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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,128 @@ | ||
| use std::path::PathBuf; | ||
|
|
||
| use super::models::Models; | ||
| use anyhow::{Error as E, Result}; | ||
| use candle_core::quantized::{ggml_file, gguf_file}; | ||
| use candle_core::Device; | ||
| use candle_transformers::models::quantized_llama::ModelWeights; | ||
| use hf_hub::api::sync::{Api, ApiBuilder}; | ||
| use hf_hub::{Repo, RepoType}; | ||
| use log::{debug, info}; | ||
| use tokenizers::Tokenizer; | ||
|
|
||
| fn format_size(size_in_bytes: usize) -> String { | ||
| if size_in_bytes < 1_000 { | ||
| format!("{}B", size_in_bytes) | ||
| } else if size_in_bytes < 1_000_000 { | ||
| format!("{:.2}KB", size_in_bytes as f64 / 1e3) | ||
| } else if size_in_bytes < 1_000_000_000 { | ||
| format!("{:.2}MB", size_in_bytes as f64 / 1e6) | ||
| } else { | ||
| format!("{:.2}GB", size_in_bytes as f64 / 1e9) | ||
| } | ||
| } | ||
|
|
||
| pub fn create_model( | ||
| model: Models, | ||
| cache_dir: &Option<PathBuf>, | ||
| ) -> Result<(ModelWeights, Device), Box<dyn std::error::Error>> { | ||
| info!( | ||
| "avx: {}, neon: {}, simd128: {}, f16c: {}", | ||
| candle_core::utils::with_avx(), | ||
| candle_core::utils::with_neon(), | ||
| candle_core::utils::with_simd128(), | ||
| candle_core::utils::with_f16c() | ||
| ); | ||
| let revision = "main".to_string(); | ||
|
|
||
| let start = std::time::Instant::now(); | ||
| let api = match cache_dir { | ||
| Some(cache_dir) => ApiBuilder::default() | ||
| .with_cache_dir(cache_dir.clone()) | ||
| .build()?, | ||
| None => Api::new()?, | ||
| }; | ||
|
|
||
| let model_path = model.repo_path(); | ||
|
|
||
| debug!("model paths: {:?}", model_path); | ||
|
|
||
| let repo = api.repo(Repo::with_revision( | ||
| model_path.0.to_string(), | ||
| RepoType::Model, | ||
| revision, | ||
| )); | ||
|
|
||
| let model_path = &repo.get(model_path.1)?; | ||
| let mut file = std::fs::File::open(model_path)?; | ||
| info!("retrieved the model files in {:?}", start.elapsed()); | ||
|
|
||
| let model = match model_path.extension().and_then(|v| v.to_str()) { | ||
| Some("gguf") => { | ||
| let model = gguf_file::Content::read(&mut file)?; | ||
| let mut total_size_in_bytes = 0; | ||
| for (_, tensor) in model.tensor_infos.iter() { | ||
| let elem_count = tensor.shape.elem_count(); | ||
| total_size_in_bytes += | ||
| elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.blck_size(); | ||
| } | ||
| debug!( | ||
| "loaded {:?} tensors ({}) in {:.2}s", | ||
| model.tensor_infos.len(), | ||
| &format_size(total_size_in_bytes), | ||
| start.elapsed().as_secs_f32(), | ||
| ); | ||
| ModelWeights::from_gguf(model, &mut file)? | ||
| } | ||
| Some("ggml" | "bin") | Some(_) | None => { | ||
| let content = ggml_file::Content::read(&mut file)?; | ||
| let mut total_size_in_bytes = 0; | ||
| for (_, tensor) in content.tensors.iter() { | ||
| let elem_count = tensor.shape().elem_count(); | ||
| total_size_in_bytes += | ||
| elem_count * tensor.dtype().type_size() / tensor.dtype().blck_size(); | ||
| } | ||
| debug!( | ||
| "loaded {:?} tensors ({}) in {:.2}s", | ||
| content.tensors.len(), | ||
| &format_size(total_size_in_bytes), | ||
| start.elapsed().as_secs_f32(), | ||
| ); | ||
