 00M.\n"); |
|
+ llama_print_timings(ctx); |
|
+ //write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); |
|
+ |
|
+ //if (ctx_guidance) { llama_free(ctx_guidance); } |
|
+ |
|
+ char *fname = farg, *fe = NULL; |
|
+ for (char *fn = fname; *fn != '\0'; fn++) { |
|
+ if (*fn == '/') |
|
+ fname = &fn[1]; |
|
+ if (*fn == '_') |
|
+ fe = fn; |
|
+ } |
|
+ if (fe > fname) { |
|
+ strcpy(fe, "_data.gguf"); |
|
+ } |
|
+ fprintf(stderr, "Save the file, nigger.\n"); |
|
+ fprintf(stderr, "Save the file to %s nigger.\n", fname); |
|
+ auto now = ggml_time_ms(); |
|
+ gguf_write_to_file(eval_gguf, fname, false); |
|
+ |
|
+ auto nowa = ggml_time_ms(); |
|
+ auto timedeltaa = nowa - now; |
|
+ fprintf(stderr, "Saving took: %dms.\n", timedeltaa); |
|
+ |
|
+ llama_free(ctx); |
|
+ ggml_free(eval_ctx); |
|
+ llama_sampling_free(ctx_sampling); |
|
+ gguf_free(eval_gguf); |
|
+ if (ff.size()) { |
|
+ farg = ff.front(); |
|
+ std::ifstream file(farg); |
|
+ if (!file) { |
|
+ fprintf(stderr, "error: failed to open file '%s'\n", farg); |
|
+ exit(1337); |
|
+ } |
|
+ |
|
+ // store the external file name in params |
|
+ params.prompt_file = farg; |
|
+ params.prompt.clear(); |
|
+ std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt)); |
|
+ if (!params.prompt.empty() && params.prompt.back() == '\n') { |
|
+ params.prompt.pop_back(); |
|
+ } |
|
+ |
|
+ ff.pop_front(); |
|
+ } else break; |
|
+ } while (true); |
|
+//ENDYO |
|
+ |
|
+ llama_free_model(model); |
|
+ |
|
+ llama_backend_free(); |
|
+ |
|
+#ifndef LOG_DISABLE_LOGS |
|
+ LOG_TEE("Log end\n"); |
|
+#endif // LOG_DISABLE_LOGS |
|
+ |
|
+ return 0; |
|
+} |
|
|
|
new file mode 100644 |
|
|
|
|
|
|
|
@@ -0,0 +1,956 @@ |
|
+#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 <map> |
|
+#include <memory> |
|
+#include <sstream> |
|
+#include <string> |
|
+#include <vector> |
|
+#include <deque> |
|
+ |
|
+#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; |
|
+} |
|
+ |
|
+#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); |
|
+} |
|
+ |
|
+static std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params_with_cb_eval( |
|
+ gpt_params & params, |
|
+ ggml_backend_sched_eval_callback cb_eval, |
|
+ void * cb_eval_user_data) { |
|
+ auto mparams = llama_model_params_from_gpt_params(params); |
|
+ |
|
+ llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams); |
|
+ if (model == NULL) { |
|
+ fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ |
|
+ auto cparams = llama_context_params_from_gpt_params(params); |
|
+ |
|
+ cparams.cb_eval = cb_eval; |
|
+ cparams.cb_eval_user_data = cb_eval_user_data; |
|
+ |
|
+ llama_context * lctx = llama_new_context_with_model(model, cparams); |
|
+ if (lctx == NULL) { |
|
+ fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ |
|
+ if (!params.control_vectors.empty()) { |
|
+ if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; |
|
+ if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); |
|
+ |
|
+ const auto cvec = llama_control_vector_load(params.control_vectors); |
|
+ if (cvec.n_embd == -1) { |
|
+ llama_free(lctx); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ |
|
+ int err = llama_control_vector_apply(lctx, |
|
+ cvec.data.data(), |
|
+ cvec.data.size(), |
|
+ cvec.n_embd, |
|
+ params.control_vector_layer_start, |
|
+ params.control_vector_layer_end); |
|
+ if (err) { |
|
+ llama_free(lctx); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ } |
|
+ |
|
+ for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { |
|
+ const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]); |
|
+ float lora_scale = std::get<1>(params.lora_adapter[i]); |
|
+ int err = llama_model_apply_lora_from_file(model, |
|
+ lora_adapter.c_str(), |
|
+ lora_scale, |
|
+ ((i > 0) || params.lora_base.empty()) |
|
+ ? NULL |
|
+ : params.lora_base.c_str(), |
|
+ params.n_threads); |
|
+ if (err != 0) { |
|
+ fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); |
|
+ llama_free(lctx); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ } |
|
+ |
|
+ if (params.ignore_eos) { |
|
+ params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; |
|
+ } |
|
+ |
|
+ { |
|
+ LOG("warming up the model with an empty run\n"); |
|
+ |
|
+ std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), }; |
|
+ llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); |
|
+ llama_kv_cache_clear(lctx); |
|
+ llama_synchronize(lctx); |
|
+ llama_reset_timings(lctx); |
|
+ } |
|
+ |
|
+ return std::make_tuple(model, lctx); |
|
+} |
|
+ |
|
+struct eval_callback_state { |
|
+ std::vector<ggml_tensor *> tensors; |
|
+ // For each hidden state tensor, how many tokens have we seen from the |
|
+ // current batch? When n_ubatch < n_batch, we don't see the hidden states |
|
+ // for all tokens at once, but only n_ubatch-sized chunks of tokens. This |
|
+ // keeps track of our progress. |
|
+ std::vector<int> tokens_seen; |
|
+ int first_prompt_idx; |
|
+ std::vector<int> extract_tokens; |
|
+}; |
|
+ |
|
