Merge branch 'main' into s6/mikupad

common/common.cpp

@@ -15,9 +15,11 @@
 #include "json.hpp"
 #include "json-schema-to-grammar.h"
 #include "llama.h"
+#include "chat-template.hpp"
 
 #include <algorithm>
 #include <cinttypes>
+#include <climits>
 #include <cmath>
 #include <codecvt>
 #include <cstdarg>
@@ -199,6 +201,16 @@ int32_t cpu_get_num_math() {
     return cpu_get_num_physical_cores();
 }
 
+
+static std::string read_file(const std::string& fname) {
+    std::ifstream file(fname);
+    if (!file) {
+        throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
+    }
+    std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+    file.close();
+    return content;
+}
 //
 // CLI argument parsing
 //
@@ -278,6 +290,13 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
         params.tensor_buft_overrides.push_back({nullptr, nullptr});
     }
 
+    if (!params.chat_template.empty() && !llama_chat_verify_template(nullptr, params.chat_template, params.use_jinja)) {
+        throw std::runtime_error(string_format(
+            "error: the supplied chat template is not supported: %s%s\n",
+            params.chat_template.c_str(),
+            params.use_jinja ? "" : "\nnote: llama.cpp was started without --jinja, we only support commonly used templates"
+        ));
+    }
     return true;
 }
 
@@ -1080,6 +1099,24 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         }
         return true;
     }
+    if (arg == "--cpu-moe" || arg == "-cmoe") {
+        params.tensor_buft_overrides.push_back({strdup("\\.ffn_(up|down|gate)_exps"), ggml_backend_cpu_buffer_type()});
+        return true;
+    }
+    if (arg == "--n-cpu-moe" || arg == "-ncmoe") {
+        CHECK_ARG
+        int32_t n_layers = std::stoi(argv[i]);
+        if (n_layers < 0) {
+            fprintf(stderr, "error: Invalid value for --n-cpu-moe: %d (must be >= 0)\n", n_layers);
+            invalid_param = true;
+            return true;
+        }
+        for (int32_t l = 0; l < n_layers; ++l) {
+            std::string pattern = "blk\\." + std::to_string(l) + "\\.(ffn_(up|down|gate)_exps)";
+            params.tensor_buft_overrides.push_back({strdup(pattern.c_str()), ggml_backend_cpu_buffer_type()});
+        }
+        return true;
+    }
     if (arg == "--no-mmap") {
         params.use_mmap = false;
         return true;
@@ -1417,7 +1454,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
     }
     if (arg == "--chat-template") {
         CHECK_ARG
-        if (!llama_chat_verify_template(argv[i])) {
+        if (!llama_chat_verify_template(nullptr, argv[i], false)) {
             fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
             fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
             invalid_param = true;
@@ -1426,6 +1463,22 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.chat_template = argv[i];
         return true;
     }
+    if (arg == "--chat-template-file") {
+        CHECK_ARG
+        std::string chat_template = read_file(std::string(argv[i]));
+        if (!llama_chat_verify_template(nullptr, chat_template, false)) {
+            fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]);
+            fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n");
+            invalid_param = true;
+            return true;
+        }
+        params.chat_template = chat_template;
+        return true;
+    }
+    if (arg == "--jinja") {
+        params.use_jinja = true;
+        return true;
+    }
     if (arg == "--slot-prompt-similarity" || arg == "-sps") {
         CHECK_ARG
         params.slot_prompt_similarity = std::stof(argv[i]);
@@ -1804,6 +1857,8 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param
         options.push_back({ "*",           "       --no-mmap",              "do not memory-map model (slower load but may reduce pageouts if not using mlock)" });
     }
     options.push_back({ "*",           "       --run-time-repack",      "repack tensors if interleaved variant is available"});
+    options.push_back({ "*",           "       --cpu-moe",              "keep all MoE weights in CPU memory"});
+    options.push_back({ "*",           "       --n-cpu-moe N",          "keep MoE weights of the first N layers in CPU memory"});
     options.push_back({ "*",           "       --numa TYPE",            "attempt optimizations that help on some NUMA systems\n"
                                                                         "  - distribute: spread execution evenly over all nodes\n"
                                                                         "  - isolate: only spawn threads on CPUs on the node that execution started on\n"
@@ -1974,6 +2029,22 @@ std::string gpt_params_get_system_info(const gpt_params & params) {
 // String utils
 //
 
+std::string string_format(const char* fmt, ...) {
+    va_list ap;
+    va_list ap2;
+    va_start(ap, fmt);
+    va_copy(ap2, ap);
+    int size = vsnprintf(NULL, 0, fmt, ap);
+    GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
+    std::vector<char> buf(size + 1);
+    int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+    GGML_ASSERT(size2 == size);
+    va_end(ap2);
+    va_end(ap);
+    return std::string(buf.data(), size);
+}
+
+
 std::vector<std::string> string_split(std::string input, char separator) {
     std::vector<std::string> parts;
     size_t separator_pos = input.find(separator);
@@ -1987,6 +2058,21 @@ std::vector<std::string> string_split(std::string input, char separator) {
     return parts;
 }
 