| debug!("params: {:?}", content.hparams); | ||
| let default_gqa = match model { | ||
| Models::L7b | ||
| | Models::L13b | ||
| | Models::L7bChat | ||
| | Models::L13bChat | ||
| | Models::L7bCode | ||
| | Models::L13bCode | ||
| | Models::L34bCode | ||
| | Models::Leo7b | ||
| | Models::Leo13b => 1, | ||
| Models::Mixtral | ||
| | Models::MixtralInstruct | ||
| | Models::Mistral7b | ||
| | Models::Mistral7bInstruct | ||
| | Models::Zephyr7bAlpha | ||
| | Models::Zephyr7bBeta | ||
| | Models::L70b | ||
| | Models::L70bChat | ||
| | Models::OpenChat35 | ||
| | Models::Starling7bAlpha => 8, | ||
| }; | ||
| ModelWeights::from_ggml(content, default_gqa)? | ||
| } | ||
| }; | ||
| Ok((model, Device::Cpu)) | ||
| } | ||
|
|
||
| pub fn create_tokenizer(model: Models) -> Result<Tokenizer, Box<dyn std::error::Error>> { | ||
| let tokenizer_path = { | ||
| let api = hf_hub::api::sync::Api::new()?; | ||
| let repo = model.tokenizer_repo(); | ||
| let api = api.model(repo.to_string()); | ||
| api.get("tokenizer.json")? | ||
| }; | ||
| let tokenizer = Tokenizer::from_file(tokenizer_path).map_err(E::msg)?; | ||
| Ok(tokenizer) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,160 +1,16 @@ | ||
| // source: https://github.com/huggingface/candle/blob/main/candle-examples/examples/mistral/main.rs | ||
| pub mod generate_parameter; | ||
| pub mod loader; | ||
| pub mod model_processor; | ||
| pub mod models; | ||
| pub mod sampler; | ||
| mod text_generation; | ||
| pub mod text_generation; | ||
| pub mod text_generator; | ||
| pub mod token_generator; | ||
|
|
||
| pub use text_generator::TextGeneratorTrait; | ||
|
|
||
| use std::path::PathBuf; | ||
|
|
||
| use anyhow::{Error as E, Result}; | ||
| use candle_core::quantized::{ggml_file, gguf_file}; | ||
| use candle_core::Device; | ||
| use candle_transformers::models::quantized_llama::ModelWeights; | ||
| use hf_hub::api::sync::{Api, ApiBuilder}; | ||
| use hf_hub::{Repo, RepoType}; | ||
| use log::{debug, info}; | ||
| pub use text_generation::TextGeneration; | ||
| use tokenizers::Tokenizer; | ||
|
|
||
| use self::models::Models; | ||
|
|
||
| #[derive(Debug, PartialEq)] | ||
| pub enum FinishReason { | ||
| Length, | ||
| EosToken, | ||
| StopSequence, | ||
| } | ||
|
|
||
| fn format_size(size_in_bytes: usize) -> String { | ||
| if size_in_bytes < 1_000 { | ||
| format!("{}B", size_in_bytes) | ||
| } else if size_in_bytes < 1_000_000 { | ||
| format!("{:.2}KB", size_in_bytes as f64 / 1e3) | ||
| } else if size_in_bytes < 1_000_000_000 { | ||
| format!("{:.2}MB", size_in_bytes as f64 / 1e6) | ||
| } else { | ||
| format!("{:.2}GB", size_in_bytes as f64 / 1e9) | ||
| } | ||
| } | ||
|
|
||
| pub fn create_model( | ||
| model: Models, | ||
| cache_dir: &Option<PathBuf>, | ||
| ) -> Result<(ModelWeights, Device), Box<dyn std::error::Error>> { | ||
| info!( | ||
| "avx: {}, neon: {}, simd128: {}, f16c: {}", | ||
| candle_core::utils::with_avx(), | ||
| candle_core::utils::with_neon(), | ||
| candle_core::utils::with_simd128(), | ||
| candle_core::utils::with_f16c() | ||
| ); | ||
| let revision = "main".to_string(); | ||
|
|
||
| let start = std::time::Instant::now(); | ||
| let api = match cache_dir { | ||
| Some(cache_dir) => ApiBuilder::default() | ||
| .with_cache_dir(cache_dir.clone()) | ||
