mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-09-17 20:31:47 +02:00
Aldehir Rojas
acecd56032
* common : implement common_schema types
* common : implement a json schema optimizer
* common : reduce optimizations
* common : refactor json-schema-to-grammar to use common_schema
* common : use common_trie
* common/schema : implement type/kind resolution
* cont : cleanup
* cont : remove common_chat_tool_parameters
* cont : simplify schema resolution
* cont : pass common_schema through the json-schema-to-grammar builder
* cont : cleanup
* cont : move enums under common_schema and add type enum
* cont : reduce test cases
* cont : clean up
* cont : clean up
* refactor : rename common_schema_parse to common_schema_from_json
* tests : fix gcc dangling-reference warning in test-json-schema
* tests : take the schema label as const char * to satisfy gcc dangling-reference
* refactor : rename common_schema_builder parse_* methods to build_*
* cont : fix may_be_string
* cont : properly handle empty tool parameters
* cont : add tests for empty $ref
* cont : remove dead code
* cont : update docs
* cont : make "{}" mean any object for json_object as well
* cont : restore (min|max)Length to imply string type
* cont : rename common_schema to common_chat_schema
77 lines
3.1 KiB
C++
77 lines
3.1 KiB
C++
#include "parsers.h"
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common_chat_params common_chat_params_init_gigachat_v3(
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const common_chat_template & tmpl,
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const autoparser::generation_params & inputs) {
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common_chat_params data;
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data.prompt = common_chat_template_direct_apply_impl(tmpl, inputs);
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data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
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data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
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data.supports_thinking = false;
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data.preserved_tokens = {
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"<|message_sep|>\n\n",
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"<|role_sep|>\n",
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};
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if (inputs.has_continuation()) {
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const auto & msg = inputs.continue_msg;
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data.generation_prompt = "assistant<|role_sep|>\n" + msg.render_content();
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data.prompt += data.generation_prompt;
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}
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auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
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auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
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const auto *tool_call_start_prefix = "<|message_sep|>\n\nfunction call<|role_sep|>\n";
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auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
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auto ret = p.eps();
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if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
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// Build a choice of all available tools
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auto tool_choice = p.choice();
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for (const auto & tool : inputs.tools) {
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const auto & function = tool.at("function");
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std::string name = function.at("name");
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const auto schema = common_chat_tool_parameters(function);
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auto tool_name = p.json_member("name", "\"" + p.tool_name(p.literal(name)) + "\"");
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auto tool_args = p.json_member("arguments", p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema)));
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auto tool_open = p.tool_open(p.literal("{") << tool_name);
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tool_choice |= p.rule("tool-" + name, tool_open << "," << tool_args << "}");
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}
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// Define the tool call structure
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auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
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auto max_calls = 1; // parallel toolcalls are not supported
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auto tool_call = p.rule("tool-call", p.literal(tool_call_start_prefix) + tool_choice);
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auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(tool_call, /* min = */ min_calls, /* max = */ max_calls));
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ret = p.content(p.until("<|message_sep|>\n\n")) << tool_calls;
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} else {
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// Content only parser
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include_grammar = false;
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ret = p.content(p.rest());
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}
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return p.literal("assistant<|role_sep|>\n") + ret;
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});
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data.parser = parser.save();
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if (include_grammar) {
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data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
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data.grammar = build_grammar([&](const common_grammar_builder & builder) {
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parser.build_grammar(builder, data.grammar_lazy);
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});
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data.grammar_triggers = {
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{COMMON_GRAMMAR_TRIGGER_TYPE_WORD, tool_call_start_prefix}
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};
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}
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return data;
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}
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