+std::string string_join(const std::vector<std::string> & strs, const std::string & delimiter) {
+    if (strs.empty()) {
+        return "";
+    }
+    
+    std::ostringstream oss;
+    for (size_t i = 0; i < strs.size(); ++i) {
+        if (i > 0) {
+            oss << delimiter;
+        }
+        oss << strs[i];
+    }
+    return oss.str();
+}
+
 std::string string_strip(const std::string & str) {
     size_t start = 0;
     size_t end = str.size();
@@ -2960,6 +3046,22 @@ std::string llama_token_to_piece(const struct llama_context * ctx, llama_token t
     return piece;
 }
 
+std::string llama_token_to_piece(const struct llama_model* model, llama_token token, bool special) {
+    std::string piece;
+    piece.resize(piece.capacity());  // using string internal cache, 15 bytes + '\n'
+    const int n_chars = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
+    if (n_chars < 0) {
+        piece.resize(-n_chars);
+        int check = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
+        GGML_ASSERT(check == -n_chars);
+    }
+    else {
+        piece.resize(n_chars);
+    }
+
+    return piece;
+}
+
 std::string llama_detokenize(llama_context * ctx, const std::vector<llama_token> & tokens, bool special) {
     std::string text;
     text.resize(std::max(text.capacity(), tokens.size()));
@@ -2986,50 +3088,60 @@ bool llama_should_add_bos_token(const llama_model * model) {
 // Chat template utils
 //
 
-bool llama_chat_verify_template(const std::string & tmpl) {
+bool llama_chat_verify_template(const struct llama_model* model, const std::string& tmpl, bool use_jinja) {
+    if (use_jinja) {
+        try {
+            auto chat_template = minja::chat_template(tmpl, "<s>", "</s>");
+            chat_template.apply({ {
+                {"role", "user"},
+                {"content", "test"},
+            } }, json(), true);
+            return true;
+        }
+        catch (const std::exception& e) {
+            fprintf(stdout,"%s: failed to apply template: %s\n", __func__, e.what());
+            return false;
+        }
+    }
     llama_chat_message chat[] = {{"user", "test"}};
-    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
+    const int res = llama_chat_apply_template(model, tmpl.c_str(), chat, 1, true, nullptr, 0);
     return res >= 0;
 }
 
 std::string llama_chat_apply_template(const struct llama_model * model,
-        const std::string & tmpl,
+    const common_chat_template& tmpl,
         const std::vector<llama_chat_msg> & msgs,
-        bool add_ass) {
+    bool add_ass,
+    bool use_jinja) {
+    if (use_jinja) {
+        auto messages = json::array();
+        for (const auto& msg : msgs) {
+            messages.push_back({ {"role", msg.role}, {"content", msg.content} });
+        }
+        return tmpl.apply(messages, /* tools= */ json(), add_ass);
+    }
     int alloc_size = 0;
-    bool fallback = false; // indicate if we must fallback to default chatml
     std::vector<llama_chat_message> chat;
     for (auto & msg : msgs) {
         chat.push_back({msg.role.c_str(), msg.content.c_str()});
         alloc_size += (msg.role.size() + msg.content.size()) * 1.25;
     }
 
-    const char * ptr_tmpl = tmpl.empty() ? nullptr : tmpl.c_str();
     std::vector<char> buf(alloc_size);
 
     // run the first time to get the total output length
-    int32_t res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), add_ass, buf.data(), buf.size());
-
+    int32_t res = llama_chat_apply_template(model, tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size());
     // error: chat template is not supported
     if (res < 0) {
-        if (ptr_tmpl != nullptr) {
             // if the custom "tmpl" is not supported, we throw an error
             // this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
             throw std::runtime_error("this custom template is not supported");
-        } else {
-            // If the built-in template is not supported, we default to chatml
-            res = llama_chat_apply_template(nullptr, "chatml", chat.data(), chat.size(), add_ass, buf.data(), buf.size());
-            fallback = true;
         }
-    }
 