+static bool eval_callback(struct ggml_tensor * t, bool ask, void * user_data) { |
|
+ struct eval_callback_state * eval_state = (eval_callback_state *)user_data; |
|
+ if (ask) { |
|
+ // Report whether we want to observe this tensor. |
|
+ if (strncmp(t->name, "l_out-", 6) == 0) { |
|
+ return true; |
|
+ } else { |
|
+ return false; |
|
+ } |
|
+ } else { |
|
+ // Actually observe the tensor data. |
|
+ |
|
+ if (eval_state->first_prompt_idx >= 0) { |
|
+ // Find the tensor collecting hidden states for the current layer. |
|
+ int layer_idx = -1; |
|
+ ggml_tensor * output_tensor = nullptr; |
|
+ for (size_t i = 0; i < eval_state->tensors.size(); ++i) { |
|
+ auto t2 = eval_state->tensors[i]; |
|
+ if (strcmp(t2->name, t->name) == 0) { |
|
+ output_tensor = t2; |
|
+ layer_idx = i; |
|
+ break; |
|
+ } |
|
+ } |
|
+ |
|
+ if (output_tensor != nullptr) { |
|
+ int ubatch_size = t->ne[1]; |
|
+ int ubatch_start = eval_state->tokens_seen[layer_idx]; |
|
+ int ubatch_end = ubatch_start + ubatch_size; |
|
+ eval_state->tokens_seen[layer_idx] += ubatch_size; |
|
+ |
|
+ int output_idx = eval_state->first_prompt_idx; |
|
+ for (int token_idx : eval_state->extract_tokens) { |
|
+ if (token_idx < ubatch_start || token_idx >= ubatch_end) { |
|
+ ++output_idx; |
|
+ continue; |
|
+ } |
|
+ int input_idx = token_idx - ubatch_start; |
|
+ |
|
+ // Copy the hidden states for the current token into the |
|
+ // output buffer. |
|
+ size_t input_offset = t->nb[1] * input_idx; |
|
+ size_t output_offset = output_tensor->nb[1] * output_idx; |
|
+ assert(t->nb[0] == output_tensor->nb[0]); |
|
+ assert(t->ne[0] == output_tensor->ne[0]); |
|
+ ggml_backend_tensor_get(t, |
|
+ (char *)output_tensor->data + output_offset, |
|
+ input_offset, |
|
+ t->nb[0] * t->ne[0]); |
|
+ //memcpy((char *)output_tensor->data + output_offset, |
|
+ // (char *)t->data + input_offset, |
|
+ // t->nb[0] * t->ne[0]); |
|
+ //std::cerr << "saved " << (t->nb[0] * t->ne[0]) << " bytes of tensor data " |
|
+ // << " for " << t->name << " in slot " << output_idx << "\n"; |
|
+ |
|
+ //float * buf = (float *)((char *)t->data + input_offset); |
|
+ //float * buf = (float *)((char *)output_tensor->data + output_offset); |
|
+ //std::cerr << "prompt " << output_idx |
|
+ // << " tensor contents for " << t->name << ": " |
|
+ // << buf[0] << ", " |
|
+ // << buf[1] << ", " |
|
+ // << buf[2] << " ... " |
|
+ // << buf[4093] << ", " |
|
+ // << buf[4094] << ", " |
|
+ // << buf[4095] << "\n"; |
|
+ |
|
+ ++output_idx; |
|
+ } |
|
+ } |
|
+ } |
|
+ |
|
+ // Continue running |
|
+ return true; |
|
+ } |
|
+} |
|
+ |
|
+struct kv_trie_node { |
|
+ int seq; |
|
+ // Flag to indicate whether the current token (that is, the last token of |
|
+ // the current sequence) has already been retained in the KV cache. When a |
|
+ // token from the old batch is reused for several different prompts in the |
|
+ // new batch, we use this flag to ensure that it's only counted once |
|
+ // against the batch's token limit. |
|
+ bool retained; |
|
+ // Map from token ID to the node representing that token appended to the |
|
+ // current sequence. For example, `root.children[3].children[4]` gives the |
|
+ // node for the token `4` in the sequence `3, 4`; its `seq` value indicates |
|
+ // a sequence in the KV cache that has `3, 4` as its prefix. |
|
+ std::map<int, kv_trie_node> children; |
|
+ |
|
+ kv_trie_node(int seq) : seq(seq), retained(false), children() {} |
|
+}; |
|
+ |
|
+kv_trie_node * kv_trie_find(kv_trie_node * node, int token_id); |
|
+kv_trie_node * kv_trie_insert(kv_trie_node * node, int token_id, int seq); |
|
+ |
|
+// Given a `node` representing some sequence, gets the `kv_trie_node` |
|
+// representing that sequence extended with `token_id`. Returns `nullptr` if |
|
+// the extended sequence is not in the trie, or if `node` itself is `nullptr`. |
|
+kv_trie_node * kv_trie_find(kv_trie_node * node, int token_id) { |
|
+ if (node == nullptr) { |
|
+ return nullptr; |
|
+ } |
|
+ auto iter = node->children.find(token_id); |
|
+ if (iter == node->children.end()) { |
|
+ return nullptr; |
|
+ } else { |
|
+ return &iter->second; |
|
+ } |
|
+} |
|
+ |
|
+// Given a `node` representing some sequence, insert a `kv_trie_node` |
|
+// representing that sequence extended with `token_id`, with `seq` as the |
|
+// sequence ID. If the extended sequence is already present in the trie, this |
|
+// does nothing. |
|
+kv_trie_node * kv_trie_insert(kv_trie_node * node, int token_id, int seq) { |
|
+ auto result = node->children.insert(std::make_pair(token_id, kv_trie_node(seq))); |
|
+ return &result.first->second; |
|
+} |
|
+ |
|
+int main(int argc, char ** argv) { |
|
+ gpt_params params; |
|
+ g_params = ¶ms; |
|
+ |
|
+ bool mf = false; |
|
+ char *farg; |
|
+ std::deque<char *> ff; |
|
+ int findex = -1; |
|
+ for (int i=0; i<argc; i++) { |
|
+ if (strcmp(argv[i], "-f") == 0) { |
|
+ if (++i >= argc) { |
|
+ exit(1337); |
|
+ break; |
|
+ } |
|
+ findex = i; |
|
+ farg = argv[i]; |
|
+ for (int j=0; argv[i][j] != '\0'; j++) { |
