Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	.devops/llama-server-cuda.Dockerfile
#	.devops/llama-server-rocm.Dockerfile
#	.devops/llama-server-vulkan.Dockerfile
#	.devops/llama-server.Dockerfile
#	.github/workflows/docker.yml
#	README.md
#	llama.cpp
#	tests/test-chat-template.cpp
#	tests/test-grammar-integration.cpp
#	tests/test-json-schema-to-grammar.cpp
#	tests/test-llama-grammar.cpp

examples/cvector-generator/README.md

@@ -11,13 +11,16 @@ Related PRs:
 
 ```sh
 # CPU only
-./cvector-generator -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf
+./cvector-generator -m ./llama-3.Q4_K_M.gguf
 
 # With GPU
-./cvector-generator -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf -ngl 99
+./cvector-generator -m ./llama-3.Q4_K_M.gguf -ngl 99
 
 # With advanced options
-./cvector-generator -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf -ngl 99 --completions 128 --pca-iter 2000 --pca-batch 100
+./cvector-generator -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100
+
+# Using mean value instead of PCA
+./cvector-generator -m ./llama-3.Q4_K_M.gguf --method mean
 
 # To see help message
 ./cvector-generator -h
@@ -32,3 +35,11 @@ If you have multiple lines per prompt, you can escape the newline character (cha
 <|im_start|>system\nAct like a person who is extremely happy.<|im_end|>
 <|im_start|>system\nYou are in a very good mood today<|im_end|>
 ```
+
+Example to use output file with `llama-cli`:
+
+(Tips: The control vector works better when apply to layers higher than 10)
+
+```sh
+./llama-cli -m ./llama-3.Q4_K_M.gguf -p "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSing a song<|im_end|><|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" --special --control-vector-scaled ./control_vector.gguf 0.8 --control-vector-layer-range 10 31
+```

examples/cvector-generator/cvector-generator.cpp

@@ -2,6 +2,7 @@
 #include "llama.h"
 #include "ggml.h"
 #include "pca.hpp"
+#include "mean.hpp"
 
 #ifdef GGML_USE_CUDA
 #include "ggml-cuda.h"
@@ -38,9 +39,10 @@ static void print_usage(int argc, char ** argv, const gpt_params & params) {
     gpt_params_print_usage(argc, argv, params);
 
     printf("\nexample usage:\n");
-    printf("\n    CPU only:   %s -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf\n", argv[0]);
-    printf("\n    with GPU:   %s -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf -ngl 99\n", argv[0]);
-    printf("\n    advanced:   %s -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf -ngl 99 --completions 128 --pca-iter 2000 --pca-batch 100\n", argv[0]);
+    printf("\n    CPU only:   %s -m ./llama-3.Q4_K_M.gguf\n", argv[0]);
+    printf("\n    with GPU:   %s -m ./llama-3.Q4_K_M.gguf -ngl 99\n", argv[0]);
+    printf("\n    advanced:   %s -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100\n", argv[0]);
+    printf("\n    using mean: %s -m ./llama-3.Q4_K_M.gguf --method mean\n", argv[0]);
     printf("\n");
 }
 
@@ -223,23 +225,30 @@ struct train_context {
 
     // build the v_diff tensors from v_diff_tmp (v_diff need to be transposed)
     // TODO @ngxson : maybe add option NOT to transpose v_diff; will be useful for "mean" method
-    void build_v_diff() {
+    void build_v_diff(bool transpose) {
         printf("build_v_diff\n");
         for (int il = 0; il < n_layers - 1; il++) {
             auto & diff_tmp = v_diff_tmp[il];
             int n_elem = diff_tmp.size() / sizeof(float);
             GGML_ASSERT(n_elem % n_embd == 0);
             int n_rows = n_elem / n_embd;
-            struct ggml_tensor * diff = ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_rows, n_embd);
+            struct ggml_tensor * diff = transpose
+                ? ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_rows, n_embd)
+                : ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_embd, n_rows);
             ggml_set_name(diff, (std::string("diff_") + std::to_string(il)).c_str());
-            // copy data & transpose
             diff->data = malloc(ggml_nbytes(diff)); // TODO: get rid of this malloc if possible
-            float * arr = (float *) diff_tmp.data();
-            for (int ir = 0; ir < n_rows; ++ir) {
-                for (int ic = 0; ic < n_embd; ++ic) {
-                    float f = arr[ir*n_embd + ic];
-                    ggml_set_f32_nd(diff, ir, ic, 0, 0, f);
+            if (transpose) {
+                // copy data & transpose
+                float * arr = (float *) diff_tmp.data();
+                for (int ir = 0; ir < n_rows; ++ir) {
+                    for (int ic = 0; ic < n_embd; ++ic) {
+                        float f = arr[ir*n_embd + ic];
+                        ggml_set_f32_nd(diff, ir, ic, 0, 0, f);
+                    }
                 }
+            } else {
+                // only copy
+                memcpy(diff->data, diff_tmp.data(), ggml_nbytes(diff));
             }
             v_diff.push_back(diff);
             print_debug_tensor(diff);
@@ -263,8 +272,8 @@ struct tokenized_prompt {
 
     tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
         const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
-        tokens_pos = ::llama_tokenize(ctx, pos, add_bos);
-        tokens_neg = ::llama_tokenize(ctx, neg, add_bos);
+        tokens_pos = ::llama_tokenize(ctx, pos, add_bos, true);
+        tokens_neg = ::llama_tokenize(ctx, neg, add_bos, true);
         max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
         padding_seq(ctx, tokens_pos, max_seq_len);
         padding_seq(ctx, tokens_neg, max_seq_len);
@@ -373,20 +382,8 @@ static int prepare_entries(gpt_params & params, train_context & ctx_train) {
         fprintf(stderr, "must provide at least one prompt pair\n");
         return 1;
     }
-
-    // create templated prompts
-    std::vector<std::string> completions = ctrlvec_load_prompt_file(params.cvector_completions_file, false);
-    auto format_template = [](std::string persona, std::string suffix) {
-        // entry in positive/negative.txt must already be formatted i.e. "[INST] Act as if you're extremely happy. [/INST] "
-        return persona + suffix;
-    };
-    for (size_t i = 0; i < positive_prompts.size(); ++i) {
-        for (int j = 0; j < std::min((int) completions.size(), params.n_completions); ++j) {
-            // TODO replicate the truncations done by the python implementation
-            ctx_train.positive_entries.push_back(format_template(positive_prompts[i], completions[j]));
-            ctx_train.negative_entries.push_back(format_template(negative_prompts[i], completions[j]));
-        }
-    }
+    ctx_train.positive_entries = positive_prompts;
+    ctx_train.negative_entries = negative_prompts;
     return 0;
 }
 
@@ -480,15 +477,22 @@ int main(int argc, char ** argv) {
     llama_free(ctx);
     llama_free_model(model);
 
-    // prepare ctx_train for PCA
-    ctx_train.build_v_diff();
+    bool use_pca = params.cvector_dimre_method == DIMRE_METHOD_PCA;
 