     // 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(
-            fallback ? nullptr : model,
-            fallback ? "chatml" : ptr_tmpl,
-            chat.data(), chat.size(), add_ass, buf.data(), buf.size());
+        res = llama_chat_apply_template(model, tmpl.source().c_str(), chat.data(), chat.size(), add_ass, buf.data(), buf.size());
     }
 
     std::string formatted_chat(buf.data(), res);
@@ -3037,12 +3149,13 @@ std::string llama_chat_apply_template(const struct llama_model * model,
 }
 
 std::string llama_chat_format_single(const struct llama_model * model,
-        const std::string & tmpl,
+    const common_chat_template& tmpl,
         const std::vector<llama_chat_msg> & past_msg,
         const llama_chat_msg & new_msg,
-        bool add_ass) {
+    bool add_ass,
+    bool use_jinja) {
     std::ostringstream ss;
-    auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false);
+    auto fmt_past_msg = past_msg.empty() ? "" : llama_chat_apply_template(model, tmpl, past_msg, false, use_jinja);
     std::vector<llama_chat_msg> chat_new(past_msg);
     // if the past_msg ends with a newline, we must preserve it in the formatted version
     if (add_ass && !fmt_past_msg.empty() && fmt_past_msg.back() == '\n') {
@@ -3050,21 +3163,77 @@ std::string llama_chat_format_single(const struct llama_model * model,
     };
     // format chat with new_msg
     chat_new.push_back(new_msg);
-    auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass);
+    auto fmt_new_msg = llama_chat_apply_template(model, tmpl, chat_new, add_ass, use_jinja);
     // get the diff part
     ss << fmt_new_msg.substr(fmt_past_msg.size(), fmt_new_msg.size() - fmt_past_msg.size());
     return ss.str();
 }
 
-std::string llama_chat_format_example(const struct llama_model * model,
-        const std::string & tmpl) {
+std::string llama_chat_format_example(const struct llama_model * model, const common_chat_template& tmpl, bool use_jinja) {
     std::vector<llama_chat_msg> msgs = {
         {"system",    "You are a helpful assistant"},
         {"user",      "Hello"},
         {"assistant", "Hi there"},
         {"user",      "How are you?"},
     };
-    return llama_chat_apply_template(model, tmpl, msgs, true);
+    return llama_chat_apply_template(model, tmpl, msgs, true, use_jinja);
+}
+
+
+common_chat_templates llama_chat_templates_from_model(const struct llama_model* model, const std::string& chat_template_override)
+{
+    auto vocab = llama_model_get_vocab(model);
+    std::string default_template_src = chat_template_override;
+    std::string template_tool_use_src = chat_template_override;
+    bool has_explicit_template = !chat_template_override.empty();
+    if (chat_template_override.empty()) {
+        auto str = llama_model_chat_template(model, /* name */ nullptr);
+        if (str) {
+            default_template_src = str;
+            has_explicit_template = true;
+        }
+        str = llama_model_chat_template(model, /* name */ "tool_use");
+        if (str) {
+            template_tool_use_src = str;
+            has_explicit_template = true;
+        }
+    }
+    if (default_template_src.empty() || default_template_src == "chatml") {
+        if (!template_tool_use_src.empty()) {
+            default_template_src = template_tool_use_src;
+        }
+        else {
+            default_template_src = R"(
+                {%- for message in messages -%}
+                    {{- "<|im_start|>" + message.role + "\n" + message.content + "<|im_end|>\n" -}}
+                {%- endfor -%}
+                {%- if add_generation_prompt -%}
+                    {{- "<|im_start|>assistant\n" -}}
+                {%- endif -%}
+            )";
+        }
+    }
+    const auto get_token = [&](llama_token token, const char* name, const char* jinja_variable_name) {
+        if (token == LLAMA_TOKEN_NULL) {
+            if (default_template_src.find(jinja_variable_name) != std::string::npos
+                || template_tool_use_src.find(jinja_variable_name) != std::string::npos) {
+                fprintf(stdout, "%s: warning: vocab does not have a %s token, jinja template won't work as intended.\n", __func__, name);
+            }
+            return std::string();
+        }
+        else {
+            return llama_token_to_piece(model, token, true);
+        }
+    };
+    auto token_bos = get_token(llama_token_bos(model), "BOS", "bos_token");
+    auto token_eos = get_token(llama_token_eos(model), "EOS", "eos_token");
+    return {
+        has_explicit_template,
+        std::make_unique<minja::chat_template>(default_template_src, token_bos, token_eos),
+        template_tool_use_src.empty()
+            ? nullptr
+            : std::make_unique<minja::chat_template>(template_tool_use_src, token_bos, token_eos)
+    };
 }
 
 //
@@ -3554,3 +3723,27 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
     fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false");
 }
+
+// Additional string utilities for builder pattern compatibility
+bool string_starts_with(const std::string & str, const std::string & prefix) {
+    return str.rfind(prefix, 0) == 0;
+}
+
+bool string_ends_with(const std::string_view & str, const std::string_view & suffix) {
+    return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
+}
+
+size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop) {
+    if (!str.empty() && !stop.empty()) {
+        const char text_last_char = str.back();
+        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
+            if (stop[char_index] == text_last_char) {
+                const auto current_partial = stop.substr(0, char_index + 1);
+                if (string_ends_with(str, current_partial)) {
+                    return str.size() - char_index - 1;
+                }
+            }
+        }
+    }
+    return std::string::npos;
+}