|
+ if (argv[i][j] == ',') { |
|
+ argv[i][j] = '\0'; |
|
+ mf = true; |
|
+ ff.push_back(&argv[i][j+1]); |
|
+ } |
|
+ } |
|
+ break; |
|
+ } |
|
+ } |
|
+ |
|
+ |
|
+ if (!gpt_params_parse(argc, argv, params)) { |
|
+ return 1; |
|
+ } |
|
+ llama_sampling_params & sparams = params.sparams; |
|
+ |
|
+ struct llama_sampling_context * ctx_sampling = llama_sampling_init(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_model *preloaded = NULL; |
|
+ llama_context * ctx; |
|
+ llama_context * ctx_guidance = NULL; |
|
+ g_model = &model; |
|
+ g_ctx = &ctx; |
|
+ |
|
+ ggml_context * eval_ctx = nullptr; |
|
+ struct eval_callback_state eval_state; |
|
+ |
|
+ // 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_with_cb_eval( |
|
+ params, |
|
+ eval_callback, |
|
+ (void *)&eval_state); |
|
+ /* |
|
+ 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); |
|
+ } |
|
+//STARTYO |
|
+ do { |
|
+ |
|
+ // print system information |
|
+ { |
|
+ LOG_TEE("\n"); |
|
+ LOG_TEE("%s\n", get_system_info(params).c_str()); |
|
+ } |
|
+ |
|
+ |
|
+ const bool add_bos = llama_should_add_bos_token(model); |
|
+ LOG_TEE("add_bos: %d\n", add_bos); |
|
+ |
|
+ std::vector<llama_token> embd_inp; |
|
+ |
|
+ |
|
+ std::cerr << "tokenize the prompt" << params.prompt << "\n"; |
|
+ if (params.chatml) { |
|
+ params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>"; |
|
+ } |
|
+ std::cerr << "tokenize bitch"; |
|
+ embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true); |
|
+ std::cerr << "yodl"; |
|
+ LOG_TEE("prompt: \"%s\"\n", log_tostr(params.prompt)); |
|
+ LOG_TEE("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; |
|
+ } |
|
+ */ |
|
+ |
|
+ |
|
+ // 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; |
|
+ |
|
+ int n_past = 0; |
|
+ int n_remain = params.n_predict; |
|
+ unsigned n_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)); |
|
+ } |
|
+ |
|
+ |
|
+ // Tokenized prompt is in embd_inp |
|
+ |
|
+ |
|
+ // Record prompt boundaries |
|
+ const int PROMPT_DELIMITER_TOKEN = 13; |
|
+ |
|
+ // Index of each delimiter token in `embd_inp`. These mark the end of each |
|
+ // prompt. |
|
+ std::vector<size_t> delim_idxs; |
|
+ |
|
+ for (size_t i = 0; i < embd_inp.size(); ++i) { |
|
+ if (embd_inp[i] == PROMPT_DELIMITER_TOKEN) { |
|
+ delim_idxs.push_back(i); |
|
+ } |
|
+ } |
|
+ |
|
+ // If the last prompt is missing an ending delimiter, add it. |
|
+ if (embd_inp.size() > 0 && embd_inp.back() != PROMPT_DELIMITER_TOKEN) { |
|
+ delim_idxs.push_back(embd_inp.size()); |
|
+ embd_inp.push_back(PROMPT_DELIMITER_TOKEN); |
|
+ } |
|
+ |
|
+ size_t num_prompts = delim_idxs.size(); |
|
+ |
|
+LOG_TEE("NIGGER"); |
|
+ // Set up eval_state |
|
+ gguf_context * eval_gguf = gguf_init_empty(); |
|
+ { |
|
+ int n_embd = llama_n_embd(model); |
|
+ int n_layer = llama_n_layer(model); |
|
+ std::cerr << "build eval state: " << num_prompts << " prompts, " |
|
+ << n_embd << " embd, " << n_layer << " layers\n"; |
|
+ |
|
+ struct ggml_init_params params = {}; |
|
+ params.mem_size = ((size_t)n_embd * num_prompts * sizeof(float) + 1024) * n_layer; |
|
+ eval_ctx = ggml_init(params); |
|
+ |
|
+ for (int i = 0; i < n_layer; ++i) { |
|
+ ggml_tensor * t = ggml_new_tensor_2d(eval_ctx, GGML_TYPE_F32, n_embd, num_prompts); |
|
+ snprintf(t->name, sizeof(t->name), "l_out-%d", i); |
|
+ eval_state.tensors.push_back(t); |
|
+ gguf_add_tensor(eval_gguf, t); |
|
+ } |
|
+ eval_state.first_prompt_idx = -1; |
|
+ } |
|
+ |
|
+ |
|
+ |
|
+ // Max tokens to include in a single batch. |
|
+ int batch_max_tokens = llama_n_batch(ctx); |
|
+ unsigned batch_max_seq = llama_n_seq_max(ctx); |
|
+ |
|
+ struct llama_batch batch = llama_batch_init(batch_max_tokens, 0, batch_max_seq); |
|
+ |
|
+ size_t prompt_idx = 0; |
|
+ size_t batch_idx = 0; |
|
+ // For each (token_id, pos) pair, these maps record the first sequence to |
|
+ // contain that pair in the old and new states of the KV cache. |
|
+ std::unique_ptr<kv_trie_node> old_kv_trie(new kv_trie_node(-1)); |
|
+ std::unique_ptr<kv_trie_node> new_kv_trie(new kv_trie_node(-1)); |
|
+ auto last = ggml_time_ms(); |
|
+ while (prompt_idx < num_prompts) { |
|
+ std::cerr << "start batch at " << prompt_idx << "\n"; |
|
+ eval_state.first_prompt_idx = prompt_idx; |
|
+ eval_state.extract_tokens.clear(); |
|
+ // Reset `tokens_seen` to zero for all layers. |
|
+ eval_state.tokens_seen.clear(); |
|
+ eval_state.tokens_seen.resize(eval_state.tensors.size(), 0); |
|
+ |
|
+ old_kv_trie = std::move(new_kv_trie); |
|
+ new_kv_trie.reset(new kv_trie_node(-1)); |
|
+ |
|
+ // std::cerr << "WHERE IS OUR OOM? " << prompt_idx << "\n"; |
|
+ // Clear the token batch. |
|
+ batch.n_tokens = 0; |
|
+ size_t context_used = 0; |
|
+ |
|
+ llama_sampling_reset(ctx_sampling); |
|
+ |
|
+ // Add prompts to the batch until it's full. |
|
+ // We alternate between using sequence IDs [0, max/2) and [max/2, max). |
|
+ // This ensures that old sequences and new sequences never collide. |
|
+ unsigned first_seq = batch_idx % 2 == 0 ? 0 : batch_max_seq / 2; |
