added ability to fast forward in time through partially duplicated prompts
This commit is contained in:
Concedo
parent
1166fda943
commit
706e19e9b4
3 changed files with 53 additions and 53 deletions
74
expose.cpp
74
expose.cpp
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@ -10,6 +10,21 @@
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#include "main.cpp"
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#include "extra.h"
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void print_tok_vec(std::vector<llama_token> & embd)
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{
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std::cout << "[";
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bool first = true;
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for (auto i: embd) {
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if(!first)
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{
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std::cout << ',';
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}
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first = false;
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std::cout << i;
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}
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std::cout << "]";
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}
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extern "C" {
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struct load_model_inputs
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@ -31,7 +46,6 @@ extern "C" {
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const float top_p;
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const float rep_pen;
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const int rep_pen_range;
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const bool reset_state = true; //determines if we can continue off the previous prompt state
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};
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struct generation_outputs
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{
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@ -43,12 +57,12 @@ extern "C" {
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llama_context_params ctx_params;
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gpt_params params;
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int n_past = 0;
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llama_token old_embd_id = -1;
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int n_threads = 4;
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int n_batch = 8;
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std::string model;
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llama_context * ctx;
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std::vector<llama_token> last_n_tokens;
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std::vector<llama_token> current_context_tokens;
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bool load_model(const load_model_inputs inputs)
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{
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@ -80,6 +94,10 @@ extern "C" {
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printf("\n---\nWarning: Your model is using an OUTDATED format. Please reconvert it for better results!\n");
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}
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//determine mem per token
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const std::vector<llama_token> tmp = { 0, 1, 2, 3 };
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llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
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return true;
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}
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@ -97,12 +115,6 @@ extern "C" {
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params.n_batch = n_batch;
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params.n_threads = n_threads;
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bool reset_state = inputs.reset_state;
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if(n_past==0)
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{
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reset_state = true;
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}
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if(params.repeat_last_n<1)
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{
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params.repeat_last_n = 1;
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@ -116,10 +128,7 @@ extern "C" {
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params.seed = time(NULL);
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}
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if(reset_state)
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{
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params.prompt.insert(0, 1, ' ');
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}
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params.prompt.insert(0, 1, ' ');
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// tokenize the prompt
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std::vector<llama_token> embd_inp;
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@ -136,6 +145,9 @@ extern "C" {
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int offset = embd_inp.size() - params.n_ctx + params.n_predict;
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embd_inp = std::vector<llama_token>(embd_inp.begin() + offset, embd_inp.end());
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}
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//determine how much npast we have to rewind from the current state
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std::vector<llama_token> embd;
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int last_n_size = params.repeat_last_n;
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@ -146,25 +158,29 @@ extern "C" {
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// char * tst2 = (char*)tst.c_str();
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// gpt_print_usage(1,&tst2,params);
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if(reset_state)
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std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
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n_past = 0;
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//fast forward the past based on identical tokens, stop once a divergence is noted
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for(int i=0;i<current_context_tokens.size();++i)
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{
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const std::vector<llama_token> tmp = { 0, 1, 2, 3 };
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llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
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std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
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n_past = 0;
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}
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else
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{
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//strip out the reset token (1) at the start of the embedding
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if(embd_inp.size()>0)
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if(current_context_tokens[i]==embd_inp[0])
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{
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n_past += 1;
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embd_inp.erase(embd_inp.begin());
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(current_context_tokens[i]);
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}
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if(old_embd_id!=-1)
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else
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{
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embd.push_back(old_embd_id);
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break;
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}
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if(embd_inp.size()<=1)
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{
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break;
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}
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}
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current_context_tokens.resize(n_past);
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int remaining_tokens = params.n_predict;
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int input_consumed = 0;
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@ -181,10 +197,7 @@ extern "C" {
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if (embd.size() > 0)
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{
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printf("|");
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// for (auto i: embd) {
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// std::cout << i << ',';
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// }
