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chatglm_pybind.cpp
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chatglm_pybind.cpp
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#include "chatglm.h"
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
namespace chatglm {
namespace py = pybind11;
using namespace pybind11::literals;
class PyBaseTokenizer : public BaseTokenizer {
public:
using BaseTokenizer::BaseTokenizer;
std::vector<int> encode(const std::string &text, int max_length) const override {
PYBIND11_OVERRIDE_PURE(std::vector<int>, BaseTokenizer, encode, text, max_length);
}
std::string decode(const std::vector<int> &ids) const override {
PYBIND11_OVERLOAD_PURE(std::string, BaseTokenizer, decode, ids);
}
std::vector<int> encode_messages(const std::vector<ChatMessage> &history, int max_length) const override {
PYBIND11_OVERLOAD_PURE(std::vector<int>, BaseTokenizer, encode_messages, history, max_length);
}
};
class PyBaseModelForCausalLM : public BaseModelForCausalLM {
public:
using BaseModelForCausalLM::BaseModelForCausalLM;
void load(ModelLoader &loader) override { PYBIND11_OVERLOAD_PURE(void, PyBaseModelForCausalLM, load, loader); }
ggml_tensor *forward(ModelContext *ctx, ggml_tensor *input_ids, int n_past, int n_ctx,
bool is_decoding) const override {
PYBIND11_OVERLOAD_PURE(ggml_tensor *, PyBaseModelForCausalLM, forward, ctx, input_ids, n_past, n_ctx,
is_decoding)
}
};
template <typename T>
static inline std::string to_string(const T &obj) {
std::ostringstream oss;
oss << obj;
return oss.str();
}
PYBIND11_MODULE(_C, m) {
m.doc() = "ChatGLM.cpp python binding";
py::enum_<ModelType>(m, "ModelType")
.value("CHATGLM", ModelType::CHATGLM)
.value("CHATGLM2", ModelType::CHATGLM2)
.value("CHATGLM3", ModelType::CHATGLM3)
.value("BAICHUAN7B", ModelType::BAICHUAN7B)
.value("BAICHUAN13B", ModelType::BAICHUAN13B)
.value("INTERNLM", ModelType::INTERNLM);
py::class_<ModelConfig>(m, "ModelConfig")
.def_readonly("model_type", &ModelConfig::model_type)
// .def_readonly("dtype", &ModelConfig::dtype)
.def_readonly("vocab_size", &ModelConfig::vocab_size)
.def_readonly("hidden_size", &ModelConfig::hidden_size)
.def_readonly("num_attention_heads", &ModelConfig::num_attention_heads)
.def_readonly("num_kv_heads", &ModelConfig::num_kv_heads)
.def_readonly("num_hidden_layers", &ModelConfig::num_hidden_layers)
.def_readonly("intermediate_size", &ModelConfig::intermediate_size)
.def_readonly("norm_eps", &ModelConfig::norm_eps)
.def_readonly("max_length", &ModelConfig::max_length)
.def_readonly("bos_token_id", &ModelConfig::bos_token_id)
.def_readonly("eos_token_id", &ModelConfig::eos_token_id)
.def_readonly("pad_token_id", &ModelConfig::pad_token_id)
.def_readonly("sep_token_id", &ModelConfig::sep_token_id)
.def_readonly("extra_eos_token_ids", &ModelConfig::extra_eos_token_ids)
.def_property_readonly("model_type_name", &ModelConfig::model_type_name);
py::class_<GenerationConfig>(m, "GenerationConfig")
.def(py::init<int, int, int, bool, int, float, float, float, int>(), "max_length"_a = 2048,
"max_new_tokens"_a = -1, "max_context_length"_a = 512, "do_sample"_a = true, "top_k"_a = 0,
"top_p"_a = 0.7, "temperature"_a = 0.95, "repetition_penalty"_a = 1.0, "num_threads"_a = 0)
.def_readwrite("max_length", &GenerationConfig::max_length)
.def_readwrite("max_new_tokens", &GenerationConfig::max_new_tokens)
.def_readwrite("max_context_length", &GenerationConfig::max_context_length)
.def_readwrite("do_sample", &GenerationConfig::do_sample)
.def_readwrite("top_k", &GenerationConfig::top_k)