|
+ unsigned last_seq = first_seq + batch_max_seq / 2; |
|
+ unsigned cur_seq = first_seq; |
|
+ unsigned kv_cache_space = n_ctx; |
|
+ while (prompt_idx < num_prompts && cur_seq < last_seq) { |
|
+ // std::cerr << "WHERE IS OUR OOM now? " << prompt_idx << "\n"; |
|
+ |
|
+ size_t start = prompt_idx == 0 ? 0 : delim_idxs[prompt_idx - 1] + 1; |
|
+ size_t end = delim_idxs[prompt_idx]; |
|
+ GGML_ASSERT(end > start && "empty prompts are not allowed"); |
|
+ |
|
+ //std::cerr << "adding " << start << " .. " << end |
|
+ // << " (" << (end - start) << " tokens); " |
|
+ // << context_used << " / " << batch_max_tokens << " context used\n"; |
|
+ |
|
+ if (end - start > kv_cache_space) { |
|
+ |
|
+ std::cerr << "biggernitch!\n"; |
|
+ // Not enough space remaining in the batch, if we hit the worst |
|
+ // case where the prompt consists entirely of new tokens. |
|
+ break; |
|
+ } |
|
+ |
|
+ // std::cerr << "gettheniggernodes!\n"; |
|
+ kv_trie_node * old_node = old_kv_trie.get(); |
|
+ kv_trie_node * new_node = new_kv_trie.get(); |
|
+ // std::cerr << "g0ttheniggernodes!\n"; |
|
+ |
|
+ for (size_t j = start; j < end; ++j) { |
|
+ int id = embd_inp[j]; |
|
+ int pos = j - start; |
|
+ |
|
+ // std::cerr << "lemmetrytofindthatnode!\n"; |
|
+ old_node = kv_trie_find(old_node, id); |
|
+ // Regardless of how we handle the token, it will ultimately be |
|
+ // present at position `j` in sequence `cur_seq`. |
|
+ // std::cerr << "lemmetrytoINSERTthatnode!\n"; |
|
+ new_node = kv_trie_insert(new_node, id, cur_seq); |
|
+ // std::cerr << "node business going on!\n"; |
|
+ if (old_node == nullptr) { |
|
+ // Add a new token. |
|
+ // std::cerr << "add that token to the batch y0!\n"; |
|
+ llama_batch_add(batch, id, pos, {(int)cur_seq}, false); |
|
+ --kv_cache_space; |
|
+ //const std::string token_str = llama_token_to_piece(ctx, id); |
|
+ //LOG_TEE("add '%s' (%d), %d to new sequence %d (fresh)\n", |
|
+ // token_str.c_str(), id, pos, cur_seq); |
|
+ } else { |
|
+ // std::cerr << "WE COPY SOME SHIT HERE.\n"; |
|
+ llama_kv_cache_seq_cp(ctx, old_node->seq, cur_seq, pos, pos + 1); |
|
+ //const std::string token_str = llama_token_to_piece(ctx, id); |
|
+ //LOG_TEE("add '%s' (%d), %d to new sequence %d, from old sequence %d\n", |
|
+ // token_str.c_str(), id, pos, cur_seq, old_seq); |
|
+ if (!old_node->retained) { |
|
+ // This token was previously going to be discarded. |
|
+ // Now that it's being kept, we have to count it |
|
+ // against `kv_cache_space`. |
|
+ old_node->retained = true; |
|
+ --kv_cache_space; |
|
+ //LOG_TEE("REUSED '%s' (%d), %d\n", token_str.c_str(), id, pos); |
|
+ } |
|
+ } |
|
+ } |
|
+ |
|
+ // std::cerr << "Push it back, Jamal.\n"; |
|
+ eval_state.extract_tokens.push_back(batch.n_tokens - 1); |
|
+ |
|
+ ++prompt_idx; |
|
+ ++cur_seq; |
|
+ } |
|
+ |
|
+ |
|
+ // std::cerr << "kv cache business going on.\n"; |
|
+ for (unsigned seq = 0; seq < batch_max_seq; ++seq) { |
|
+ if (seq < first_seq || seq >= last_seq) { |
|
+ llama_kv_cache_seq_rm(ctx, seq, -1, -1); |
|
+ } |
|
+ } |
|
+ // std::cerr << "kv cache business went ok.\n"; |
|
+ |
|
+ // Force defragmentation of the KV cache. `llama_decode` needs a |
|
+ // contiguous block of `batch.n_tokens` cache slots, which it won't be |
|
+ // able to find if the cache is too fragmented. Since we build batches |
|
+ // so as to maximize cache/context utilization, any fragmentation at |
|
+ // all will usually cause it to fail. |
|
+ // |
|
+ // FIXME: This sometimes doesn't fully defragment the cache, as shown |
|
+ // by `llama_kv_cache_view` debugging stats: if all free space was |
|
+ // contiguous, then `max_contiguous` should equal the number of free |
|
+ // cells (`n_cells - used_cells`), but often this is not the case. |
|
+ |
|
+ //std::cerr << "defrag kv cache\n"; |
|
+ llama_kv_cache_defrag(ctx); |
|
+ //std::cerr << "UPD0000T kv cache\n"; |
|
+ llama_kv_cache_update(ctx); |
|
+ //std::cerr << "defragGGGED kv cache\n"; |
|
+ |
|
+ for (int i = 0; i < 10; ++i) { |
|
+ // Debug prints to check cache usage and fragmentation: |
|
+ auto view = llama_kv_cache_view_init(ctx, 1); |
|
+ llama_kv_cache_view_update(ctx, &view); |
|
+ // std::cerr << "kv cache cells: " << view.n_cells << "\n"; |
|
+ // std::cerr << "kv cache tokens: " << view.token_count << "\n"; |
|
+ // std::cerr << "kv cache used: " << view.used_cells << "\n"; |
|
+ // std::cerr << "kv cache contiguous free space: " |
|
+ // << view.max_contiguous << " / " |
|
+ // << (view.n_cells - view.used_cells) << "\n"; |
|
+ if (view.max_contiguous < view.n_cells - view.used_cells) { |
|
+ //std::cerr << "defrag didn't work! trying again...\n"; |
|
+ llama_kv_cache_defrag(ctx); |
|
+ llama_kv_cache_update(ctx); |
|
+ } else { |
|
+ break; |
|
+ } |
|
+ } |
|
+ |
|
+ GGML_ASSERT(batch.n_tokens > 0); |
|
+ |
|
+ std::cerr << "batch " << eval_state.first_prompt_idx << ": " |
|
+ << (prompt_idx - eval_state.first_prompt_idx) << " prompts, " |
|
+ << batch.n_tokens << " new tokens, " |
