halfway refactoring, wip adding other model types

llama_adapter.cpp

@@ -0,0 +1,301 @@
+//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 "./examples/main/main.cpp"
+#include "extra.h"
+#include "ggml.h"
+#include "llama_adapter.h"
+
+//return val: 0=fail, 1=(original ggml, alpaca), 2=(ggmf), 3=(ggjt)
+static int file_format = 0;
+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;
+
+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)
+{
+    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;
+
+    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;
+}