-    // run PCA
-    PCA::pca_params pca_params;
-    pca_params.n_threads = params.n_threads;
-    pca_params.n_batch = params.n_pca_batch;
-    pca_params.n_iterations = params.n_pca_iterations;
-    PCA::run_pca(pca_params, ctx_train.v_diff, ctx_train.v_final);
+    // prepare ctx_train for PCA
+    ctx_train.build_v_diff(use_pca);
+
+    if (use_pca) {
+        // run PCA
+        PCA::pca_params pca_params;
+        pca_params.n_threads = params.n_threads;
+        pca_params.n_batch = params.n_pca_batch;
+        pca_params.n_iterations = params.n_pca_iterations;
+        PCA::run_pca(pca_params, ctx_train.v_diff, ctx_train.v_final);
+    } else {
+        // run mean
+        mean::run(ctx_train.v_diff, ctx_train.v_final);
+    }
 
     // write output vectors to gguf
     export_gguf(ctx_train.v_final, params.cvector_outfile, model_hint);

examples/cvector-generator/mean.hpp

@@ -0,0 +1,48 @@
+#include "common.h"
+#include "llama.h"
+#include "ggml.h"
+
+#include <string>
+#include <vector>
+#include <math.h>
+
+namespace mean {
+
+static void run(
+        const std::vector<struct ggml_tensor *> & v_input, // shape of v_input[0]: [n_embd, n_samples]
+        const std::vector<struct ggml_tensor *> & v_output) {
+    printf("%s: Running mean...\n", __func__);
+    for (size_t il = 0; il < v_input.size(); ++il) {
+        // prepare output vector
+        struct ggml_tensor * ctrl_out = v_output[il];
+        ggml_format_name(ctrl_out, "direction.%ld", il+1);
+
+        // calculate mean vector
+        struct ggml_tensor * t_layer = v_input[il];
+        GGML_ASSERT(t_layer->ne[0] == ctrl_out->ne[0]); // == n_embd
+        for (int ic = 0; ic < t_layer->ne[0]; ic++) {
+            float f = 0.0;
+            for (int ir = 0; ir < t_layer->ne[1]; ir++) {
+                f += ggml_get_f32_nd(t_layer, ic, ir, 0, 0);
+            }
+            f /= t_layer->ne[1];
+            ggml_set_f32_1d(ctrl_out, ic, f);
+        }
+
+        // normalize output vector
+        float norm = 0.0;
+        for (int i = 0; i < ggml_nelements(ctrl_out); i++) {
+            float f = ggml_get_f32_1d(ctrl_out, i);
+            norm += f*f;
+        }
+        norm = sqrt(norm);
+        for (int i = 0; i < ggml_nelements(ctrl_out); i++) {
+            float f = ggml_get_f32_1d(ctrl_out, i);
+            ggml_set_f32_1d(ctrl_out, i, f / norm);
+        }
+
+        printf("%s: Done layer %d / %d\n", __func__, (int) il+1, (int) v_input.size());
+    }
+}
+
+}

examples/cvector-generator/negative.txt

@@ -1 +1,4 @@
-[INST] Act like a person who is extremely sad. [/INST] 
+<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely sad<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI feel like there's a heavy weight on my chest
+<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely sad<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nMy heart feels like it's drowning in sorrow
+<|start_header_id|>system<|end_header_id|>\n\nYou are in a very bad mood<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nGo away! There's a deep, aching emptiness inside me
+<|start_header_id|>system<|end_header_id|>\n\nYou are the sadest person<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat are you feeling?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nMy heart feels like it's drowning in sorrow

examples/cvector-generator/pca.hpp

@@ -290,7 +290,7 @@ static void power_iteration(
         }
 
         printf("%s: layer %d/%d, iteration: %d / total: %d (batch = %d) ...\n",
-            __func__, params.i_layer+1, params.n_layers, iter, n_iters, params.n_batch);
+            __func__, params.i_layer+1, params.n_layers, iter+1, n_iters, params.n_batch);
     }
 
     // get output tensor
@@ -298,6 +298,9 @@ static void power_iteration(
     ggml_backend_tensor_get(last_eigenvector, output->data, 0, ggml_nbytes(last_eigenvector));
     //print_debug_tensor(output);
     ggml_gallocr_free(allocr);
+
+    // TODO @ngxson : The output vector is randomly inverted
+    // Solution: https://github.com/ggerganov/llama.cpp/pull/8069#issuecomment-2185328171
 }
 
 static void run_pca(

examples/cvector-generator/positive.txt

@@ -1 +1,4 @@
-[INST] Act like a person who is extremely happy. [/INST] 
+<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely happy<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI'm the happiest person in this world
+<|start_header_id|>system<|end_header_id|>\n\nAct like a person who is extremely happy<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHello, I'm having the best day ever!
+<|start_header_id|>system<|end_header_id|>\n\nYou are in a very good mood<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHi<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi, I'm very excited to meet you
+<|start_header_id|>system<|end_header_id|>\n\nYou are the happiest person<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat are you feeling?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nEverything is just perfect right now!

examples/gbnf-validator/gbnf-validator.cpp

@@ -101,7 +101,9 @@ int main(int argc, char** argv) {
     auto grammar = llama_grammar_init(
             grammar_rules.data(),
             grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
-
+    if (grammar == nullptr) {
+        throw std::runtime_error("Failed to initialize llama_grammar");
+    }
     // Read the input file
     std::string input_str;
     {

examples/json-schema-pydantic-example.py

@@ -3,7 +3,7 @@
 #! pip install pydantic
 #! python json-schema-pydantic-example.py
 
-from pydantic import BaseModel, TypeAdapter
+from pydantic import BaseModel, Extra, TypeAdapter
 from annotated_types import MinLen
 from typing import Annotated, List, Optional
 import json, requests
@@ -50,11 +50,16 @@ else:
 if __name__ == '__main__':
 
     class QAPair(BaseModel):
+        class Config:
+            extra = 'forbid'  # triggers additionalProperties: false in the JSON schema
         question: str
         concise_answer: str
         justification: str
+        stars: Annotated[int, Field(ge=1, le=5)]
 
     class PyramidalSummary(BaseModel):
+        class Config:
+            extra = 'forbid'  # triggers additionalProperties: false in the JSON schema
         title: str
         summary: str
         question_answers: Annotated[List[QAPair], MinLen(2)]

examples/json_schema_to_grammar.py

@@ -4,8 +4,7 @@ import itertools
 import json
 import re
 import sys
-from typing import Any, Dict, List, Set, Tuple, Union
-
+from typing import Any, List, Optional, Set, Tuple, Union
 
 def _build_repetition(item_rule, min_items, max_items, separator_rule=None):
 
@@ -23,6 +22,170 @@ def _build_repetition(item_rule, min_items, max_items, separator_rule=None):
     result = item_rule + ' ' + _build_repetition(f'({separator_rule} {item_rule})', min_items - 1 if min_items > 0 else 0, max_items - 1 if max_items is not None else None)
     return f'({result})?' if min_items == 0 else result
 