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// printf("\nnp:%d embd:%d",n_past,embd.size());
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//printf("\nnp:%d embd:%d txt:%s",n_past,embd.size(),llama_token_to_str(ctx, embd[0]));
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if (llama_eval(ctx, embd.data(), embd.size(), n_past, params.n_threads))
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{
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fprintf(stderr, "Failed to predict\n");
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@ -222,13 +235,12 @@ extern "C" {
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(id);
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current_context_tokens.push_back(id);
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}
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// add it to the context
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old_embd_id = id;
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embd.push_back(id);
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// decrement remaining sampling budget
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--remaining_tokens;
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//printf("\nid:%d word:%s\n",id,llama_token_to_str(ctx, id));
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@ -239,10 +251,10 @@ extern "C" {
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// some user input remains from prompt or interaction, forward it to processing
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while ((int) embd_inp.size() > input_consumed)
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{
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old_embd_id = embd_inp[input_consumed];
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embd.push_back(embd_inp[input_consumed]);
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last_n_tokens.erase(last_n_tokens.begin());
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last_n_tokens.push_back(embd_inp[input_consumed]);
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current_context_tokens.push_back(embd_inp[input_consumed]);
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++input_consumed;
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if ((int) embd.size() >= params.n_batch)
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{
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@ -23,8 +23,7 @@ class generation_inputs(ctypes.Structure):
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("top_k", ctypes.c_int),
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("top_p", ctypes.c_float),
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("rep_pen", ctypes.c_float),
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("rep_pen_range", ctypes.c_int),
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("reset_state", ctypes.c_bool)]
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("rep_pen_range", ctypes.c_int)]
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class generation_outputs(ctypes.Structure):
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_fields_ = [("status", ctypes.c_int),
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@ -48,7 +47,7 @@ def load_model(model_filename,batch_size=8,max_context_length=512,n_parts_overwr
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ret = handle.load_model(inputs)
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return ret
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def generate(prompt,max_length=20, max_context_length=512,temperature=0.8,top_k=100,top_p=0.85,rep_pen=1.1,rep_pen_range=128,seed=-1,reset_state=True):
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def generate(prompt,max_length=20, max_context_length=512,temperature=0.8,top_k=100,top_p=0.85,rep_pen=1.1,rep_pen_range=128,seed=-1):
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inputs = generation_inputs()
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outputs = ctypes.create_unicode_buffer(ctypes.sizeof(generation_outputs))
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inputs.prompt = prompt.encode("UTF-8")
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@ -60,7 +59,6 @@ def generate(prompt,max_length=20, max_context_length=512,temperature=0.8,top_k=
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inputs.rep_pen = rep_pen
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inputs.rep_pen_range = rep_pen_range
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inputs.seed = seed
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inputs.reset_state = reset_state
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ret = handle.generate(inputs,outputs)
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if(ret.status==1):
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return ret.text.decode("UTF-8")
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@ -80,7 +78,6 @@ maxctx = 2048
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maxlen = 128
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modelbusy = False
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port = 5001
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last_context = ""
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embedded_kailite = None
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class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
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@ -130,7 +127,6 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
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def do_POST(self):
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global modelbusy
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global last_context
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content_length = int(self.headers['Content-Length'])
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body = self.rfile.read(content_length)
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@ -159,17 +155,13 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
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self.end_headers()
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return
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print("\nInput: " + json.dumps(genparams))
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fresh_state = True
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modelbusy = True
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if kai_api_flag:
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fullprompt = genparams.get('prompt', "")
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else:
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fullprompt = genparams.get('text', "")
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newprompt = fullprompt
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if last_context!="" and newprompt.startswith(last_context):
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fresh_state = False
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newprompt = newprompt[len(last_context):]
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print("Resuming state, new input len: " + str(len(newprompt)))
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recvtxt = ""
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@ -183,11 +175,9 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
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top_p=genparams.get('top_p', 0.85),
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rep_pen=genparams.get('rep_pen', 1.1),
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rep_pen_range=genparams.get('rep_pen_range', 128),
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seed=-1,
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reset_state=fresh_state
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seed=-1
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)
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print("\nOutput: " + recvtxt)
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last_context = fullprompt + recvtxt
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res = {"results": [{"text": recvtxt}]}
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self.send_response(200)
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self.end_headers()
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@ -201,11 +191,9 @@ class ServerRequestHandler(http.server.SimpleHTTPRequestHandler):
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top_p=genparams.get('top_p', 0.85),
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rep_pen=genparams.get('rep_pen', 1.1),
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rep_pen_range=genparams.get('rep_pen_range', 128),
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seed=-1,
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reset_state=fresh_state
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seed=-1
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)
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print("\nOutput: " + recvtxt)
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last_context = fullprompt + recvtxt
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res = {"data": {"seqs":[recvtxt]}}
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self.send_response(200)
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self.end_headers()
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BIN
llamacpp.dll
BIN
llamacpp.dll
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