still refactoring, but need a checkpoint to prepare build for 1.0.7

Makefile

@@ -211,7 +211,7 @@ endif
 
 BLAS_BUILD = 
 ifeq ($(OS),Windows_NT)
-	BLAS_BUILD = $(CXX) $(CXXFLAGS) ggml_blas.o common.o extra.o expose.o llama_adapter.o libopenblas.lib -shared -o llamacpp_blas.dll $(LDFLAGS)
+	BLAS_BUILD = $(CXX) $(CXXFLAGS) ggml_blas.o common.o extra.o expose.o model_adapter.o libopenblas.lib -shared -o llamacpp_blas.dll $(LDFLAGS)
 else
 	BLAS_BUILD = @echo 'Your OS is $(OS) and does not appear to be Windows. If you want to use openblas, please link it manually with LLAMA_OPENBLAS=1'
 endif
@@ -258,8 +258,8 @@ extra.o: extra.cpp extra.h
 expose.o: expose.cpp expose.h
 	$(CXX) $(CXXFLAGS) -c expose.cpp -o expose.o
 
-llama_adapter.o: llama_adapter.cpp llama_adapter.h
-	$(CXX) $(CXXFLAGS) -c llama_adapter.cpp -o llama_adapter.o
+model_adapter.o: 
+	$(CXX) $(CXXFLAGS) -c llama_adapter.cpp -o model_adapter.o
 
 clean:
 	rm -vf *.o main quantize perplexity embedding main.exe quantize.exe llamacpp.dll llamacpp_blas.dll gpt2.exe gptj.exe
@@ -274,10 +274,10 @@ gptj: ggml_old_v1.o
 	$(CXX) $(CXXFLAGS) otherarch/gptj_old.cpp otherarch/utils.cpp ggml_old_v1.o -o gptj $(LDFLAGS)
 
 
-llamalib: ggml.o common.o extra.o expose.o llama_adapter.o
-	$(CXX) $(CXXFLAGS) ggml.o common.o extra.o expose.o llama_adapter.o -shared -o llamacpp.dll $(LDFLAGS)
+llamalib: ggml.o common.o extra.o expose.o model_adapter.o
+	$(CXX) $(CXXFLAGS) ggml.o common.o extra.o expose.o model_adapter.o -shared -o llamacpp.dll $(LDFLAGS)
 
-llamalib_blas: ggml_blas.o common.o extra.o expose.o llama_adapter.o
+llamalib_blas: ggml_blas.o common.o extra.o expose.o model_adapter.o
 	$(BLAS_BUILD)
 	
 quantize: examples/quantize/quantize.cpp ggml.o llama.o

README.md

@@ -2,7 +2,7 @@
 
 A self contained distributable from Concedo that exposes llama.cpp function bindings, allowing it to be used via a simulated Kobold API endpoint. 
 
-What does it mean? You get llama.cpp with a fancy UI, persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer. In a tiny package under 1 MB in size, excluding model weights.
+What does it mean? You get llama.cpp with a fancy UI, persistent stories, editing tools, save formats, memory, world info, author's note, characters, scenarios and everything Kobold and Kobold Lite have to offer. In a tiny package around 10 MB in size, excluding model weights.
 
 ![Preview](preview.png)
 

expose.cpp

@@ -7,7 +7,7 @@
 //No dynamic memory allocation! Setup structs with FIXED (known) shapes and sizes for ALL output fields
 //Python will ALWAYS provide the memory, we just write to it.
 
-#include "llama_adapter.h"
+#include "model_adapter.h"
 #include "expose.h"
 #include "extra.h"
 

extra.cpp

@@ -18,6 +18,35 @@
  #include <alloca.h>
  #endif
 
+static clock_t bench_timer = 0;
+
+void timer_start()
+{
+    bench_timer = clock();
+}
+double timer_check()
+{
+    double ticks = clock() - bench_timer;
+    double time_taken = ((double)ticks) / CLOCKS_PER_SEC;
+    return time_taken;
+}
+
+void print_tok_vec(std::vector<int> &embd)
+{
+    std::cout << "[";
+    bool first = true;
+    for (auto i : embd)
+    {
+        if (!first)
+        {
+            std::cout << ',';
+        }
+        first = false;
+        std::cout << i;
+    }
+    std::cout << "]";
+}
+
 //return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt) 
  FileFormat check_file_format(const std::string & fname)
  {

extra.h

@@ -24,6 +24,10 @@ enum FileFormat
     GGJT=3    
 };
 