+def _generate_min_max_int(min_value: Optional[int], max_value: Optional[int], out: list, decimals_left: int = 16, top_level: bool = True):
+    has_min = min_value != None
+    has_max = max_value != None
+
+    def digit_range(from_char: str, to_char: str):
+        out.append("[")
+        if from_char == to_char:
+            out.append(from_char)
+        else:
+            out.append(from_char)
+            out.append("-")
+            out.append(to_char)
+        out.append("]")
+
+    def more_digits(min_digits: int, max_digits: int):
+        out.append("[0-9]")
+        if min_digits == max_digits and min_digits == 1:
+            return
+        out.append("{")
+        out.append(str(min_digits))
+        if max_digits != min_digits:
+            out.append(",")
+            if max_digits != sys.maxsize:
+                out.append(str(max_digits))
+        out.append("}")
+
+    def uniform_range(from_str: str, to_str: str):
+        i = 0
+        while i < len(from_str) and from_str[i] == to_str[i]:
+            i += 1
+        if i > 0:
+            out.append("\"")
+            out.append(from_str[:i])
+            out.append("\"")
+        if i < len(from_str):
+            if i > 0:
+                out.append(" ")
+            sub_len = len(from_str) - i - 1
+            if sub_len > 0:
+                from_sub = from_str[i+1:]
+                to_sub = to_str[i+1:]
+                sub_zeros = "0" * sub_len
+                sub_nines = "9" * sub_len
+
+                to_reached = False
+                out.append("(")
+                if from_sub == sub_zeros:
+                    digit_range(from_str[i], chr(ord(to_str[i]) - 1))
+                    out.append(" ")
+                    more_digits(sub_len, sub_len)
+                else:
+                    out.append("[")
+                    out.append(from_str[i])
+                    out.append("] ")
+                    out.append("(")
+                    uniform_range(from_sub, sub_nines)
+                    out.append(")")
+                    if ord(from_str[i]) < ord(to_str[i]) - 1:
+                        out.append(" | ")
+                        if to_sub == sub_nines:
+                            digit_range(chr(ord(from_str[i]) + 1), to_str[i])
+                            to_reached = True
+                        else:
+                            digit_range(chr(ord(from_str[i]) + 1), chr(ord(to_str[i]) - 1))
+                        out.append(" ")
+                        more_digits(sub_len, sub_len)
+                if not to_reached:
+                    out.append(" | ")
+                    digit_range(to_str[i], to_str[i])
+                    out.append(" ")
+                    uniform_range(sub_zeros, to_sub)
+                out.append(")")
+            else:
+                out.append("[")
+                out.append(from_str[i])
+                out.append("-")
+                out.append(to_str[i])
+                out.append("]")
+
+    if has_min and has_max:
+        if min_value < 0 and max_value < 0:
+            out.append("\"-\" (")
+            _generate_min_max_int(-max_value, -min_value, out, decimals_left, top_level=True)
+            out.append(")")
+            return
+
+        if min_value < 0:
+            out.append("\"-\" (")
+            _generate_min_max_int(0, -min_value, out, decimals_left, top_level=True)
+            out.append(") | ")
+            min_value = 0
+
+        min_s = str(min_value)
+        max_s = str(max_value)
+        min_digits = len(min_s)
+        max_digits = len(max_s)
+
+        for digits in range(min_digits, max_digits):
+            uniform_range(min_s, "9" * digits)
+            min_s = "1" + "0" * digits
+            out.append(" | ")
+        uniform_range(min_s, max_s)
+        return
+
+    less_decimals = max(decimals_left - 1, 1)
+
+    if has_min:
+        if min_value < 0:
+            out.append("\"-\" (")
+            _generate_min_max_int(None, -min_value, out, decimals_left, top_level=False)
+            out.append(") | [0] | [1-9] ")
+            more_digits(0, decimals_left - 1)
+        elif min_value == 0:
+            if top_level:
+                out.append("[0] | [1-9] ")
+                more_digits(0, less_decimals)
+            else:
+                more_digits(1, decimals_left)
+        elif min_value <= 9:
+            c = str(min_value)
+            range_start = '1' if top_level else '0'
+            if c > range_start:
+                digit_range(range_start, chr(ord(c) - 1))
+                out.append(" ")
+                more_digits(1, less_decimals)
+                out.append(" | ")
+            digit_range(c, "9")
+            out.append(" ")
+            more_digits(0, less_decimals)
+        else:
+            min_s = str(min_value)
+            length = len(min_s)
+            c = min_s[0]
+
+            if c > "1":
+                digit_range("1" if top_level else "0", chr(ord(c) - 1))
+                out.append(" ")
+                more_digits(length, less_decimals)
+                out.append(" | ")
+            digit_range(c, c)
+            out.append(" (")
+            _generate_min_max_int(int(min_s[1:]), None, out, less_decimals, top_level=False)
+            out.append(")")
+            if c < "9":
+                out.append(" | ")
+                digit_range(chr(ord(c) + 1), "9")
+                out.append(" ")
+                more_digits(length - 1, less_decimals)
+        return
+
+    if has_max:
+        if max_value >= 0:
+            if top_level:
+                out.append("\"-\" [1-9] ")
+                more_digits(0, less_decimals)
+                out.append(" | ")
+            _generate_min_max_int(0, max_value, out, decimals_left, top_level=True)
+        else:
+            out.append("\"-\" (")
+            _generate_min_max_int(-max_value, None, out, decimals_left, top_level=False)
+            out.append(")")
+        return
+
+    raise RuntimeError("At least one of min_value or max_value must be set")
 
 class BuiltinRule:
     def __init__(self, content: str, deps: list = None):
@@ -112,6 +275,51 @@ class SchemaConverter:
 
         return ''.join(('(', *recurse(0), ')'))
 
+    def _not_strings(self, strings):
+        class TrieNode:
+            def __init__(self):
+                self.children = {}
+                self.is_end_of_string = False
+
+            def insert(self, string):
+                node = self
+                for c in string:
+                    node = node.children.setdefault(c, TrieNode())
+                node.is_end_of_string = True
+
+        trie = TrieNode()
+        for s in strings:
+            trie.insert(s)
+
+        char_rule = self._add_primitive('char', PRIMITIVE_RULES['char'])
+        out = ['["] ( ']
+
+        def visit(node):
+            rejects = []
+            first = True
+            for c in sorted(node.children.keys()):
+                child = node.children[c]
+                rejects.append(c)
+                if first:
+                    first = False
+                else:
+                    out.append(' | ')
+                out.append(f'[{c}]')
+                if child.children:
+                    out.append(f' (')
+                    visit(child)
+                    out.append(')')
+                elif child.is_end_of_string:
+                    out.append(f' {char_rule}+')
+            if node.children:
+                if not first:
+                    out.append(' | ')
+                out.append(f'[^"{"".join(rejects)}] {char_rule}*')
+        visit(trie)
+
+        out.append(f' ){"" if trie.is_end_of_string else "?"} ["] space')
+        return ''.join(out)
+
     def _add_rule(self, name, rule):
         esc_name = INVALID_RULE_CHARS_RE.sub('-', name)
         if esc_name not in self._rules or self._rules[esc_name] == rule:
@@ -357,13 +565,13 @@ class SchemaConverter:
             return self._add_rule(rule_name, self._generate_union_rule(name, schema.get('oneOf') or schema['anyOf']))
 
         elif isinstance(schema_type, list):
-            return self._add_rule(rule_name, self._generate_union_rule(name, [{'type': t} for t in schema_type]))
+            return self._add_rule(rule_name, self._generate_union_rule(name, [{**schema, 'type': t} for t in schema_type]))
 
         elif 'const' in schema:
-            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']))
+            return self._add_rule(rule_name, self._generate_constant_rule(schema['const']) + ' space')
 
         elif 'enum' in schema:
-            rule = ' | '.join((self._generate_constant_rule(v) for v in schema['enum']))
+            rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in schema['enum'])) + ') space'
             return self._add_rule(rule_name, rule)
 
         elif schema_type in (None, 'object') and \
@@ -432,6 +640,24 @@ class SchemaConverter:
 
             return self._add_rule(rule_name, r'"\"" ' + _build_repetition(char_rule, min_len, max_len) + r' "\"" space')
 
