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hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/resnet/README.md | # candle-resnet
A candle implementation of inference using a pre-trained [ResNet](https://arxiv.org/abs/1512.03385).
This uses a classification head trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Running an example
```
$ cargo run --example resnet --release -- --image tiger.jpg
loaded image Tensor[dims 3, 224, 224; f32]
model built
tiger, Panthera tigris : 90.21%
tiger cat : 8.93%
lion, king of beasts, Panthera leo: 0.35%
leopard, Panthera pardus: 0.16%
jaguar, panther, Panthera onca, Felis onca: 0.09%
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/wuerstchen/main.rs | #[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::stable_diffusion;
use candle_transformers::models::wuerstchen;
use anyhow::{Error as E, Result};
use candle::{DType, Device, IndexOp, Tensor};
use clap::Parser;
use tokenizers::Tokenizer;
const PRIOR_GUIDANCE_SCALE: f64 = 4.0;
const RESOLUTION_MULTIPLE: f64 = 42.67;
const LATENT_DIM_SCALE: f64 = 10.67;
const PRIOR_CIN: usize = 16;
const DECODER_CIN: usize = 4;
#[derive(Parser)]
#[command(author, version, about, long_about = None)]
struct Args {
/// The prompt to be used for image generation.
#[arg(
long,
default_value = "A very realistic photo of a rusty robot walking on a sandy beach"
)]
prompt: String,
#[arg(long, default_value = "")]
uncond_prompt: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
use_flash_attn: bool,
/// The height in pixels of the generated image.
#[arg(long)]
height: Option<usize>,
/// The width in pixels of the generated image.
#[arg(long)]
width: Option<usize>,
/// The decoder weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
decoder_weights: Option<String>,
/// The CLIP weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
clip_weights: Option<String>,
/// The CLIP weight file used by the prior model, in .safetensors format.
#[arg(long, value_name = "FILE")]
prior_clip_weights: Option<String>,
/// The prior weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
prior_weights: Option<String>,
/// The VQGAN weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
vqgan_weights: Option<String>,
#[arg(long, value_name = "FILE")]
/// The file specifying the tokenizer to used for tokenization.
tokenizer: Option<String>,
#[arg(long, value_name = "FILE")]
/// The file specifying the tokenizer to used for prior tokenization.
prior_tokenizer: Option<String>,
/// The number of samples to generate.
#[arg(long, default_value_t = 1)]
num_samples: i64,
/// The name of the final image to generate.
#[arg(long, value_name = "FILE", default_value = "sd_final.png")]
final_image: String,
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum ModelFile {
Tokenizer,
PriorTokenizer,
Clip,
PriorClip,
Decoder,
VqGan,
Prior,
}
impl ModelFile {
fn get(&self, filename: Option<String>) -> Result<std::path::PathBuf> {
use hf_hub::api::sync::Api;
match filename {
Some(filename) => Ok(std::path::PathBuf::from(filename)),
None => {
let repo_main = "warp-ai/wuerstchen";
let repo_prior = "warp-ai/wuerstchen-prior";
let (repo, path) = match self {
Self::Tokenizer => (repo_main, "tokenizer/tokenizer.json"),
Self::PriorTokenizer => (repo_prior, "tokenizer/tokenizer.json"),
Self::Clip => (repo_main, "text_encoder/model.safetensors"),
Self::PriorClip => (repo_prior, "text_encoder/model.safetensors"),
Self::Decoder => (repo_main, "decoder/diffusion_pytorch_model.safetensors"),
Self::VqGan => (repo_main, "vqgan/diffusion_pytorch_model.safetensors"),
Self::Prior => (repo_prior, "prior/diffusion_pytorch_model.safetensors"),
};
let filename = Api::new()?.model(repo.to_string()).get(path)?;
Ok(filename)
}
}
}
}
fn output_filename(
basename: &str,
sample_idx: i64,
num_samples: i64,
timestep_idx: Option<usize>,
) -> String {
let filename = if num_samples > 1 {
match basename.rsplit_once('.') {
None => format!("{basename}.{sample_idx}.png"),
Some((filename_no_extension, extension)) => {
format!("{filename_no_extension}.{sample_idx}.{extension}")
}
}
} else {
basename.to_string()
};
match timestep_idx {
None => filename,
Some(timestep_idx) => match filename.rsplit_once('.') {
None => format!("{filename}-{timestep_idx}.png"),
Some((filename_no_extension, extension)) => {