+void print_tok_vec(std::vector<int> &embd);
+void timer_start();
+double timer_check();
+
 FileFormat check_file_format(const std::string & fname);
 
 std::vector<llama_token> legacy_llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos);

gptj_adapter.cpp

@@ -0,0 +1,403 @@
+//This is Concedo's shitty adapter for adding python bindings for llama
+
+//Considerations:
+//Don't want to use pybind11 due to dependencies on MSVCC
+//ZERO or MINIMAL changes as possible to main.cpp - do not move their function declarations here!
+//Leave main.cpp UNTOUCHED, We want to be able to update the repo and pull any changes automatically.
+//No dynamic memory allocation! Setup structs with FIXED (known) shapes and sizes for ALL output fields
+//Python will ALWAYS provide the memory, we just write to it.
+
+#include <time.h>
+#include "model_adapter.h"
+#include "otherarch/otherarch.h"
+
+//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
+static FileFormat file_format = FileFormat::FAIL;
+static llama_context_params ctx_params;
+static gpt_params params;
+static int n_past = 0;
+static int n_threads = 4;
+static int n_batch = 8;
+static std::string model;
+static llama_context *ctx;
+static std::vector<llama_token> last_n_tokens;
+static std::vector<llama_token> current_context_tokens;
+
+void nnn()
+{
+    ggml_time_init();
+    const int64_t t_main_start_us = ggml_time_us();
+
+    gpt_params params;
+    params.model = "models/gpt-j-6B/ggml-model.bin";
+
+    if (params.seed < 0) {
+        params.seed = time(NULL);
+    }
+
+    printf("%s: seed = %d\n", __func__, params.seed);
+
+    std::mt19937 rng(params.seed);
+
+
+    int64_t t_load_us = 0;
+
+    gpt_vocab vocab;
+    gptj_model model;
+
+    // load the model
+    {
+        const int64_t t_start_us = ggml_time_us();
+
+        if (!legacy_gptj_model_load(params.model, model, vocab)) {
+            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
+            return 1;
+        }
+
+        t_load_us = ggml_time_us() - t_start_us;
+    }
+
+    int n_past = 0;
+
+    int64_t t_sample_us  = 0;
+    int64_t t_predict_us = 0;
+
+    std::vector<float> logits;
+
+    // tokenize the prompt
+    std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(vocab, params.prompt);
+
+    params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
+
+    printf("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+    printf("\n");
+
+    std::vector<gpt_vocab::id> embd;
+
+    // determine the required inference memory per token:
+    size_t mem_per_token = 0;
+    legacy_gptj_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
+
+    for (int i = embd.size(); i < embd_inp.size() + params.n_predict; i++) {
+        // predict
+        if (embd.size() > 0) {
+            const int64_t t_start_us = ggml_time_us();
+
+            if (!legacy_gptj_eval(model, params.n_threads, n_past, embd, logits, mem_per_token)) {
+                printf("Failed to predict\n");
+                return 1;
+            }
+
+            t_predict_us += ggml_time_us() - t_start_us;
+        }
+
+        n_past += embd.size();
+        embd.clear();
+
+        if (i >= embd_inp.size()) {
+            // sample next token
+            const int   top_k = params.top_k;
+            const float top_p = params.top_p;
+            const float temp  = params.temp;
+
+            const int n_vocab = model.hparams.n_vocab;
+
+            gpt_vocab::id id = 0;
+
+            {
+                const int64_t t_start_sample_us = ggml_time_us();
+
+                id = gpt_sample_top_k_top_p(vocab, logits.data() + (logits.size() - n_vocab), top_k, top_p, temp, rng);
+
+                t_sample_us += ggml_time_us() - t_start_sample_us;
+            }
+
+            // add it to the context
+            embd.push_back(id);
+        } else {
+            // if here, it means we are still processing the input prompt
+            for (int k = i; k < embd_inp.size(); k++) {
+                embd.push_back(embd_inp[k]);
+                if (embd.size() > params.n_batch) {
+                    break;
+                }
+            }
+            i += embd.size() - 1;
+        }
+
+        // display text
+        for (auto id : embd) {
+            printf("%s", vocab.id_to_token[id].c_str());
+        }
+        fflush(stdout);
+
+        // end of text token
+        if (embd.back() == 50256) {
+            break;