|
+ << context_used << " total tokens\n"; |
|
+ |
|
+ |
|
+ // fprintf(stderr, "Let us OOM now.\n"); |
|
+ if (llama_decode(ctx, batch)) { |
|
+ LOG_TEE("%s : failed to eval\n", __func__); |
|
+ return 1; |
|
+ } |
|
+ // fprintf(stderr, "We OOMed not.\n"); |
|
+ |
|
+ auto now = ggml_time_ms(); |
|
+ auto timedelta = now - last; |
|
+ last = now; |
|
+ std::cerr << "time delta: " << timedelta << "ms\n"; |
|
+ // fprintf(stderr, "We OOMed still not.\n"); |
|
+ ++batch_idx; |
|
+ } |
|
+ |
|
+ |
|
+ //fprintf(stderr, "We DID NOT 00M.\n"); |
|
+ llama_print_timings(ctx); |
|
+ //write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); |
|
+ |
|
+ //if (ctx_guidance) { llama_free(ctx_guidance); } |
|
+ |
|
+ char *fname = farg, *fe = NULL; |
|
+ for (char *fn = fname; *fn != '\0'; fn++) { |
|
+ if (*fn == '/') |
|
+ fname = &fn[1]; |
|
+ if (*fn == '_') |
|
+ fe = fn; |
|
+ } |
|
+ if (fe > fname) { |
|
+ strcpy(fe, "_data.gguf"); |
|
+ } |
|
+ fprintf(stderr, "Save the file, nigger.\n"); |
|
+ fprintf(stderr, "Save the file to %s nigger.\n", fname); |
|
+ auto now = ggml_time_ms(); |
|
+ gguf_write_to_file(eval_gguf, fname, false); |
|
+ |
|
+ auto nowa = ggml_time_ms(); |
|
+ auto timedeltaa = nowa - now; |
|
+ fprintf(stderr, "Saving took: %dms.\n", timedeltaa); |
|
+ |
|
+ llama_free(ctx); |
|
+ ggml_free(eval_ctx); |
|
+ llama_sampling_free(ctx_sampling); |
|
+ if (ff.size()) { |
|
+ farg = ff.front(); |
|
+ std::ifstream file(farg); |
|
+ if (!file) { |
|
+ fprintf(stderr, "error: failed to open file '%s'\n", farg); |
|
+ exit(1337); |
|
+ } |
|
+ |
|
+ // store the external file name in params |
|
+ params.prompt_file = farg; |
|
+ params.prompt.clear(); |
|
+ std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt)); |
|
+ if (!params.prompt.empty() && params.prompt.back() == '\n') { |
|
+ params.prompt.pop_back(); |
|
+ } |
|
+ |
|
+ ff.pop_front(); |
|
+ } else break; |
|
+ } while (true); |
|
+//ENDYO |
|
+ |
|
+ llama_free_model(model); |
|
+ |
|
+ llama_backend_free(); |
|
+ |
|
+#ifndef LOG_DISABLE_LOGS |
|
+ LOG_TEE("Log end\n"); |
|
+#endif // LOG_DISABLE_LOGS |
|
+ |
|
+ return 0; |
|
+} |
|
|
|
new file mode 100644 |
|
|
|
|
|
|
|
@@ -0,0 +1,915 @@ |
|
+ |
|
+#include "common.h" |
|
+ |
|
+#include "console.h" |
|
+#include "llama.h" |
|
+#include <deque> |
|
+#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; |
|
+} |
|
+ |
|
+#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); |
|
+} |
|
+ |
|
+static std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params_with_cb_eval( |
|
+ gpt_params & params, |
|
+ ggml_backend_sched_eval_callback cb_eval, |
|
+ void * cb_eval_user_data, |
|
+ struct llama_model *preloaded = NULL) { |
|
+ auto mparams = llama_model_params_from_gpt_params(params); |
|
+ |
|
+ llama_model * model = preloaded ? preloaded : llama_load_model_from_file(params.model.c_str(), mparams); |
|
+ if (model == NULL) { |
|
+ fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ |
|
+ auto cparams = llama_context_params_from_gpt_params(params); |
|
+ |
|
+ cparams.cb_eval = cb_eval; |
|
+ cparams.cb_eval_user_data = cb_eval_user_data; |
|
+ |
|
+ llama_context * lctx = llama_new_context_with_model(model, cparams); |
|
+ if (lctx == NULL) { |
|
+ fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ |
|
+ if (!params.control_vectors.empty()) { |
|
+ if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; |
|
+ if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); |
|
+ |
|
+ const auto cvec = llama_control_vector_load(params.control_vectors); |
|
+ if (cvec.n_embd == -1) { |
|
+ llama_free(lctx); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ |
|
+ int err = llama_control_vector_apply(lctx, |
|
+ cvec.data.data(), |
|
+ cvec.data.size(), |
|
+ cvec.n_embd, |
|
+ params.control_vector_layer_start, |
|
+ params.control_vector_layer_end); |
|
+ if (err) { |
|
+ llama_free(lctx); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ } |
|
+ |
|
+ for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { |
|
+ const std::string& lora_adapter = std::get<0>(params.lora_adapter[i]); |
|
+ float lora_scale = std::get<1>(params.lora_adapter[i]); |
|
+ int err = llama_model_apply_lora_from_file(model, |
|
+ lora_adapter.c_str(), |
|
+ lora_scale, |
|
+ ((i > 0) || params.lora_base.empty()) |
|
+ ? NULL |
|
+ : params.lora_base.c_str(), |
|
+ params.n_threads); |
|
+ if (err != 0) { |
|
+ fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); |
|
+ llama_free(lctx); |
|
+ llama_free_model(model); |
|
+ return std::make_tuple(nullptr, nullptr); |
|
+ } |
|
+ } |
|
+ |
|
+ if (params.ignore_eos) { |
|
+ params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; |
|
+ } |
|
+ |
|
+ { |
|
+ LOG("warming up the model with an empty run\n"); |
|
+ |
|
+ std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), }; |
|
+ llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); |
|
+ llama_kv_cache_clear(lctx); |
|
+ llama_synchronize(lctx); |
|
+ llama_reset_timings(lctx); |
|
+ } |
|
+ |
