mirror of
https://github.com/geoffsee/predict-otron-9001.git
synced 2025-09-08 22:46:44 +00:00
398 lines
12 KiB
Rust
398 lines
12 KiB
Rust
#[cfg(feature = "accelerate")]
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extern crate accelerate_src;
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#[cfg(feature = "mkl")]
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extern crate intel_mkl_src;
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use anyhow::{Error as E, Result};
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use candle_transformers::models::gemma::{Config as Config1, Model as Model1};
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use candle_transformers::models::gemma2::{Config as Config2, Model as Model2};
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use candle_transformers::models::gemma3::{Config as Config3, Model as Model3};
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use clap::ValueEnum;
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// Removed gemma_cli import as it's not needed for the API
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use candle_core::{utils, DType, Device, Tensor};
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use candle_examples::token_output_stream::TokenOutputStream;
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use candle_nn::VarBuilder;
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use candle_transformers::generation::LogitsProcessor;
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use hf_hub::{api::sync::Api, Repo, RepoType};
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use std::io::Write;
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use tokenizers::Tokenizer;
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use std::sync::mpsc::{self, Receiver, Sender};
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use std::thread;
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#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
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pub enum WhichModel {
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#[value(name = "gemma-2b")]
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Base2B,
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#[value(name = "gemma-7b")]
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Base7B,
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#[value(name = "gemma-2b-it")]
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Instruct2B,
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#[value(name = "gemma-7b-it")]
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Instruct7B,
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#[value(name = "gemma-1.1-2b-it")]
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InstructV1_1_2B,
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#[value(name = "gemma-1.1-7b-it")]
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InstructV1_1_7B,
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#[value(name = "codegemma-2b")]
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CodeBase2B,
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#[value(name = "codegemma-7b")]
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CodeBase7B,
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#[value(name = "codegemma-2b-it")]
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CodeInstruct2B,
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#[value(name = "codegemma-7b-it")]
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CodeInstruct7B,
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#[value(name = "gemma-2-2b")]
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BaseV2_2B,
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#[value(name = "gemma-2-2b-it")]
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InstructV2_2B,
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#[value(name = "gemma-2-9b")]
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BaseV2_9B,
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#[value(name = "gemma-2-9b-it")]
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InstructV2_9B,
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#[value(name = "gemma-3-1b")]
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BaseV3_1B,
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#[value(name = "gemma-3-1b-it")]
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InstructV3_1B,
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}
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enum Model {
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V1(Model1),
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V2(Model2),
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V3(Model3),
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}
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impl Model {
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fn forward(&mut self, input_ids: &Tensor, pos: usize) -> candle_core::Result<Tensor> {
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match self {
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Self::V1(m) => m.forward(input_ids, pos),
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Self::V2(m) => m.forward(input_ids, pos),
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Self::V3(m) => m.forward(input_ids, pos),
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}
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}
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}
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pub struct TextGeneration {
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model: Model,
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device: Device,
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tokenizer: TokenOutputStream,
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logits_processor: LogitsProcessor,
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repeat_penalty: f32,
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repeat_last_n: usize,
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}
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fn device(cpu: bool) -> Result<Device> {
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if cpu {
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Ok(Device::Cpu)
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} else if utils::cuda_is_available() {
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Ok(Device::new_cuda(0)?)
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} else if utils::metal_is_available() {
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Ok(Device::new_metal(0)?)
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} else {
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Ok(Device::Cpu)
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}
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}
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impl TextGeneration {
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#[allow(clippy::too_many_arguments)]
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fn new(
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model: Model,
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tokenizer: Tokenizer,
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seed: u64,
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temp: Option<f64>,
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top_p: Option<f64>,
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repeat_penalty: f32,
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repeat_last_n: usize,
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device: &Device,
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) -> Self {
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let logits_processor = LogitsProcessor::new(seed, temp, top_p);
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Self {
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model,
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tokenizer: TokenOutputStream::new(tokenizer),
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logits_processor,
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repeat_penalty,
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repeat_last_n,
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device: device.clone(),
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}
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}
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/// Stream-only generation: sends freshly generated token strings over `tx`.
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/// (Does not send the prompt tokens; only newly generated model tokens.)
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fn run_stream(
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&mut self,
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prompt: &str,
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sample_len: usize,
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tx: Sender<Result<String>>,
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) -> Result<()> {
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self.tokenizer.clear();
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// Encode prompt (context only; do not emit prompt tokens to the stream).