+        }
+    }
+
+    // report timing
+    {
+        const int64_t t_main_end_us = ggml_time_us();
+
+        printf("\n\n");
+        printf("%s: mem per token = %8zu bytes\n", __func__, mem_per_token);
+        printf("%s:     load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
+        printf("%s:   sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
+        printf("%s:  predict time = %8.2f ms / %.2f ms per token\n", __func__, t_predict_us/1000.0f, t_predict_us/1000.0f/n_past);
+        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
+    }
+
+    ggml_free(model.ctx);
+
+    return 0;
+}
+
+bool llama_load_model(const load_model_inputs inputs, FileFormat in_file_format)
+{
+    printf("System Info: %s\n", llama_print_system_info());
+
+    ctx_params = llama_context_default_params();
+
+    n_threads = inputs.threads;
+    n_batch = inputs.batch_size;
+    model = inputs.model_filename;
+
+    ctx_params.n_ctx = inputs.max_context_length;
+    ctx_params.n_parts = inputs.n_parts_overwrite;
+    ctx_params.seed = -1;
+    ctx_params.f16_kv = inputs.f16_kv;
+    ctx_params.logits_all = false;
+
+    file_format = in_file_format;
+
+    if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF)
+    {
+        ctx = legacy_llama_init_from_file(model.c_str(), ctx_params);
+    }
+    else
+    {
+        ctx = llama_init_from_file(model.c_str(), ctx_params);
+    }
+
+    if (ctx == NULL)
+    {
+        fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, model.c_str());
+        return false;
+    }
+
+    if (file_format < FileFormat::GGJT)
+    {
+        printf("\n---\nWarning: Your model has an INVALID or OUTDATED format (ver %d). Please reconvert it for better results!\n---\n", file_format);
+    }
+
+    //determine mem per token
+    const std::vector<llama_token> tmp = {0, 1, 2, 3};
+    llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
+
+    return true;
+}
+
+generation_outputs llama_generate(const generation_inputs inputs, generation_outputs &output)
+{
+    params.prompt = inputs.prompt;
+    params.seed = inputs.seed;
+    params.n_predict = inputs.max_length;
+    params.top_k = inputs.top_k;
+    params.top_p = inputs.top_p;
+    params.temp = inputs.temperature;
+    params.repeat_last_n = inputs.rep_pen_range;
+    params.repeat_penalty = inputs.rep_pen;
+    params.n_ctx = inputs.max_context_length;
+    params.n_batch = n_batch;
+    params.n_threads = n_threads;
+
+    if (params.repeat_last_n < 1)
+    {
+        params.repeat_last_n = 1;
+    }
+    if (params.top_k < 1)
+    {
+        params.top_k = 300; //to disable top_k we actually need to increase this value to a very high number
+    }
+    if (params.seed <= 0)
+    {
+        params.seed = time(NULL);
+    }
+
+    params.prompt.insert(0, 1, ' ');
+
+    // tokenize the prompt
+    std::vector<llama_token> embd_inp;
+    if (file_format == 1)
+    {
+        embd_inp = ::legacy_llama_tokenize(ctx, params.prompt, true);
+    }
+    else
+    {
+        embd_inp = ::llama_tokenize(ctx, params.prompt, true);
+    }
+
+    //params.n_predict = std::min(params.n_predict, params.n_ctx - (int) embd_inp.size());
+    //truncate to front of the prompt if its too long
+    if (embd_inp.size() + params.n_predict > params.n_ctx)
+    {
+        int offset = embd_inp.size() - params.n_ctx + params.n_predict;
+        embd_inp = std::vector<llama_token>(embd_inp.begin() + offset, embd_inp.end());
+    }
+
+    //determine how much npast we have to rewind from the current state
+
+    std::vector<llama_token> embd;
+
+    int last_n_size = params.repeat_last_n;
+    last_n_tokens.resize(last_n_size);
+
+    //display usage
+    // std::string tst = " ";
+    // char * tst2 = (char*)tst.c_str();
+    // gpt_print_usage(1,&tst2,params);
+
+    std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
+    n_past = 0;
+
+    //fast forward the past based on identical tokens, stop once a divergence is noted
+    int embd_inp_len = embd_inp.size();
+    for (int i = 0; i < current_context_tokens.size(); ++i)
+    {
+        if (current_context_tokens[i] == embd_inp[i])
+        {
+            n_past += 1;
+            last_n_tokens.push_back(current_context_tokens[i]);
+        }
+        else
+        {
+            break;
+        }
+        if ((i + 2) >= embd_inp_len)
+        {
+            break;
+        }
+    }
+