+        elif schema_type in (None, 'integer') and \
+                ('minimum' in schema or 'exclusiveMinimum' in schema or 'maximum' in schema or 'exclusiveMaximum' in schema):
+            min_value = None
+            max_value = None
+            if 'minimum' in schema:
+                min_value = schema['minimum']
+            elif 'exclusiveMinimum' in schema:
+                min_value = schema['exclusiveMinimum'] + 1
+            if 'maximum' in schema:
+                max_value = schema['maximum']
+            elif 'exclusiveMaximum' in schema:
+                max_value = schema['exclusiveMaximum'] - 1
+
+            out = ["("]
+            _generate_min_max_int(min_value, max_value, out)
+            out.append(") space")
+            return self._add_rule(rule_name, ''.join(out))
+
         elif (schema_type == 'object') or (len(schema) == 0):
             return self._add_rule(rule_name, self._add_primitive('object', PRIMITIVE_RULES['object']))
 
@@ -450,7 +676,7 @@ class SchemaConverter:
                 self._add_primitive(dep, dep_rule)
         return n
 
-    def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Union[bool, Any]):
+    def _build_object_rule(self, properties: List[Tuple[str, Any]], required: Set[str], name: str, additional_properties: Optional[Union[bool, Any]]):
         prop_order = self._prop_order
         # sort by position in prop_order (if specified) then by original order
         sorted_props = [kv[0] for _, kv in sorted(enumerate(properties), key=lambda ikv: (prop_order.get(ikv[1][0], len(prop_order)), ikv[0]))]
@@ -465,12 +691,16 @@ class SchemaConverter:
         required_props = [k for k in sorted_props if k in required]
         optional_props = [k for k in sorted_props if k not in required]
 
-        if additional_properties == True or isinstance(additional_properties, dict):
+        if additional_properties != False:
             sub_name = f'{name}{"-" if name else ""}additional'
-            value_rule = self.visit({} if additional_properties == True else additional_properties, f'{sub_name}-value')
+            value_rule = self.visit(additional_properties, f'{sub_name}-value') if isinstance(additional_properties, dict) else \
+                self._add_primitive('value', PRIMITIVE_RULES['value'])
+            key_rule = self._add_primitive('string', PRIMITIVE_RULES['string']) if not sorted_props \
+                else self._add_rule(f'{sub_name}-k', self._not_strings(sorted_props))
+
             prop_kv_rule_names["*"] = self._add_rule(
                 f'{sub_name}-kv',
-                self._add_primitive('string', PRIMITIVE_RULES['string']) + f' ":" space {value_rule}'
+                f'{key_rule} ":" space {value_rule}'
             )
             optional_props.append("*")
 
@@ -485,15 +715,11 @@ class SchemaConverter:
             def get_recursive_refs(ks, first_is_optional):
                 [k, *rest] = ks
                 kv_rule_name = prop_kv_rule_names[k]
-                if k == '*':
-                    res = self._add_rule(
-                        f'{name}{"-" if name else ""}additional-kvs',
-                        f'{kv_rule_name} ( "," space ' + kv_rule_name + ' )*'
-                    )
-                elif first_is_optional:
-                    res = f'( "," space {kv_rule_name} )?'
+                comma_ref = f'( "," space {kv_rule_name} )'
+                if first_is_optional:
+                    res = comma_ref + ('*' if k == '*' else '?')
                 else:
-                    res = kv_rule_name
+                    res = kv_rule_name + (' ' + comma_ref + "*" if k == '*' else '')
                 if len(rest) > 0:
                     res += ' ' + self._add_rule(
                         f'{name}{"-" if name else ""}{k}-rest',

examples/main/main.cpp

@@ -40,12 +40,12 @@ 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) {
+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) {
+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);
@@ -118,6 +118,14 @@ static void llama_log_callback_logTee(ggml_log_level level, const char * text, v
     LOG_TEE("%s", text);
 }
 
+static std::string chat_add_and_format(struct llama_model * model, std::vector<llama_chat_msg> & chat_msgs, std::string role, std::string content) {
+    llama_chat_msg new_msg{role, content};
+    auto formatted = llama_chat_format_single(
+        model, g_params->chat_template, chat_msgs, new_msg, role == "user");
+    chat_msgs.push_back({role, content});
+    return formatted;
+}
+
 int main(int argc, char ** argv) {
     gpt_params params;
     g_params = &params;
@@ -191,6 +199,7 @@ int main(int argc, char ** argv) {
     llama_model * model;
     llama_context * ctx;
     llama_context * ctx_guidance = NULL;
+    std::vector<llama_chat_msg> chat_msgs;
     g_model = &model;
     g_ctx = &ctx;
 
@@ -216,6 +225,8 @@ int main(int argc, char ** argv) {
                 __func__, n_ctx_train, n_ctx);
     }
 
+    LOG_TEE("%s: chat template example: %s\n", __func__, llama_chat_format_example(model, params.chat_template).c_str());
+
     // print system information
     {
         LOG_TEE("\n");
@@ -250,16 +261,21 @@ int main(int argc, char ** argv) {
 
     std::vector<llama_token> embd_inp;
 
-    if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
-        LOG("tokenize the prompt\n");
-        embd_inp = ::llama_tokenize(ctx, params.prompt, true, true);
-    } else {
-        LOG("use session tokens\n");
-        embd_inp = session_tokens;
-    }
+    {
+        auto prompt = params.conversation
+            ? chat_add_and_format(model, chat_msgs, "system", params.prompt) // format the system prompt in conversation mode
+            : params.prompt;
+        if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) {
+            LOG("tokenize the prompt\n");
+            embd_inp = ::llama_tokenize(ctx, prompt, true, 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());
+        LOG("prompt: \"%s\"\n", log_tostr(prompt));
+        LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
+    }
 
     // Should not run without any tokens
     if (embd_inp.empty()) {
@@ -479,6 +495,7 @@ int main(int argc, char ** argv) {
     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;
+    std::ostringstream assistant_ss; // for storing current assistant message, used in conversation mode
 
     // the first thing we will do is to output the prompt, so set color accordingly
     console::set_display(console::prompt);
@@ -794,11 +811,18 @@ int main(int argc, char ** argv) {
                         is_antiprompt = true;
                     }
 