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let mut tokens = self
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.tokenizer
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.tokenizer()
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.encode(prompt, true)
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.map_err(E::msg)?
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.get_ids()
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.to_vec();
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// Warm the tokenizer's internal state with prompt tokens (so merges are correct),
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// but do not send them to the receiver.
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for &t in tokens.iter() {
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let _ = self.tokenizer.next_token(t)?;
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}
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// Make sure stdout isn't holding anything (if caller also prints).
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std::io::stdout().flush()?;
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let mut generated_tokens = 0usize;
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let eos_token = match self.tokenizer.get_token("<eos>") {
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Some(token) => token,
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None => anyhow::bail!("cannot find the <eos> token"),
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};
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let eot_token = match self.tokenizer.get_token("<end_of_turn>") {
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Some(token) => token,
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None => {
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eprintln!("Warning: <end_of_turn> token not found, using <eos> as backup");
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eos_token
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}
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};
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let start_gen = std::time::Instant::now();
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for index in 0..sample_len {
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let context_size = if index > 0 { 1 } else { tokens.len() };
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let start_pos = tokens.len().saturating_sub(context_size);
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let ctxt = &tokens[start_pos..];
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let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
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let logits = self.model.forward(&input, start_pos)?;
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let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
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let logits = if self.repeat_penalty == 1. {
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logits
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} else {
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let start_at = tokens.len().saturating_sub(self.repeat_last_n);
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candle_transformers::utils::apply_repeat_penalty(
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&logits,
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self.repeat_penalty,
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&tokens[start_at..],
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)?
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};
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let next_token = self.logits_processor.sample(&logits)?;
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tokens.push(next_token);
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generated_tokens += 1;
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if next_token == eos_token || next_token == eot_token {
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break;
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}
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if let Some(t) = self.tokenizer.next_token(next_token)? {
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// Best-effort send; ignore if receiver dropped.
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let _ = tx.send(Ok(t));
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}
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}
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let _dt = start_gen.elapsed();
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// Flush any remaining buffered bytes as one final chunk.
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if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
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let _ = tx.send(Ok(rest));
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}
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Ok(())
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}
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}
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#[derive(Debug, Clone)]
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pub struct GemmaInferenceConfig {
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pub tracing: bool,
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pub prompt: String,
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pub model: WhichModel,
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pub cpu: bool,
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pub dtype: Option<String>,
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pub model_id: Option<String>,
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pub revision: String,
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pub use_flash_attn: bool,
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pub seed: u64,
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pub temperature: f64,
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pub top_p: Option<f64>,
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pub repeat_penalty: f32,
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pub repeat_last_n: usize,
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pub max_tokens: usize,
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}
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impl Default for GemmaInferenceConfig {
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fn default() -> Self {
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Self {
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tracing: false,
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prompt: "Hello".to_string(),
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model: WhichModel::InstructV2_2B,
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cpu: false,
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dtype: None,
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model_id: None,
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revision: "main".to_string(),
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use_flash_attn: false,
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seed: 299792458,
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temperature: 0.8,
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top_p: None,
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repeat_penalty: 1.1,
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repeat_last_n: 128,
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max_tokens: 100,
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}
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}
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}
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// Removed From<Args> implementation as Args is not available and not needed for API usage
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/// Builds the model and returns a channel that streams generated token strings.
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/// If model setup fails, the `Result` is returned immediately.