format!("{filename_no_extension}-{timestep_idx}.{extension}")
}
},
}
}
fn encode_prompt(
prompt: &str,
uncond_prompt: Option<&str>,
tokenizer: std::path::PathBuf,
clip_weights: std::path::PathBuf,
clip_config: stable_diffusion::clip::Config,
device: &Device,
) -> Result<Tensor> {
let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
let pad_id = match &clip_config.pad_with {
Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
};
println!("Running with prompt \"{prompt}\".");
let mut tokens = tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let tokens_len = tokens.len();
while tokens.len() < clip_config.max_position_embeddings {
tokens.push(pad_id)
}
let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
println!("Building the clip transformer.");
let text_model =
stable_diffusion::build_clip_transformer(&clip_config, clip_weights, device, DType::F32)?;
let text_embeddings = text_model.forward_with_mask(&tokens, tokens_len - 1)?;
match uncond_prompt {
None => Ok(text_embeddings),
Some(uncond_prompt) => {
let mut uncond_tokens = tokenizer
.encode(uncond_prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let uncond_tokens_len = uncond_tokens.len();
while uncond_tokens.len() < clip_config.max_position_embeddings {
uncond_tokens.push(pad_id)
}
let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
let uncond_embeddings =
text_model.forward_with_mask(&uncond_tokens, uncond_tokens_len - 1)?;
let text_embeddings = Tensor::cat(&[text_embeddings, uncond_embeddings], 0)?;
Ok(text_embeddings)
}
}
}
fn run(args: Args) -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let Args {
prompt,
uncond_prompt,
cpu,
height,
width,
tokenizer,
final_image,
num_samples,
clip_weights,
prior_weights,
vqgan_weights,
decoder_weights,
tracing,
..
} = args;
let _guard = if tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
let device = candle_examples::device(cpu)?;
let height = height.unwrap_or(1024);
let width = width.unwrap_or(1024);
let prior_text_embeddings = {
let tokenizer = ModelFile::PriorTokenizer.get(args.prior_tokenizer)?;
let weights = ModelFile::PriorClip.get(args.prior_clip_weights)?;
encode_prompt(
&prompt,
Some(&uncond_prompt),
tokenizer.clone(),
weights,
stable_diffusion::clip::Config::wuerstchen_prior(),
&device,
)?
};
println!("generated prior text embeddings {prior_text_embeddings:?}");
let text_embeddings = {
let tokenizer = ModelFile::Tokenizer.get(tokenizer)?;
let weights = ModelFile::Clip.get(clip_weights)?;
encode_prompt(
&prompt,
None,
tokenizer.clone(),
weights,
stable_diffusion::clip::Config::wuerstchen(),
&device,
)?
};
println!("generated text embeddings {text_embeddings:?}");
println!("Building the prior.");
let b_size = 1;
let image_embeddings = {
// https://huggingface.co/warp-ai/wuerstchen-prior/blob/main/prior/config.json
let latent_height = (height as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
let latent_width = (width as f64 / RESOLUTION_MULTIPLE).ceil() as usize;
let mut latents = Tensor::randn(
0f32,
1f32,
(b_size, PRIOR_CIN, latent_height, latent_width),
&device,
)?;
let prior = {
let file = ModelFile::Prior.get(prior_weights)?;
let vb = unsafe {
candle_nn::VarBuilder::from_mmaped_safetensors(&[file], DType::F32, &device)?
};
wuerstchen::prior::WPrior::new(
/* c_in */ PRIOR_CIN,
/* c */ 1536,
/* c_cond */ 1280,
/* c_r */ 64,
/* depth */ 32,
/* nhead */ 24,
args.use_flash_attn,
vb,
)?
};
let prior_scheduler = wuerstchen::ddpm::DDPMWScheduler::new(60, Default::default())?;
let timesteps = prior_scheduler.timesteps();
let timesteps = ×teps[..timesteps.len() - 1];
println!("prior denoising");
for (index, &t) in timesteps.iter().enumerate() {
let start_time = std::time::Instant::now();
let latent_model_input = Tensor::cat(&[&latents, &latents], 0)?;
let ratio = (Tensor::ones(2, DType::F32, &device)? * t)?;
let noise_pred = prior.forward(&latent_model_input, &ratio, &prior_text_embeddings)?;
let noise_pred = noise_pred.chunk(2, 0)?;
let (noise_pred_text, noise_pred_uncond) = (&noise_pred[0], &noise_pred[1]);
let noise_pred = (noise_pred_uncond
+ ((noise_pred_text - noise_pred_uncond)? * PRIOR_GUIDANCE_SCALE)?)?;
latents = prior_scheduler.step(&noise_pred, t, &latents)?;
let dt = start_time.elapsed().as_secs_f32();
println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt);
}
((latents * 42.)? - 1.)?