|
+ return std::make_tuple(model, lctx); |
|
+} |
|
+ |
|
+struct eval_callback_state { |
|
+ std::vector<ggml_tensor *> tensors; |
|
+ // For each hidden state tensor, how many tokens have we seen from the |
|
+ // current batch? When n_ubatch < n_batch, we don't see the hidden states |
|
+ // for all tokens at once, but only n_ubatch-sized chunks of tokens. This |
|
+ // keeps track of our progress. |
|
+ std::vector<int> tokens_seen; |
|
+ int first_prompt_idx; |
|
+ std::vector<int> extract_tokens; |
|
+}; |
|
+ |
|
+static bool eval_callback(struct ggml_tensor * t, bool ask, void * user_data) { |
|
+ struct eval_callback_state * eval_state = (eval_callback_state *)user_data; |
|
+ if (ask) { |
|
+ // Report whether we want to observe this tensor. |
|
+ if (strncmp(t->name, "l_out-", 6) == 0) { |
|
+ return true; |
|
+ } else { |
|
+ return false; |
|
+ } |
|
+ } else { |
|
+ // Actually observe the tensor data. |
|
+ |
|
+ if (eval_state->first_prompt_idx >= 0) { |
|
+ // Find the tensor collecting hidden states for the current layer. |
|
+ int layer_idx = -1; |
|
+ ggml_tensor * output_tensor = nullptr; |
|
+ for (size_t i = 0; i < eval_state->tensors.size(); ++i) { |
|
+ auto t2 = eval_state->tensors[i]; |
|
+ if (strcmp(t2->name, t->name) == 0) { |
|
+ output_tensor = t2; |
|
+ layer_idx = i; |
|
+ break; |
|
+ } |
|
+ } |
|
+ |
|
+ if (output_tensor != nullptr) { |
|
+ int ubatch_size = t->ne[1]; |
|
+ int ubatch_start = eval_state->tokens_seen[layer_idx]; |
|
+ int ubatch_end = ubatch_start + ubatch_size; |
|
+ eval_state->tokens_seen[layer_idx] += ubatch_size; |
|
+ |
|
+ int output_idx = eval_state->first_prompt_idx; |
|
+ for (int token_idx : eval_state->extract_tokens) { |
|
+ if (token_idx < ubatch_start || token_idx >= ubatch_end) { |
|
+ continue; |
|
+ } |
|
+ int input_idx = token_idx - ubatch_start; |
|
+ |
|
+ // Copy the hidden states for the current token into the |
|
+ // output buffer. |
|
+ size_t input_offset = t->nb[1] * input_idx; |
|
+ size_t output_offset = output_tensor->nb[1] * output_idx; |
|
+ assert(t->nb[0] == output_tensor->nb[0]); |
|
+ assert(t->ne[0] == output_tensor->ne[0]); |
|
+ ggml_backend_tensor_get(t, |
|
+ (char *)output_tensor->data + output_offset, |
|
+ input_offset, |
|
+ t->nb[0] * t->ne[0]); |
|
+ //memcpy((char *)output_tensor->data + output_offset, |
|
+ // (char *)t->data + input_offset, |
|
+ // t->nb[0] * t->ne[0]); |
|
+ //std::cerr << "saved " << (t->nb[0] * t->ne[0]) << " bytes of tensor data " |
|
+ // << " for " << t->name << " in slot " << output_idx << "\n"; |
|
+ |
|
+ //float * buf = (float *)((char *)t->data + input_offset); |
|
+ //float * buf = (float *)((char *)output_tensor->data + output_offset); |
|
+ //std::cerr << "prompt " << output_idx |
|
+ // << " tensor contents for " << t->name << ": " |
|
+ // << buf[0] << ", " |
|
+ // << buf[1] << ", " |
|
+ // << buf[2] << " ... " |
|
+ // << buf[4093] << ", " |
|
+ // << buf[4094] << ", " |
|
+ // << buf[4095] << "\n"; |
|
+ |
|
+ ++output_idx; |
|
+ } |
|
+ } |
|
+ } |
|
+ |
|
+ // Continue running |
|
+ return true; |
|
+ } |
|
+} |
|
+ |
|
+int main(int argc, char ** argv) { |
|
+ gpt_params params; |
|
+ g_params = ¶ms; |
|
+ |
|
+ bool mf = false; |
|
+ char *farg; |
|
+ std::deque<char *> ff; |
|
+ int findex = -1; |
|
+ for (int i=0; i<argc; i++) { |
|
+ if (strcmp(argv[i], "-f") == 0) { |
|
+ if (++i >= argc) { |
|
+ exit(1337); |
|
+ break; |
|
+ } |
|
+ findex = i; |
|
+ farg = argv[i]; |
|
+ for (int j=0; argv[i][j] != '\0'; j++) { |
|
+ if (argv[i][j] == ',') { |
|
+ argv[i][j] = '\0'; |
|
+ mf = true; |
|
+ ff.push_back(&argv[i][j+1]); |
|
+ } |
|
+ } |
|
+ break; |
|
+ } |
|
+ } |
|
+ if (!gpt_params_parse(argc, argv, params)) { |
|
+ printf("Fuck you"); |
|
+ 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_model *preloaded = NULL; |
|
+ 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__); |
|
+//STARTYO |
|
+ do |
|
+ { |
|
+ ggml_context * eval_ctx = nullptr; |
|
+ struct eval_callback_state eval_state; |
|
+ |
|
+ std::tie(model, ctx) = llama_init_from_gpt_params_with_cb_eval( |
|
+ params, |
|
+ eval_callback, |
|
+ (void *)&eval_state, |
|
+ preloaded); |
|
+ preloaded = model; |
|
+ /* |
|
+ 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()); |
|
+ } |
|
+ |
|
+ 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 (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; |
|
+ unsigned 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); |
|
+ |
|
+ |
|
+ |
|
+ // Tokenized prompt is in embd_inp |
|
+ |
|
+ |
|
+ printf("Start training from prompts %s ...\n", farg); |
|
+ // Record prompt boundaries |
|
+ const int PROMPT_DELIMITER_TOKEN = 13; |
|
+ |
|
+ // Index of each delimiter token in `embd_inp`. These mark the end of each |
|
+ // prompt. |
|
+ std::vector<size_t> delim_idxs; |
|
+ |
|
+ for (size_t i = 0; i < embd_inp.size(); ++i) { |
|
+ if (embd_inp[i] == PROMPT_DELIMITER_TOKEN) { |
|
+ delim_idxs.push_back(i); |
|
+ } |
|
+ } |
|
+ |
|
