use crate::models::common::generate::{GenerationDataProvider, PrepareData};
use crate::models::llama::LlamaForCausalLM;
use crate::models::minicpm5::config::MiniCPM5Config;
use anyhow::Result;
use candle_core::{DType, Device};
use candle_nn::VarBuilder;
use crate::utils::{find_type_files, get_device, get_dtype};
use crate::{chat_template::ChatTemplate, tokenizer::TokenizerModel};
pub struct MiniCPM5GenerateModel<'a> {
chat_template: ChatTemplate<'a>,
tokenizer: TokenizerModel,
model: LlamaForCausalLM,
device: Device,
model_name: String,
}
impl<'a> MiniCPM5GenerateModel<'a> {
pub fn init(path: &str, device: Option<&Device>, dtype: Option<DType>) -> Result<Self> {
let chat_template = ChatTemplate::init(path)?;
let tokenizer = TokenizerModel::init(path)?;
let config_path = path.to_string() + "/config.json";
let cfg: MiniCPM5Config = serde_json::from_slice(&std::fs::read(config_path)?)?;
let device = &get_device(device);
let cfg_dtype = cfg.torch_dtype.as_str();
let dtype = get_dtype(dtype, cfg_dtype);
let model_list = find_type_files(path, "safetensors")?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_list, dtype, device)? };
let model = LlamaForCausalLM::new(
vb,
cfg.vocab_size,
cfg.hidden_size,
cfg.num_hidden_layers,
cfg.num_attention_heads,
Some(cfg.num_key_value_heads),
Some(cfg.head_dim),
false,
"self_attn",
Some("o_proj"),
cfg.intermediate_size,
cfg.hidden_act,
false,
"mlp",
cfg.rms_norm_eps,
"input_layernorm",
"post_attention_layernorm",
cfg.rope_theta,
cfg.eos_token_id.clone(),
)?;
let model_name = std::path::Path::new(path)
.file_name()
.and_then(|s| s.to_str())
.unwrap_or("minicpm5")
.to_string();
Ok(MiniCPM5GenerateModel {
chat_template,
tokenizer,
model,
device: device.clone(),
model_name,
})
}
}
impl<'a> GenerationDataProvider for MiniCPM5GenerateModel<'a> {
fn get_data(&self, mes: &crate::params::chat::ChatCompletionParameters) -> Result<PrepareData> {
let mes_render = self.chat_template.apply_chat_template(mes)?;
let in_reasoning = self.is_in_reasoning(&mes_render);
let input_ids = self.tokenizer.text_encode(mes_render, &self.device)?;
let multi_model_data = self.get_multi_model_data();
Ok(PrepareData {
in_reasoning,
input_ids,
multi_model_data,
})
}
}
crate::impl_generate_model!(MiniCPM5GenerateModel<'a>);