};
println!("Building the vqgan.");
let vqgan = {
let file = ModelFile::VqGan.get(vqgan_weights)?;
let vb = unsafe {
candle_nn::VarBuilder::from_mmaped_safetensors(&[file], DType::F32, &device)?
};
wuerstchen::paella_vq::PaellaVQ::new(vb)?
};
println!("Building the decoder.");
// https://huggingface.co/warp-ai/wuerstchen/blob/main/decoder/config.json
let decoder = {
let file = ModelFile::Decoder.get(decoder_weights)?;
let vb = unsafe {
candle_nn::VarBuilder::from_mmaped_safetensors(&[file], DType::F32, &device)?
};
wuerstchen::diffnext::WDiffNeXt::new(
/* c_in */ DECODER_CIN,
/* c_out */ DECODER_CIN,
/* c_r */ 64,
/* c_cond */ 1024,
/* clip_embd */ 1024,
/* patch_size */ 2,
args.use_flash_attn,
vb,
)?
};
for idx in 0..num_samples {
// https://huggingface.co/warp-ai/wuerstchen/blob/main/model_index.json
let latent_height = (image_embeddings.dim(2)? as f64 * LATENT_DIM_SCALE) as usize;
let latent_width = (image_embeddings.dim(3)? as f64 * LATENT_DIM_SCALE) as usize;
let mut latents = Tensor::randn(
0f32,
1f32,
(b_size, DECODER_CIN, latent_height, latent_width),
&device,
)?;
println!("diffusion process with prior {image_embeddings:?}");
let scheduler = wuerstchen::ddpm::DDPMWScheduler::new(12, Default::default())?;
let timesteps = scheduler.timesteps();
let timesteps = ×teps[..timesteps.len() - 1];
for (index, &t) in timesteps.iter().enumerate() {
let start_time = std::time::Instant::now();
let ratio = (Tensor::ones(1, DType::F32, &device)? * t)?;
let noise_pred =
decoder.forward(&latents, &ratio, &image_embeddings, Some(&text_embeddings))?;
latents = scheduler.step(&noise_pred, t, &latents)?;
let dt = start_time.elapsed().as_secs_f32();
println!("step {}/{} done, {:.2}s", index + 1, timesteps.len(), dt);
}
println!(
"Generating the final image for sample {}/{}.",
idx + 1,
num_samples
);
let image = vqgan.decode(&(&latents * 0.3764)?)?;
let image = (image.clamp(0f32, 1f32)? * 255.)?
.to_dtype(DType::U8)?
.i(0)?;
let image_filename = output_filename(&final_image, idx + 1, num_samples, None);
candle_examples::save_image(&image, image_filename)?
}
Ok(())
}
fn main() -> Result<()> {
let args = Args::parse();
run(args)
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/wuerstchen/README.md | # candle-wuerstchen: Efficient Pretraining of Text-to-Image Models
The `wuerstchen` example is a port of the [diffusers
implementation](https://github.com/huggingface/diffusers/tree/19edca82f1ff194c07317369a92b470dbae97f34/src/diffusers/pipelines/wuerstchen) for Würstchen v2.
The candle implementation reproduces the same structure/files for models and
pipelines. Useful resources:
- [Official implementation](https://github.com/dome272/Wuerstchen).
- [Arxiv paper](https://arxiv.org/abs/2306.00637).
- Blog post: [Introducing Würstchen: Fast Diffusion for Image Generation](https://huggingface.co/blog/wuerstchen).
## Getting the weights
The weights are automatically downloaded for you from the [HuggingFace
Hub](https://huggingface.co/) on the first run. There are various command line
flags to use local files instead, run with `--help` to learn about them.
## Running some example.
```bash
cargo run --example wuerstchen --release --features cuda,cudnn -- \
--prompt "Anthropomorphic cat dressed as a fire fighter"
```
The final image is named `sd_final.png` by default.