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pub fn run_gemma_api(cfg: GemmaInferenceConfig) -> Result<Receiver<Result<String>>> {
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use tracing_chrome::ChromeLayerBuilder;
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use tracing_subscriber::prelude::*;
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let _guard = if cfg.tracing {
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let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
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tracing_subscriber::registry().with(chrome_layer).init();
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Some(guard)
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} else {
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None
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};
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println!(
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"avx: {}, neon: {}, simd128: {}, f16c: {}",
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utils::with_avx(),
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utils::with_neon(),
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utils::with_simd128(),
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utils::with_f16c()
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);
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let device = device(cfg.cpu)?;
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println!("Device: {:?}", device);
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let dtype = match cfg.dtype.as_deref() {
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Some("f16") => DType::F16,
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Some("bf16") => DType::BF16,
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Some("f32") => DType::F32,
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Some(dtype) => anyhow::bail!("Unsupported dtype {dtype}"),
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None => {
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if device.is_cuda() {
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DType::BF16
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} else {
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DType::F16
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}
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}
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};
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println!("Using dtype: {:?}", dtype);
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let start = std::time::Instant::now();
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let api = Api::new()?;
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let model_id = cfg.model_id.unwrap_or_else(|| {
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match cfg.model {
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WhichModel::Base2B => "google/gemma-2b",
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WhichModel::Base7B => "google/gemma-7b",
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WhichModel::Instruct2B => "google/gemma-2b-it",
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WhichModel::Instruct7B => "google/gemma-7b-it",
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WhichModel::InstructV1_1_2B => "google/gemma-1.1-2b-it",
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WhichModel::InstructV1_1_7B => "google/gemma-1.1-7b-it",
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WhichModel::CodeBase2B => "google/codegemma-2b",
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WhichModel::CodeBase7B => "google/codegemma-7b",
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WhichModel::CodeInstruct2B => "google/codegemma-2b-it",
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WhichModel::CodeInstruct7B => "google/codegemma-7b-it",
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WhichModel::BaseV2_2B => "google/gemma-2-2b",
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WhichModel::InstructV2_2B => "google/gemma-2-2b-it",
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WhichModel::BaseV2_9B => "google/gemma-2-9b",
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WhichModel::InstructV2_9B => "google/gemma-2-9b-it",
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WhichModel::BaseV3_1B => "google/gemma-3-1b-pt",
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WhichModel::InstructV3_1B => "google/gemma-3-1b-it",
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}
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.to_string()
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});
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println!("Loading model: {}", &model_id);
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let repo = api.repo(Repo::with_revision(model_id, RepoType::Model, cfg.revision));
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let tokenizer_filename = repo.get("tokenizer.json")?;
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let config_filename = repo.get("config.json")?;
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let filenames = match cfg.model {
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WhichModel::BaseV3_1B | WhichModel::InstructV3_1B => vec![repo.get("model.safetensors")?],
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_ => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?,
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};
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println!("Retrieved files in {:?}", start.elapsed());
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let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
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let start = std::time::Instant::now();
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let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
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let model: Model = match cfg.model {
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WhichModel::Base2B
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| WhichModel::Base7B
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| WhichModel::Instruct2B
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| WhichModel::Instruct7B
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| WhichModel::InstructV1_1_2B
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| WhichModel::InstructV1_1_7B
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| WhichModel::CodeBase2B
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| WhichModel::CodeBase7B
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| WhichModel::CodeInstruct2B
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| WhichModel::CodeInstruct7B => {
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let config: Config1 = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
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let model = Model1::new(cfg.use_flash_attn, &config, vb)?;
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Model::V1(model)
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}
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WhichModel::BaseV2_2B
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| WhichModel::InstructV2_2B
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| WhichModel::BaseV2_9B
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| WhichModel::InstructV2_9B => {
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let config: Config2 = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
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let model = Model2::new(cfg.use_flash_attn, &config, vb)?;
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Model::V2(model)
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}
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WhichModel::BaseV3_1B | WhichModel::InstructV3_1B => {
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let config: Config3 = serde_json::from_reader(std::fs::File::open(config_filename)?)?;
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let model = Model3::new(cfg.use_flash_attn, &config, vb)?;
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Model::V3(model)
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}
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};
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println!("Loaded model in {:?}", start.elapsed());
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let mut pipeline = TextGeneration::new(
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model,
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tokenizer,
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cfg.seed,
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cfg.temperature.into(),
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cfg.top_p,
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cfg.repeat_penalty,
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cfg.repeat_last_n,
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&device,
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);
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let prompt = match cfg.model {
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WhichModel::InstructV3_1B => {
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format!(
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"<start_of_turn>user\n{}<end_of_turn>\n<start_of_turn>model\n",
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cfg.prompt
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)
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}
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_ => cfg.prompt,
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};
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println!("Starting inference...");
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// Create the channel after successful setup.
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let (tx, rx) = mpsc::channel::<Result<String>>();
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// Spawn generation thread; send tokens to the channel.
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thread::spawn(move || {
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// If generation fails, forward the error once.
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if let Err(e) = pipeline.run_stream(&prompt, cfg.max_tokens, tx.clone()) {
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let _ = tx.send(Err(e));
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}
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// Channel closes when tx is dropped.
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});
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Ok(rx)
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}
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