+ // If the last prompt is missing an ending delimiter, add it. |
|
+ if (embd_inp.size() > 0 && embd_inp.back() != PROMPT_DELIMITER_TOKEN) { |
|
+ delim_idxs.push_back(embd_inp.size()); |
|
+ embd_inp.push_back(PROMPT_DELIMITER_TOKEN); |
|
+ } |
|
+ |
|
+ size_t num_prompts = delim_idxs.size(); |
|
+ |
|
+ |
|
+ // Set up eval_state |
|
+ gguf_context * eval_gguf = gguf_init_empty(); |
|
+ { |
|
+ int n_embd = llama_n_embd(model); |
|
+ int n_layer = llama_n_layer(model); |
|
+ std::cerr << "build eval state: " << num_prompts << " prompts, " |
|
+ << n_embd << " embd, " << n_layer << " layers\n"; |
|
+ |
|
+ struct ggml_init_params params = {}; |
|
+ params.mem_size = ((size_t)n_embd * num_prompts * sizeof(float) + 1024) * n_layer; |
|
+ eval_ctx = ggml_init(params); |
|
+ |
|
+ for (int i = 0; i < n_layer; ++i) { |
|
+ ggml_tensor * t = ggml_new_tensor_2d(eval_ctx, GGML_TYPE_F32, n_embd, num_prompts); |
|
+ snprintf(t->name, sizeof(t->name), "l_out-%d", i); |
|
+ eval_state.tensors.push_back(t); |
|
+ gguf_add_tensor(eval_gguf, t); |
|
+ } |
|
+ eval_state.first_prompt_idx = -1; |
|
+ } |
|
+ |
|
+ |
|
+ // Max tokens to include in a single batch. |
|
+ int batch_max_tokens = llama_n_batch(ctx); |
|
+ unsigned batch_max_seq = llama_n_seq_max(ctx); |
|
+ |
|
+ struct llama_batch batch = llama_batch_init(batch_max_tokens, 0, batch_max_seq); |
|
+ |
|
+ size_t prompt_idx = 0; |
|
+ std::vector<size_t> prev_prompt_start_for_sequence; |
|
+ auto last = ggml_time_ms(); |
|
+ while (prompt_idx < num_prompts) { |
|
+ //std::cerr << "start batch at " << prompt_idx << "\n"; |
|
+ eval_state.first_prompt_idx = prompt_idx; |
|
+ eval_state.extract_tokens.clear(); |
|
+ // Reset `tokens_seen` to zero for all layers. |
|
+ eval_state.tokens_seen.clear(); |
|
+ eval_state.tokens_seen.resize(eval_state.tensors.size(), 0); |
|
+ |
|
+ // Clear the token batch. |
|
+ batch.n_tokens = 0; |
|
+ size_t context_used = 0; |
|
+ |
|
+ llama_sampling_reset(ctx_sampling); |
|
+ |
|
+ // Add prompts to the batch until it's full. |
|
+ unsigned next_seq = 0; |
|
+ while (prompt_idx < num_prompts && next_seq < batch_max_seq) { |
|
+ size_t start = prompt_idx == 0 ? 0 : delim_idxs[prompt_idx - 1] + 1; |
|
+ size_t end = delim_idxs[prompt_idx]; |
|
+ GGML_ASSERT(end > start && "empty prompts are not allowed"); |
|
+ |
|
+ // Number of initial tokens in common between this prompt and the |
|
+ // previous prompt to use this sequence ID. |
|
+ size_t common = 0; |
|
+ |
|
+ if (next_seq < prev_prompt_start_for_sequence.size()) { |
|
+ size_t prev_start = prev_prompt_start_for_sequence[next_seq]; |
|
+ GGML_ASSERT(prev_start <= start); |
|
+ while (start + common < embd_inp.size() |
|
+ && embd_inp[prev_start + common] != PROMPT_DELIMITER_TOKEN |
|
+ && embd_inp[start + common] == embd_inp[prev_start + common] |
|
+ ) { |
|
+ ++common; |
|
+ } |
|
+ } |
|
+ // If the current prompt is a prefix of the previous one, then it's |
|
+ // possible that all tokens are marked as common. Ensure that the |
|
+ // last token of the current prompt is never marked as common so |
|
+ // that we get its hidden states. |
|
+ if (common >= end - start) { |
|
+ common = end - start - 1; |
|
+ } |
|
+ GGML_ASSERT(start + common < end); |
|
+ |
|
+ //std::cerr << "adding " << start << " .. " << end |
|
+ // << " (" << (end - start) << " tokens); " |
|
+ // << context_used << " / " << batch_max_tokens << " context used\n"; |
|
+ |
|
+ // FIXME: We artificially reduce the batch size limit here to |
|
+ // account for `llama_kv_cache_defrag` not fully defragmenting the |
|
+ // cache. See the comment below. |
|
+ if (end - start > (size_t)(batch_max_tokens * 8 / 10 - context_used)) { |
|
+ // Not enough space for this prompt in the batch. |
|
+ std::cerr << "check space for prompt: [" << end << "-" << start << "=" << end - start << "] | " << context_used << "\n"; |
|
+ GGML_ASSERT(end - start <= (size_t)context_used); |
|
+ break; |
|
+ } |
|
+ |
|
+ // Clear the KV cache for this sequence, except for the common |
|
+ // prefix. |
|
+ llama_kv_cache_seq_rm(ctx, next_seq, common, -1); |
|
+ |
|
+ //std::cerr << "still cooking\n"; |
|
+ for (size_t j = start + common; j < end; ++j) { |
|
+ int id = embd_inp[j]; |
|
+ |
|
+ // 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, id, false); |
|
+ |
|
+ // Add the token to the current batch. Its position within the |
|
+ // context is relative to the start of the current prompt. |
|
+ llama_batch_add(batch, id, j - start, {(int)next_seq}, false); |
|
+ |
|
+ //const std::string token_str = llama_token_to_piece(ctx, id); |
|
+ //std::cerr << "pos " << (j - start) << ": token " |
|
+ // << id << " \"" << token_str << "\"\n"; |
|
+ } |
|
+ |
|
+ eval_state.extract_tokens.push_back(batch.n_tokens - 1); |
|
+ if (next_seq >= prev_prompt_start_for_sequence.size()) { |
|
+ GGML_ASSERT(next_seq == prev_prompt_start_for_sequence.size()); |
|
+ prev_prompt_start_for_sequence.push_back(start); |
|
+ } else { |
|
+ prev_prompt_start_for_sequence[next_seq] = start; |
|
+ } |
|