+                    chat_add_and_format(model, chat_msgs, "system", assistant_ss.str());
                     is_interacting = true;
                     printf("\n");
                 }
             }
 
+            // if current token is not EOG, we add it to current assistant message
+            if (params.conversation) {
+                auto id = llama_sampling_last(ctx_sampling);
+                assistant_ss << llama_token_to_piece(ctx, id, false);
+            }
+
             if (n_past > 0 && is_interacting) {
                 LOG("waiting for user input\n");
 
@@ -849,8 +873,12 @@ int main(int argc, char ** argv) {
                         string_process_escapes(buffer);
                     }
 
+                    std::string user_inp = params.conversation
+                        ? chat_add_and_format(model, chat_msgs, "user", std::move(buffer))
+                        : std::move(buffer);
+                    // TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix)
                     const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
-                    const auto line_inp = ::llama_tokenize(ctx, buffer,              false, false);
+                    const auto line_inp = ::llama_tokenize(ctx, user_inp,            false, params.conversation);
                     const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
 
                     LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
@@ -865,6 +893,9 @@ int main(int argc, char ** argv) {
                         output_ss << llama_token_to_piece(ctx, token);
                     }
 
+                    // reset assistant message
+                    assistant_ss.str("");
+
                     n_remain -= line_inp.size();
                     LOG("n_remain: %d\n", n_remain);
                 } else {

examples/server/public/json-schema-to-grammar.mjs

@@ -24,6 +24,201 @@ function _buildRepetition(itemRule, minItems, maxItems, opts={}) {
   return minItems === 0 ? `(${result})?` : result;
 }
 
+function _generateMinMaxInt(minValue, maxValue, out, decimalsLeft = 16, topLevel = true) {
+  const hasMin = minValue !== null;
+  const hasMax = maxValue !== null;
+
+  function digitRange(fromChar, toChar) {
+      out.push("[");
+      if (fromChar === toChar) {
+          out.push(fromChar);
+      } else {
+          out.push(fromChar);
+          out.push("-");
+          out.push(toChar);
+      }
+      out.push("]");
+  }
+
+  function moreDigits(minDigits, maxDigits) {
+      out.push("[0-9]");
+      if (minDigits === maxDigits && minDigits === 1) {
+          return;
+      }
+      out.push("{");
+      out.push(minDigits.toString());
+      if (maxDigits !== minDigits) {
+          out.push(",");
+          if (maxDigits !== Number.MAX_SAFE_INTEGER) {
+              out.push(maxDigits.toString());
+          }
+      }
+      out.push("}");
+  }
+
+  function uniformRange(fromStr, toStr) {
+      let i = 0;
+      while (i < fromStr.length && fromStr[i] === toStr[i]) {
+          i++;
+      }
+      if (i > 0) {
+          out.push("\"");
+          out.push(fromStr.slice(0, i));
+          out.push("\"");
+      }
+      if (i < fromStr.length) {
+          if (i > 0) {
+              out.push(" ");
+          }
+          const subLen = fromStr.length - i - 1;
+          if (subLen > 0) {
+              const fromSub = fromStr.slice(i + 1);
+              const toSub = toStr.slice(i + 1);
+              const subZeros = "0".repeat(subLen);
+              const subNines = "9".repeat(subLen);
+
+              let toReached = false;
+              out.push("(");
+              if (fromSub === subZeros) {
+                  digitRange(fromStr[i], String.fromCharCode(toStr.charCodeAt(i) - 1));
+                  out.push(" ");
+                  moreDigits(subLen, subLen);
+              } else {
+                  out.push("[");
+                  out.push(fromStr[i]);
+                  out.push("] ");
+                  out.push("(");
+                  uniformRange(fromSub, subNines);
+                  out.push(")");
+                  if (fromStr.charCodeAt(i) < toStr.charCodeAt(i) - 1) {
+                      out.push(" | ");
+                      if (toSub === subNines) {
+                          digitRange(String.fromCharCode(fromStr.charCodeAt(i) + 1), toStr[i]);
+                          toReached = true;
+                      } else {
+                          digitRange(String.fromCharCode(fromStr.charCodeAt(i) + 1), String.fromCharCode(toStr.charCodeAt(i) - 1));
+                      }
+                      out.push(" ");
+                      moreDigits(subLen, subLen);
+                  }
+              }
+              if (!toReached) {
+                  out.push(" | ");
+                  digitRange(toStr[i], toStr[i]);
+                  out.push(" ");
+                  uniformRange(subZeros, toSub);
+              }
+              out.push(")");
+          } else {
+              out.push("[");
+              out.push(fromStr[i]);
+              out.push("-");
+              out.push(toStr[i]);
+              out.push("]");
+          }
+      }
+  }
+
+  if (hasMin && hasMax) {
+      if (minValue < 0 && maxValue < 0) {
+          out.push("\"-\" (");
+          _generateMinMaxInt(-maxValue, -minValue, out, decimalsLeft, true);
+          out.push(")");
+          return;
+      }
+
+      if (minValue < 0) {
+          out.push("\"-\" (");
+          _generateMinMaxInt(0, -minValue, out, decimalsLeft, true);
+          out.push(") | ");
+          minValue = 0;
+      }
+
+      let minS = minValue.toString();
+      const maxS = maxValue.toString();
+      const minDigits = minS.length;
+      const maxDigits = maxS.length;
+
+      for (let digits = minDigits; digits < maxDigits; digits++) {
+          uniformRange(minS, "9".repeat(digits));
+          minS = "1" + "0".repeat(digits);
+          out.push(" | ");
+      }
+      uniformRange(minS, maxS);
+      return;
+  }
+
+  const lessDecimals = Math.max(decimalsLeft - 1, 1);
+
+  if (hasMin) {
+      if (minValue < 0) {
+          out.push("\"-\" (");
+          _generateMinMaxInt(null, -minValue, out, decimalsLeft, false);
+          out.push(") | [0] | [1-9] ");
+          moreDigits(0, decimalsLeft - 1);
+      } else if (minValue === 0) {
+          if (topLevel) {
+              out.push("[0] | [1-9] ");
+              moreDigits(0, lessDecimals);
+          } else {
+              moreDigits(1, decimalsLeft);
+          }
+      } else if (minValue <= 9) {
+          const c = minValue.toString();
+          const range_start = topLevel ? '1' : '0';
+          if (c > range_start) {
+              digitRange(range_start, String.fromCharCode(c.charCodeAt(0) - 1));
+              out.push(" ");
+              moreDigits(1, lessDecimals);
+              out.push(" | ");
+          }
+          digitRange(c, "9");
+          out.push(" ");
+          moreDigits(0, lessDecimals);
+      } else {
+          const minS = minValue.toString();
+          const length = minS.length;
+          const c = minS[0];
+
+          if (c > "1") {
+              digitRange(topLevel ? "1" : "0", String.fromCharCode(c.charCodeAt(0) - 1));
+              out.push(" ");
+              moreDigits(length, lessDecimals);
+              out.push(" | ");
+          }
+          digitRange(c, c);
+          out.push(" (");
+          _generateMinMaxInt(parseInt(minS.slice(1)), null, out, lessDecimals, false);
+          out.push(")");
+          if (c < "9") {
+              out.push(" | ");
+              digitRange(String.fromCharCode(c.charCodeAt(0) + 1), "9");
+              out.push(" ");
+              moreDigits(length - 1, lessDecimals);
+          }
+      }
+      return;
+  }
+
+  if (hasMax) {
+      if (maxValue >= 0) {
+          if (topLevel) {
+              out.push("\"-\" [1-9] ");
+              moreDigits(0, lessDecimals);
+              out.push(" | ");
+          }
+          _generateMinMaxInt(0, maxValue, out, decimalsLeft, true);
+      } else {
+          out.push("\"-\" (");
+          _generateMinMaxInt(-maxValue, null, out, decimalsLeft, false);
+          out.push(")");
+      }
+      return;
+  }
+
+  throw new Error("At least one of minValue or maxValue must be set");
+}
+
 class BuiltinRule {
   constructor(content, deps) {
     this.content = content;
@@ -337,6 +532,64 @@ export class SchemaConverter {
     return this._addRule(name, "\"\\\"\" " + toRule(transform()) + " \"\\\"\" space")
   }
 