+    last_n_tokens.erase(last_n_tokens.begin(), last_n_tokens.begin() + n_past);
+    embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_past);
+
+    //if using BLAS and prompt is big enough, switch to single thread and use a huge batch
+    bool blasmode = (embd_inp.size() >= 32 && ggml_cpu_has_blas());
+    int original_batch = params.n_batch;
+    int original_threads = params.n_threads;
+    if (blasmode)
+    {
+        params.n_batch = 512;
+        params.n_threads = 1;
+    }
+
+    current_context_tokens.resize(n_past);
+
+    int remaining_tokens = params.n_predict;
+    int input_consumed = 0;
+    std::mt19937 rng(params.seed);
+    std::string concat_output = "";
+
+    bool startedsampling = false;
+
+    timer_start();
+    double time1 = 0, time2 = 0;
+    unsigned int embd_inp_size = embd_inp.size();
+    printf("\n");
+
+    while (remaining_tokens > 0)
+    {
+        llama_token id = 0;
+        // predict
+        unsigned int embdsize = embd.size();
+        if (embdsize > 0)
+        {
+            //print progress
+            if (!startedsampling)
+            {
+                printf("\rProcessing Prompt%s (%d / %d tokens)", (blasmode ? " [BLAS]" : ""), input_consumed, embd_inp_size);
+            }
+            else
+            {
+                printf("\rGenerating (%d / %d tokens)", (1 + params.n_predict - remaining_tokens), params.n_predict);
+            }
+            //printf("\nnp:%d embd:%d txt:%s",n_past,embd.size(),llama_token_to_str(ctx, embd[0]));
+            if (llama_eval(ctx, embd.data(), embdsize, n_past, params.n_threads))
+            {
+                fprintf(stderr, "Failed to predict\n");
+                snprintf(output.text, sizeof(output.text), "%s", "");
+                output.status = 0;
+                return output;
+            }
+        }
+
+        n_past += embd.size();
+        embd.clear();
+        if ((int)embd_inp.size() <= input_consumed)
+        {
+            // out of user input, sample next token
+            const float top_k = params.top_k;
+            const float top_p = params.top_p;
+            const float temp = params.temp;
+            const float repeat_penalty = params.repeat_penalty;
+
+            if (!startedsampling)
+            {
+                startedsampling = true;
+                params.n_batch = original_batch;
+                params.n_threads = original_threads;
+                time1 = timer_check();
+                timer_start();
+                printf("\n");
+            }
+
+            {
+                auto logits = llama_get_logits(ctx);
+                // set the logit of the eos token (2) to zero to avoid sampling it
+                logits[llama_token_eos()] = 0;
+                //set logits of opening square bracket to zero.
+                logits[518] = 0;
+                logits[29961] = 0;
+
+                id = llama_sample_top_p_top_k(ctx, last_n_tokens.data(), last_n_tokens.size(), top_k, top_p, temp, repeat_penalty);
+
+                last_n_tokens.erase(last_n_tokens.begin());
+                last_n_tokens.push_back(id);
+                current_context_tokens.push_back(id);
+            }
+
+            // add it to the context
+            embd.push_back(id);
+
+            // decrement remaining sampling budget
+            --remaining_tokens;
+            //printf("\nid:%d word:%s\n",id,llama_token_to_str(ctx, id));
+            concat_output += llama_token_to_str(ctx, id);
+        }
+        else
+        {
+            // some user input remains from prompt or interaction, forward it to processing
+            while ((int)embd_inp.size() > input_consumed)
+            {
+                embd.push_back(embd_inp[input_consumed]);
+                last_n_tokens.erase(last_n_tokens.begin());
+                last_n_tokens.push_back(embd_inp[input_consumed]);
+                current_context_tokens.push_back(embd_inp[input_consumed]);
+                ++input_consumed;
+                if ((int)embd.size() >= params.n_batch)
+                {
+                    break;
+                }
+            }
+        }
+    }
+    time2 = timer_check();
+    printf("\nTime Taken - Processing:%.1fs, Generation:%.1fs, Total:%.1fs", time1, time2, (time1 + time2));
+
+    output.status = 1;
+    snprintf(output.text, sizeof(output.text), "%s", concat_output.c_str());
+    return output;
+}