.def_readwrite("top_p", &GenerationConfig::top_p)
.def_readwrite("temperature", &GenerationConfig::temperature)
.def_readwrite("repetition_penalty", &GenerationConfig::repetition_penalty)
.def_readwrite("num_threads", &GenerationConfig::num_threads);
py::class_<FunctionMessage>(m, "FunctionMessage")
.def("__repr__", &to_string<FunctionMessage>)
.def("__str__", &to_string<FunctionMessage>)
.def_readwrite("name", &FunctionMessage::name)
.def_readwrite("arguments", &FunctionMessage::arguments);
py::class_<CodeMessage>(m, "CodeMessage")
.def("__repr__", &to_string<CodeMessage>)
.def("__str__", &to_string<CodeMessage>)
.def_readwrite("input", &CodeMessage::input);
py::class_<ToolCallMessage>(m, "ToolCallMessage")
.def("__repr__", &to_string<ToolCallMessage>)
.def("__str__", &to_string<ToolCallMessage>)
.def_readwrite("type", &ToolCallMessage::type)
.def_readwrite("function", &ToolCallMessage::function)
.def_readwrite("code", &ToolCallMessage::code);
py::class_<ChatMessage>(m, "ChatMessage")
.def(py::init<std::string, std::string, std::vector<ToolCallMessage>>(), "role"_a, "content"_a,
"tool_calls"_a = std::vector<ToolCallMessage>{})
.def("__repr__", &to_string<ChatMessage>)
.def("__str__", &to_string<ChatMessage>)
.def_readonly_static("ROLE_SYSTEM", &ChatMessage::ROLE_SYSTEM)
.def_readonly_static("ROLE_USER", &ChatMessage::ROLE_USER)
.def_readonly_static("ROLE_ASSISTANT", &ChatMessage::ROLE_ASSISTANT)
.def_readonly_static("ROLE_OBSERVATION", &ChatMessage::ROLE_OBSERVATION)
.def_readwrite("role", &ChatMessage::role)
.def_readwrite("content", &ChatMessage::content)
.def_readwrite("tool_calls", &ChatMessage::tool_calls);
py::class_<BaseTokenizer, PyBaseTokenizer>(m, "BaseTokenizer")
.def("encode", &BaseTokenizer::encode, "text"_a, "max_length"_a)
.def("decode", &BaseTokenizer::decode, "ids"_a)
.def("encode_messages", &BaseTokenizer::encode_messages, "messages"_a, "max_length"_a)
.def("decode_message", &BaseTokenizer::decode_message, "ids"_a);
py::class_<BaseModelForCausalLM, PyBaseModelForCausalLM>(m, "BaseModelForCausalLM")
.def("generate_next_token", &BaseModelForCausalLM::generate_next_token, "input_ids"_a, "gen_config"_a,
"n_past"_a, "n_ctx"_a)
.def_readonly("config", &BaseModelForCausalLM::config);
// ===== ChatGLM =====
py::class_<ChatGLMTokenizer, BaseTokenizer>(m, "ChatGLMTokenizer");
py::class_<ChatGLMForCausalLM, BaseModelForCausalLM>(m, "ChatGLMForCausalLM");
// ===== ChatGLM2 =====
py::class_<ChatGLM2Tokenizer, BaseTokenizer>(m, "ChatGLM2Tokenizer");
py::class_<ChatGLM2ForCausalLM, BaseModelForCausalLM>(m, "ChatGLM2ForCausalLM");
// ===== ChatGLM3 =====
py::class_<ChatGLM3Tokenizer, BaseTokenizer>(m, "ChatGLM3Tokenizer");
// ===== Baichuan7B/13B =====
py::class_<BaichuanTokenizer, BaseTokenizer>(m, "BaichuanTokenizer");
py::class_<Baichuan7BForCausalLM, BaseModelForCausalLM>(m, "Baichuan7BForCausalLM");
py::class_<Baichuan13BForCausalLM, BaseModelForCausalLM>(m, "Baichuan13BForCausalLM");
// ===== InternLM =====
py::class_<InternLMTokenizer, BaseTokenizer>(m, "InternLMTokenizer");
py::class_<InternLM7BForCausalLM, BaseModelForCausalLM>(m, "InternLM7BForCausalLM");
py::class_<InternLM20BForCausalLM, BaseModelForCausalLM>(m, "InternLM20BForCausalLM");
// ===== Pipeline ====
py::class_<Pipeline>(m, "Pipeline")
.def(py::init<const std::string &>(), "path"_a)
.def_property_readonly("model", [](const Pipeline &self) { return self.model.get(); })
.def_property_readonly("tokenizer", [](const Pipeline &self) { return self.tokenizer.get(); });
}
} // namespace chatglm