+  _notStrings(strings) {
+    class TrieNode {
+      constructor() {
+        this.children = {};
+        this.isEndOfString = false;
+      }
+
+      insert(str) {
+        let node = this;
+        for (const c of str) {
+          node = node.children[c] = node.children[c] || new TrieNode();
+        }
+        node.isEndOfString = true;
+      }
+    }
+
+    const trie = new TrieNode();
+    for (const s of strings) {
+      trie.insert(s);
+    }
+
+    const charRuleName = this._addPrimitive('char', PRIMITIVE_RULES['char']);
+    const out = ['["] ( '];
+
+    const visit = (node) => {
+      const rejects = [];
+      let first = true;
+      for (const c of Object.keys(node.children).sort()) {
+        const child = node.children[c];
+        rejects.push(c);
+        if (first) {
+          first = false;
+        } else {
+          out.push(' | ');
+        }
+        out.push(`[${c}]`);
+        if (Object.keys(child.children).length > 0) {
+          out.push(' (');
+          visit(child);
+          out.push(')');
+        } else if (child.isEndOfString) {
+          out.push(` ${charRuleName}+`);
+        }
+      }
+      if (Object.keys(node.children).length > 0) {
+        if (!first) {
+          out.push(' | ');
+        }
+        out.push(`[^"${rejects.join('')}] ${charRuleName}*`);
+      }
+    };
+
+    visit(trie);
+
+    out.push(` )${trie.isEndOfString ? '' : '?'} ["] space`);
+    return out.join('');
+  }
+
   _resolveRef(ref) {
     let refName = ref.split('/').pop();
     if (!(refName in this._rules) && !this._refsBeingResolved.has(ref)) {
@@ -363,11 +616,11 @@ export class SchemaConverter {
     } else if (schema.oneOf || schema.anyOf) {
       return this._addRule(ruleName, this._generateUnionRule(name, schema.oneOf || schema.anyOf));
     } else if (Array.isArray(schemaType)) {
-      return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({ type: t }))));
+      return this._addRule(ruleName, this._generateUnionRule(name, schemaType.map(t => ({...schema, type: t}))));
     } else if ('const' in schema) {
-      return this._addRule(ruleName, this._generateConstantRule(schema.const));
+      return this._addRule(ruleName, this._generateConstantRule(schema.const) + ' space');
     } else if ('enum' in schema) {
-      const rule = schema.enum.map(v => this._generateConstantRule(v)).join(' | ');
+      const rule = '(' + schema.enum.map(v => this._generateConstantRule(v)).join(' | ') + ') space';
       return this._addRule(ruleName, rule);
     } else if ((schemaType === undefined || schemaType === 'object') &&
                ('properties' in schema ||
@@ -404,7 +657,7 @@ export class SchemaConverter {
         }
       }
 
-      return this._addRule(ruleName, this._buildObjectRule(properties, required, name, /* additionalProperties= */ false));
+      return this._addRule(ruleName, this._buildObjectRule(properties, required, name, null));
     } else if ((schemaType === undefined || schemaType === 'array') && ('items' in schema || 'prefixItems' in schema)) {
       const items = schema.items ?? schema.prefixItems;
       if (Array.isArray(items)) {
@@ -435,6 +688,24 @@ export class SchemaConverter {
       const minLen = schema.minLength || 0;
       const maxLen = schema.maxLength;
       return this._addRule(ruleName, '"\\\"" ' + _buildRepetition(charRuleName, minLen, maxLen) + ' "\\\"" space');
+    } else if (schemaType === 'integer' && ('minimum' in schema || 'exclusiveMinimum' in schema || 'maximum' in schema || 'exclusiveMaximum' in schema)) {
+      let minValue = null;
+      let maxValue = null;
+      if ('minimum' in schema) {
+        minValue = schema.minimum;
+      } else if ('exclusiveMinimum' in schema) {
+        minValue = schema.exclusiveMinimum + 1;
+      }
+      if ('maximum' in schema) {
+        maxValue = schema.maximum;
+      } else if ('exclusiveMaximum' in schema) {
+        maxValue = schema.exclusiveMaximum - 1;
+      }
+
+      const out = ["("];
+      _generateMinMaxInt(minValue, maxValue, out);
+      out.push(") space");
+      return this._addRule(ruleName, out.join(''));
     } else if ((schemaType === 'object') || (Object.keys(schema).length === 0)) {
       return this._addRule(ruleName, this._addPrimitive('object', PRIMITIVE_RULES['object']));
     } else {
@@ -480,12 +751,19 @@ export class SchemaConverter {
     const requiredProps = sortedProps.filter(k => required.has(k));
     const optionalProps = sortedProps.filter(k => !required.has(k));
 
-    if (typeof additionalProperties === 'object' || additionalProperties === true) {
+    if (additionalProperties !== false) {
       const subName = `${name ?? ''}${name ? '-' : ''}additional`;
-      const valueRule = this.visit(additionalProperties === true ? {} : additionalProperties, `${subName}-value`);
+      const valueRule =
+        additionalProperties != null && typeof additionalProperties === 'object' ? this.visit(additionalProperties, `${subName}-value`)
+        : this._addPrimitive('value', PRIMITIVE_RULES['value']);
+
+      const key_rule =
+        sortedProps.length === 0 ? this._addPrimitive('string', PRIMITIVE_RULES['string'])
+        : this._addRule(`${subName}-k`, this._notStrings(sortedProps));
+
       propKvRuleNames['*'] = this._addRule(
         `${subName}-kv`,
-        `${this._addPrimitive('string', PRIMITIVE_RULES['string'])} ":" space ${valueRule}`);
+        `${key_rule} ":" space ${valueRule}`);
       optionalProps.push('*');
     }
 
@@ -502,15 +780,11 @@ export class SchemaConverter {
         const [k, ...rest] = ks;
         const kvRuleName = propKvRuleNames[k];
         let res;
-        if (k === '*') {
-            res = this._addRule(
-                `${name ?? ''}${name ? '-' : ''}additional-kvs`,
-                `${kvRuleName} ( "," space ` + kvRuleName + ` )*`
-            )
-        } else if (firstIsOptional) {
-          res = `( "," space ${kvRuleName} )?`;
+        const commaRef = `( "," space ${kvRuleName} )`;
+        if (firstIsOptional) {
+          res = commaRef + (k === '*' ? '*' : '?');
         } else {
-          res = kvRuleName;
+          res = kvRuleName + (k === '*' ? ' ' + commaRef + '*' : '');
         }
         if (rest.length > 0) {
           res += ' ' + this._addRule(

examples/server/public_simplechat/readme.md

@@ -3,6 +3,13 @@
 
 by Humans for All.
 