llama_adapter.cpp

@@ -9,12 +9,11 @@
 
 #include <time.h>
 #include "./examples/main/main.cpp"
-#include "extra.h"
 #include "ggml.h"
-#include "llama_adapter.h"
+#include "model_adapter.h"
 
 //return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
-static int file_format = 0;
+static FileFormat file_format = FileFormat::FAIL;
 static llama_context_params ctx_params;
 static gpt_params params;
 static int n_past = 0;
@@ -25,36 +24,7 @@ static llama_context *ctx;
 static std::vector<llama_token> last_n_tokens;
 static std::vector<llama_token> current_context_tokens;
 
-static clock_t bench_timer = 0;
-
-void timer_start()
-{
-    bench_timer = clock();
-}
-double timer_check()
-{
-    double ticks = clock() - bench_timer;
-    double time_taken = ((double)ticks) / CLOCKS_PER_SEC;
-    return time_taken;
-}
-
-void print_tok_vec(std::vector<llama_token> &embd)
-{
-    std::cout << "[";
-    bool first = true;
-    for (auto i : embd)
-    {
-        if (!first)
-        {
-            std::cout << ',';
-        }
-        first = false;
-        std::cout << i;
-    }
-    std::cout << "]";
-}
-
-bool llama_load_model(const load_model_inputs inputs, FileFormat file_format)
+bool llama_load_model(const load_model_inputs inputs, FileFormat in_file_format)
 {
     printf("System Info: %s\n", llama_print_system_info());
 
@@ -70,6 +40,8 @@ bool llama_load_model(const load_model_inputs inputs, FileFormat file_format)
     ctx_params.f16_kv = inputs.f16_kv;
     ctx_params.logits_all = false;
 
+    file_format = in_file_format;
+
     if (file_format == FileFormat::GGML || file_format == FileFormat::GGHF)
     {
         ctx = legacy_llama_init_from_file(model.c_str(), ctx_params);

llamacpp_for_kobold.py

@@ -334,9 +334,9 @@ def main(args):
     print(f"Starting Kobold HTTP Server on port {args.port}")
     epurl = ""
     if args.host=="":
-        epurl = f"http://localhost:{args.port}" + ("?streaming=1" if not args.nostream else "")   
+        epurl = f"http://localhost:{args.port}" + ("?streaming=1" if args.stream else "")   
     else:
-        epurl = f"http://{args.host}:{args.port}?host={args.host}" + ("&streaming=1" if not args.nostream else "")   
+        epurl = f"http://{args.host}:{args.port}?host={args.host}" + ("&streaming=1" if args.stream else "")   
     
         
     print(f"Please connect to custom endpoint at {epurl}")
@@ -351,7 +351,7 @@ if __name__ == '__main__':
     parser.add_argument("--host", help="Host IP to listen on. If empty, all routable interfaces are accepted.", default="")
     default_threads = (os.cpu_count() if os.cpu_count()<=6 else max(6,os.cpu_count()-2))
     parser.add_argument("--threads", help="Use a custom number of threads if specified. Otherwise, uses an amount based on CPU cores", type=int, default=default_threads)
-    parser.add_argument("--nostream", help="Disables pseudo streaming", action='store_true')
+    parser.add_argument("--stream", help="Uses pseudo streaming", action='store_true')
     parser.add_argument("--noblas", help="Do not use OpenBLAS for accelerated prompt ingestion", action='store_true')
     args = parser.parse_args()
     main(args)