| .build()?, | ||
| None => Api::new()?, | ||
| }; | ||
|
|
||
| let model_path = model.repo_path(); | ||
|
|
||
| debug!("model paths: {:?}", model_path); | ||
|
|
||
| let repo = api.repo(Repo::with_revision( | ||
| model_path.0.to_string(), | ||
| RepoType::Model, | ||
| revision, | ||
| )); | ||
|
|
||
| let model_path = &repo.get(model_path.1)?; | ||
| let mut file = std::fs::File::open(model_path)?; | ||
| info!("retrieved the model files in {:?}", start.elapsed()); | ||
|
|
||
| let model = match model_path.extension().and_then(|v| v.to_str()) { | ||
| Some("gguf") => { | ||
| let model = gguf_file::Content::read(&mut file)?; | ||
| let mut total_size_in_bytes = 0; | ||
| for (_, tensor) in model.tensor_infos.iter() { | ||
| let elem_count = tensor.shape.elem_count(); | ||
| total_size_in_bytes += | ||
| elem_count * tensor.ggml_dtype.type_size() / tensor.ggml_dtype.blck_size(); | ||
| } | ||
| debug!( | ||
| "loaded {:?} tensors ({}) in {:.2}s", | ||
| model.tensor_infos.len(), | ||
| &format_size(total_size_in_bytes), | ||
| start.elapsed().as_secs_f32(), | ||
| ); | ||
| ModelWeights::from_gguf(model, &mut file)? | ||
| } | ||
| Some("ggml" | "bin") | Some(_) | None => { | ||
| let content = ggml_file::Content::read(&mut file)?; | ||
| let mut total_size_in_bytes = 0; | ||
| for (_, tensor) in content.tensors.iter() { | ||
| let elem_count = tensor.shape().elem_count(); | ||
| total_size_in_bytes += | ||
| elem_count * tensor.dtype().type_size() / tensor.dtype().blck_size(); | ||
| } | ||
| debug!( | ||
| "loaded {:?} tensors ({}) in {:.2}s", | ||
| content.tensors.len(), | ||
| &format_size(total_size_in_bytes), | ||
| start.elapsed().as_secs_f32(), | ||
| ); | ||
| debug!("params: {:?}", content.hparams); | ||
| let default_gqa = match model { | ||
| Models::L7b | ||
| | Models::L13b | ||
| | Models::L7bChat | ||
| | Models::L13bChat | ||
| | Models::L7bCode | ||
| | Models::L13bCode | ||
| | Models::L34bCode | ||
| | Models::Leo7b | ||
| | Models::Leo13b => 1, | ||
| Models::Mixtral | ||
| | Models::MixtralInstruct | ||
| | Models::Mistral7b | ||
| | Models::Mistral7bInstruct | ||
| | Models::Zephyr7bAlpha | ||
| | Models::Zephyr7bBeta | ||
| | Models::L70b | ||
| | Models::L70bChat | ||
| | Models::OpenChat35 | ||
| | Models::Starling7bAlpha => 8, | ||
| }; | ||
| ModelWeights::from_ggml(content, default_gqa)? | ||
| } | ||
| }; | ||
| Ok((model, Device::Cpu)) | ||
| } | ||
|
|
||
| pub fn create_tokenizer(model: Models) -> Result<Tokenizer, Box<dyn std::error::Error>> { | ||
| let tokenizer_path = { | ||
| let api = hf_hub::api::sync::Api::new()?; | ||
| let repo = model.tokenizer_repo(); | ||
| let api = api.model(repo.to_string()); | ||
| api.get("tokenizer.json")? | ||
| }; | ||
| let tokenizer = Tokenizer::from_file(tokenizer_path).map_err(E::msg)?; | ||
| Ok(tokenizer) | ||
| } | ||
|
|
||
| pub fn create_text_generation( | ||
| model: Models, | ||
| cache_dir: &Option<PathBuf>, | ||
| ) -> Result<TextGeneration, Box<dyn std::error::Error>> { | ||
| let tokenizer = create_tokenizer(model)?; | ||
| let model = create_model(model, cache_dir)?; | ||
|
|
||
| let device = Device::Cpu; | ||
|
|
||
| Ok(TextGeneration::new(model.0, tokenizer, &device)) | ||
| } |
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