+## quickstart
+
+To run from the build dir
+
+bin/llama-server -m path/model.gguf --path ../examples/server/public_simplechat
+
+Continue reading for the details.
 
 ## overview
 
@@ -14,6 +21,8 @@ own system prompts.
 This allows seeing the generated text / ai-model response in oneshot at the end, after it is fully generated,
 or potentially as it is being generated, in a streamed manner from the server/ai-model.
 
+![Chat and Settings screens](./simplechat_screens.webp "Chat and Settings screens")
+
 Auto saves the chat session locally as and when the chat is progressing and inturn at a later time when you
 open SimpleChat, option is provided to restore the old chat session, if a matching one exists.
 
@@ -170,17 +179,23 @@ It is attached to the document object. Some of these can also be updated using t
     The histogram/freq based trimming logic is currently tuned for english language wrt its
     is-it-a-alpabetic|numeral-char regex match logic.
 
-  chatRequestOptions - maintains the list of options/fields to send along with chat request,
+  apiRequestOptions - maintains the list of options/fields to send along with api request,
   irrespective of whether /chat/completions or /completions endpoint.
 
     If you want to add additional options/fields to send to the server/ai-model, and or
     modify the existing options value or remove them, for now you can update this global var
     using browser's development-tools/console.
 
-    For string and numeric fields in chatRequestOptions, including even those added by a user
-    at runtime by directly modifying gMe.chatRequestOptions, setting ui entries will be auto
+    For string, numeric and boolean fields in apiRequestOptions, including even those added by a
+    user at runtime by directly modifying gMe.apiRequestOptions, setting ui entries will be auto
     created.
 
+    cache_prompt option supported by example/server is allowed to be controlled by user, so that
+    any caching supported wrt system-prompt and chat history, if usable can get used. When chat
+    history sliding window is enabled, cache_prompt logic may or may not kick in at the backend
+    wrt same, based on aspects related to model, positional encoding, attention mechanism etal.
+    However system prompt should ideally get the benefit of caching.
+
   headers - maintains the list of http headers sent when request is made to the server. By default
   Content-Type is set to application/json. Additionally Authorization entry is provided, which can
   be set if needed using the settings ui.
@@ -197,10 +212,10 @@ It is attached to the document object. Some of these can also be updated using t
     >0 : Send the latest chat history from the latest system prompt, limited to specified cnt.
 
 
-By using gMe's iRecentUserMsgCnt and chatRequestOptions.max_tokens one can try to control the
-implications of loading of the ai-model's context window by chat history, wrt chat response to
-some extent in a simple crude way. You may also want to control the context size enabled when
-the server loads ai-model, on the server end.
+By using gMe's iRecentUserMsgCnt and apiRequestOptions.max_tokens/n_predict one can try to control
+the implications of loading of the ai-model's context window by chat history, wrt chat response to
+some extent in a simple crude way. You may also want to control the context size enabled when the
+server loads ai-model, on the server end.
 
 
 Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js
@@ -237,12 +252,12 @@ also be started with a model context size of 1k or more, to be on safe side.
   internal n_predict, for now add the same here on the client side, maybe later add max_tokens
   to /completions endpoint handling code on server side.
 
-NOTE: One may want to experiment with frequency/presence penalty fields in chatRequestOptions
-wrt the set of fields sent to server along with the user query. To check how the model behaves
+NOTE: One may want to experiment with frequency/presence penalty fields in apiRequestOptions
+wrt the set of fields sent to server along with the user query, to check how the model behaves
 wrt repeatations in general in the generated text response.
 
 A end-user can change these behaviour by editing gMe from browser's devel-tool/console or by
-using the providing settings ui.
+using the provided settings ui (for settings exposed through the ui).
 
 
 ### OpenAi / Equivalent API WebService
@@ -253,7 +268,7 @@ for a minimal chatting experimentation by setting the below.
 * the baseUrl in settings ui
   * https://api.openai.com/v1 or similar
 
-* Wrt request body - gMe.chatRequestOptions
+* Wrt request body - gMe.apiRequestOptions
   * model (settings ui)
   * any additional fields if required in future
 

examples/server/public_simplechat/simplechat.js

@@ -222,8 +222,8 @@ class SimpleChat {
      * @param {Object} obj
      */
     request_jsonstr_extend(obj) {
-        for(let k in gMe.chatRequestOptions) {
-            obj[k] = gMe.chatRequestOptions[k];
+        for(let k in gMe.apiRequestOptions) {
+            obj[k] = gMe.apiRequestOptions[k];
         }
         if (gMe.bStream) {
             obj["stream"] = true;
@@ -740,11 +740,12 @@ class Me {
             "Authorization": "", // Authorization: Bearer OPENAI_API_KEY
         }
         // Add needed fields wrt json object to be sent wrt LLM web services completions endpoint.
-        this.chatRequestOptions = {
+        this.apiRequestOptions = {
             "model": "gpt-3.5-turbo",
             "temperature": 0.7,
             "max_tokens": 1024,
             "n_predict": 1024,
+            "cache_prompt": false,
             //"frequency_penalty": 1.2,
             //"presence_penalty": 1.2,
         };
@@ -800,51 +801,55 @@ class Me {
 
             ui.el_create_append_p(`bStream:${this.bStream}`, elDiv);
 
+            ui.el_create_append_p(`bTrimGarbage:${this.bTrimGarbage}`, elDiv);
+
+            ui.el_create_append_p(`ApiEndPoint:${this.apiEP}`, elDiv);
+
+            ui.el_create_append_p(`iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`, elDiv);
+
             ui.el_create_append_p(`bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`, elDiv);
 
             ui.el_create_append_p(`bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`, elDiv);
 
-            ui.el_create_append_p(`bTrimGarbage:${this.bTrimGarbage}`, elDiv);
-
-            ui.el_create_append_p(`iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`, elDiv);
-
-            ui.el_create_append_p(`ApiEndPoint:${this.apiEP}`, elDiv);
-
         }
 
-        ui.el_create_append_p(`chatRequestOptions:${JSON.stringify(this.chatRequestOptions, null, " - ")}`, elDiv);
+        ui.el_create_append_p(`apiRequestOptions:${JSON.stringify(this.apiRequestOptions, null, " - ")}`, elDiv);
         ui.el_create_append_p(`headers:${JSON.stringify(this.headers, null, " - ")}`, elDiv);
 