+ |
|
+ ++prompt_idx; |
|
+ ++next_seq; |
|
+ context_used += end - start; |
|
+ } |
|
+ |
|
+ while (prev_prompt_start_for_sequence.size() > next_seq) { |
|
+ llama_kv_cache_seq_rm(ctx, prev_prompt_start_for_sequence.size() - 1, -1, -1); |
|
+ prev_prompt_start_for_sequence.pop_back(); |
|
+ } |
|
+ |
|
+ // Force defragmentation of the KV cache. `llama_decode` needs a |
|
+ // contiguous block of `batch.n_tokens` cache slots, which it won't be |
|
+ // able to find if the cache is too fragmented. Since we build batches |
|
+ // so as to maximize cache/context utilization, any fragmentation at |
|
+ // all will usually cause it to fail. |
|
+ // |
|
+ // FIXME: This sometimes doesn't fully defragment the cache, as shown |
|
+ // by `llama_kv_cache_view` debugging stats: if all free space was |
|
+ // contiguous, then `max_contiguous` should equal the number of free |
|
+ // cells (`n_cells - used_cells`), but often this is not the case. |
|
+ std::cerr << "defrag boi\n"; |
|
+ llama_kv_cache_defrag(ctx); |
|
+ llama_kv_cache_update(ctx); |
|
+ |
|
+ /* |
|
+ // Debug prints to check cache usage and fragmentation: |
|
+ auto view = llama_kv_cache_view_init(ctx, 1); |
|
+ llama_kv_cache_view_update(ctx, &view); |
|
+ //std::cerr << "kv cache cells: " << view.n_cells << "\n"; |
|
+ //std::cerr << "kv cache tokens: " << view.token_count << "\n"; |
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+ //std::cerr << "kv cache used: " << view.used_cells << "\n"; |
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+ std::cerr << "kv cache max_contiguous: " << view.max_contiguous << "\n"; |
|
+ std::cerr << "kv cache free cells: " << (view.n_cells - view.used_cells) << "\n"; |
|
+ */ |
|
+ |
|
+ //GGML_ASSERT(batch.n_tokens > 0); |
|
+ |
|
+ |
|
+ std::cerr << "batch " << eval_state.first_prompt_idx << ": " |
|
+ << (prompt_idx - eval_state.first_prompt_idx) << " prompts, " |
|
+ << batch.n_tokens << " new tokens, " |
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+ << context_used << " total tokens\n"; |
|
+ |
|
+ //std::cerr << "prompt " << eval_state.prompt_idx << ": " << batch.n_tokens << " tokens\n"; |
|
+ |
|
+ //batch.logits[batch.n_tokens - 1] = true; |
|
+ |
|
+ |
|
+ if (llama_decode(ctx, batch)) { |
|
+ LOG_TEE("%s : failed to eval\n", __func__); |
|
+ return 1; |
|
+ } |
|
+ |
|
+ auto now = ggml_time_ms(); |
|
+ auto timedelta = now - last; |
|
+ last = now; |
|
+ std::cerr << "time delta: " << timedelta << "ms\n"; |
|
+ |
|
+ //const llama_token id = llama_sampling_sample(ctx_sampling, ctx, nullptr, batch.n_tokens - 1); |
|
+ //const std::string token_str = llama_token_to_piece(ctx, id); |
|
+ //LOG_TEE("sample token %d: \"%s\"\n", id, token_str.c_str()); |
|
+ } |
|
+ |
|
+ char *fname = farg, *fe = NULL; |
|
+ for (char *fn = fname; *fn != '\0'; fn++) { |
|
+ if (*fn == '/') |
|
+ fname = &fn[1]; |
|
+ if (*fn == '_') |
|
+ fe = fn; |
|
+ } |
|
+ if (fe > fname) { |
|
+ strcpy(fe, "_data.gguf"); |
|
+ } |
|
+ |
|
+ gguf_write_to_file(eval_gguf, fname, false); |
|
+ |
|
+ 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); |
|
+ ggml_free(eval_ctx); |
|
+ llama_sampling_free(ctx_sampling); |
|
+ if (ff.size()) { |
|
+ farg = ff.front(); |
|
+ std::ifstream file(farg); |
|
+ if (!file) { |
|
+ fprintf(stderr, "error: failed to open file '%s'\n", farg); |
|
+ exit(1337); |
|
+ } |
|
+ // store the external file name in params |
|
+ params.prompt_file = farg; |
|
+ params.prompt.clear(); |
|
+ std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt)); |
|
+ if (!params.prompt.empty() && params.prompt.back() == '\n') { |
|
+ params.prompt.pop_back(); |
|
+ } |
|
+ ff.pop_front(); |
|
+ } else break; |
|
+ } while (true); |
|
+//ENDYO |
|
+ llama_free_model(model); |
|
+ |
|
+ llama_backend_free(); |
|
+#ifndef LOG_DISABLE_LOGS |
|
+ LOG_TEE("Log end\n"); |
|
+#endif // LOG_DISABLE_LOGS |
|
+ |
|
+ return 0; |
|
+} |
|
|
|
|
|
|
|
|
|
@@ -1,7 +1,8 @@ |
|
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) |
|
|
|
set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in") |
|
-set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp") |
|
+set(TEMP_FILE "${OUTPUT_DIR}/common/build-info.cpp.in") |
|
+set(OUTPUT_FILE "${OUTPUT_DIR}/common/build-info.cpp") |
|
|
|
# Only write the build info if it changed |
|
if(EXISTS ${OUTPUT_FILE}) |
|
@@ -17,8 +18,12 @@ if(EXISTS ${OUTPUT_FILE}) |
|
NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR |
|
NOT OLD_TARGET STREQUAL BUILD_TARGET |
|
) |
|
- configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE}) |
|
+ message(STATUS ${TEMPLATE_FILE} ${TEMP_FILE} ${OUTPUT_FILE}) |
|
+ configure_file(${TEMPLATE_FILE} ${TEMP_FILE} COPYONLY) |
|
+ configure_file(${TEMP_FILE} ${OUTPUT_FILE}) |
|
endif() |
|
else() |
|
- configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE}) |
|
+ message(STATUS ${TEMPLATE_FILE} ${TEMP_FILE} ${OUTPUT_FILE}) |
|
+ configure_file(${TEMPLATE_FILE} ${TEMP_FILE} COPYONLY) |
|
+ configure_file(${TEMP_FILE} ${OUTPUT_FILE}) |
|
endif() |
|
|