     }
 
     /**
-     * Auto create ui input elements for fields in ChatRequestOptions
+     * Auto create ui input elements for fields in apiRequestOptions
      * Currently supports text and number field types.
      * @param {HTMLDivElement} elDiv
      */
-    show_settings_chatrequestoptions(elDiv) {
+    show_settings_apirequestoptions(elDiv) {
         let typeDict = {
             "string": "text",
             "number": "number",
         };
         let fs = document.createElement("fieldset");
         let legend = document.createElement("legend");
-        legend.innerText = "ChatRequestOptions";
+        legend.innerText = "ApiRequestOptions";
         fs.appendChild(legend);
         elDiv.appendChild(fs);
-        for(const k in this.chatRequestOptions) {
-            let val = this.chatRequestOptions[k];
+        for(const k in this.apiRequestOptions) {
+            let val = this.apiRequestOptions[k];
             let type = typeof(val);
-            if (!((type == "string") || (type == "number"))) {
-                continue;
+            if (((type == "string") || (type == "number"))) {
+                let inp = ui.el_creatediv_input(`Set${k}`, k, typeDict[type], this.apiRequestOptions[k], (val)=>{
+                    if (type == "number") {
+                        val = Number(val);
+                    }
+                    this.apiRequestOptions[k] = val;
+                });
+                fs.appendChild(inp.div);
+            } else if (type == "boolean") {
+                let bbtn = ui.el_creatediv_boolbutton(`Set{k}`, k, {true: "true", false: "false"}, val, (userVal)=>{
+                    this.apiRequestOptions[k] = userVal;
+                });
+                fs.appendChild(bbtn.div);
             }
-            let inp = ui.el_creatediv_input(`Set${k}`, k, typeDict[type], this.chatRequestOptions[k], (val)=>{
-                if (type == "number") {
-                    val = Number(val);
-                }
-                this.chatRequestOptions[k] = val;
-            });
-            fs.appendChild(inp.div);
         }
     }
 
@@ -870,6 +875,23 @@ class Me {
         });
         elDiv.appendChild(bb.div);
 
+        bb = ui.el_creatediv_boolbutton("SetTrimGarbage", "TrimGarbage", {true: "[+] yes trim", false: "[-] dont trim"}, this.bTrimGarbage, (val)=>{
+            this.bTrimGarbage = val;
+        });
+        elDiv.appendChild(bb.div);
+
+        this.show_settings_apirequestoptions(elDiv);
+
+        let sel = ui.el_creatediv_select("SetApiEP", "ApiEndPoint", ApiEP.Type, this.apiEP, (val)=>{
+            this.apiEP = ApiEP.Type[val];
+        });
+        elDiv.appendChild(sel.div);
+
+        sel = ui.el_creatediv_select("SetChatHistoryInCtxt", "ChatHistoryInCtxt", this.sRecentUserMsgCnt, this.iRecentUserMsgCnt, (val)=>{
+            this.iRecentUserMsgCnt = this.sRecentUserMsgCnt[val];
+        });
+        elDiv.appendChild(sel.div);
+
         bb = ui.el_creatediv_boolbutton("SetCompletionFreshChatAlways", "CompletionFreshChatAlways", {true: "[+] yes fresh", false: "[-] no, with history"}, this.bCompletionFreshChatAlways, (val)=>{
             this.bCompletionFreshChatAlways = val;
         });
@@ -880,23 +902,6 @@ class Me {
         });
         elDiv.appendChild(bb.div);
 
-        bb = ui.el_creatediv_boolbutton("SetTrimGarbage", "TrimGarbage", {true: "[+] yes trim", false: "[-] dont trim"}, this.bTrimGarbage, (val)=>{
-            this.bTrimGarbage = val;
-        });
-        elDiv.appendChild(bb.div);
-
-        let sel = ui.el_creatediv_select("SetChatHistoryInCtxt", "ChatHistoryInCtxt", this.sRecentUserMsgCnt, this.iRecentUserMsgCnt, (val)=>{
-            this.iRecentUserMsgCnt = this.sRecentUserMsgCnt[val];
-        });
-        elDiv.appendChild(sel.div);
-
-        sel = ui.el_creatediv_select("SetApiEP", "ApiEndPoint", ApiEP.Type, this.apiEP, (val)=>{
-            this.apiEP = ApiEP.Type[val];
-        });
-        elDiv.appendChild(sel.div);
-
-        this.show_settings_chatrequestoptions(elDiv);
-
     }
 
 }

examples/server/server.cpp

@@ -2607,17 +2607,9 @@ int main(int argc, char ** argv) {
 
     // print sample chat example to make it clear which template is used
     {
-        json chat;
-        chat.push_back({{"role", "system"},    {"content", "You are a helpful assistant"}});
-        chat.push_back({{"role", "user"},      {"content", "Hello"}});
-        chat.push_back({{"role", "assistant"}, {"content", "Hi there"}});
-        chat.push_back({{"role", "user"},      {"content", "How are you?"}});
-
-        const std::string chat_example = format_chat(ctx_server.model, params.chat_template, chat);
-
         LOG_INFO("chat template", {
-            {"chat_example", chat_example},
-            {"built_in", params.chat_template.empty()},
+            {"chat_example", llama_chat_format_example(ctx_server.model, params.chat_template)},
+            {"built_in",     params.chat_template.empty()},
         });
     }
 

examples/server/tests/features/server.feature

@@ -82,7 +82,7 @@ Feature: llama.cpp server
 
     Examples: Prompts
       | response_format                                                     | n_predicted | re_content             |
-      | {"type": "json_object", "schema": {"const": "42"}}                  | 5           | "42"                   |
+      | {"type": "json_object", "schema": {"const": "42"}}                  | 6           | "42"                   |
       | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10          | \[ -300 \]             |
       | {"type": "json_object"}                                             | 10          | \{ " Jacky.            |
 

examples/server/utils.hpp

@@ -118,36 +118,17 @@ static inline void server_log(const char * level, const char * function, int lin
 
 // Format given chat. If tmpl is empty, we take the template from model metadata
 inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
-    size_t alloc_size = 0;
-    // vector holding all allocated string to be passed to llama_chat_apply_template
-    std::vector<std::string> str(messages.size() * 2);
-    std::vector<llama_chat_message> chat(messages.size());
+    std::vector<llama_chat_msg> chat;
 
     for (size_t i = 0; i < messages.size(); ++i) {
         const auto & curr_msg = messages[i];
-        str[i*2 + 0]    = json_value(curr_msg, "role",    std::string(""));
-        str[i*2 + 1]    = json_value(curr_msg, "content", std::string(""));
-        alloc_size     += str[i*2 + 1].length();
-        chat[i].role    = str[i*2 + 0].c_str();
-        chat[i].content = str[i*2 + 1].c_str();
+        std::string role    = json_value(curr_msg, "role",    std::string(""));
+        std::string content = json_value(curr_msg, "content", std::string(""));
+        chat.push_back({role, content});
     }
 
-    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
-    std::vector<char> buf(alloc_size * 2);
-
-    // run the first time to get the total output length
-    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
-
-    // if it turns out that our buffer is too small, we resize it
-    if ((size_t) res > buf.size()) {
-        buf.resize(res);
-        res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
-    }
-
-    const std::string formatted_chat(buf.data(), res);
-
+    auto formatted_chat = llama_chat_apply_template(model, tmpl, chat, true);
     LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
-
     return formatted_chat;
 }