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama_multiprocess/main.rs | // An implementation of LLaMA https://github.com/facebookresearch/llama
//
// This is based on nanoGPT in a similar way to:
// https://github.com/Lightning-AI/lit-llama/blob/main/lit_llama/model.py
//
// The tokenizer config can be retrieved from:
// https://huggingface.co/hf-internal-testing/llama-tokenizer/raw/main/tokenizer.json
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{bail, Error as E, Result};
use clap::Parser;
use candle::{DType, Device, Tensor};
use candle_transformers::generation::LogitsProcessor;
use cudarc::driver::safe::CudaDevice;
use cudarc::nccl::safe::{Comm, Id};
use hf_hub::{api::sync::Api, Repo, RepoType};
use std::io::Write;
use std::rc::Rc;
mod model;
use model::{Config, Llama};
const MAX_SEQ_LEN: usize = 4096;
const DEFAULT_PROMPT: &str = r"
EDWARD:
I wonder how our princely father 'scaped,
Or whether he be 'scaped away or no
From Clifford's and Northumberland's pursuit:
Had he been ta'en, we should have heard the news;
Had he been slain, we should have heard the news;
Or had he 'scaped, methinks we should have heard
The happy tidings of his good escape.
How fares my brother? why is he so sad?
RICHARD:
I cannot joy, until I be resolved
Where our right valiant father is become.
I saw him in the battle range about;
And watch'd him how he singled Clifford forth.
Methought he bore him in the thickest troop
As doth a lion in a herd of neat;
Or as a bear, encompass'd round with dogs,
Who having pinch'd a few and made them cry,
The rest stand all aloof, and bark at him.
So fared our father with his enemies;
So fled his enemies my warlike father:
Methinks, 'tis prize enough to be his son.
See how the morning opes her golden gates,
And takes her farewell of the glorious sun!
How well resembles it the prime of youth,
Trimm'd like a younker prancing to his love!
EDWARD:
Dazzle mine eyes, or do I see three suns?
RICHARD:
Three glorious suns, each one a perfect sun;
Not separated with the racking clouds,
But sever'd in a pale clear-shining sky.
See, see! they join, embrace, and seem to kiss,
As if they vow'd some league inviolable:
Now are they but one lamp, one light, one sun.
In this the heaven figures some event.
EDWARD:
'Tis wondrous strange, the like yet never heard of.
I think it cites us, brother, to the field,
That we, the sons of brave Plantagenet,
Each one already blazing by our meeds,
Should notwithstanding join our lights together
And over-shine the earth as this the world.
Whate'er it bodes, henceforward will I bear
Upon my target three fair-shining suns.
";
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
#[arg(long)]
num_shards: usize,
#[arg(long)]
rank: Option<usize>,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, default_value_t = 100)]
sample_len: usize,
/// Disable the key-value cache.
#[arg(long)]
no_kv_cache: bool,
/// The initial prompt.
#[arg(long)]
prompt: Option<String>,
#[arg(long)]
model_id: Option<String>,
#[arg(long)]
revision: Option<String>,
#[arg(long)]
dtype: Option<String>,
}
fn main() -> Result<()> {
use tokenizers::Tokenizer;
let args = Args::parse();
let dtype = match args.dtype.as_deref() {
Some("f16") => DType::F16,
Some("bf16") => DType::BF16,
Some("f32") => DType::F32,
Some(dtype) => bail!("Unsupported dtype {dtype}"),
None => DType::F16,
};
let api = Api::new()?;
let model_id = args
.model_id
.unwrap_or_else(|| "meta-llama/Llama-2-7b-hf".to_string());
println!("loading the model weights from {model_id}");
let revision = args.revision.unwrap_or("main".to_string());
let api = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
let config_filename = api.get("config.json")?;
let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
let tokenizer_filename = api.get("tokenizer.json")?;
let filenames = candle_examples::hub_load_safetensors(&api, "model.safetensors.index.json")?;
if args.rank.is_none() {
let children: Vec<_> = (0..args.num_shards)
.map(|rank| {
let mut args: std::collections::VecDeque<_> = std::env::args().collect();
args.push_back("--rank".to_string());
args.push_back(format!("{rank}"));
let name = args.pop_front().unwrap();
std::process::Command::new(name).args(args).spawn().unwrap()
})
.collect();
for mut child in children {
child.wait().unwrap();
}
return Ok(());
}
let i = args.rank.unwrap();
let num_shards = args.num_shards;
let rank = i;
// Primitive IPC
let id = if rank == 0 {
let id = Id::new().unwrap();
std::fs::File::create("nccl_id.txt.tmp")?
.write_all(&id.internal().iter().map(|&i| i as u8).collect::<Vec<_>>())
.unwrap();
std::fs::rename("nccl_id.txt.tmp", "nccl_id.txt")?;
id
} else {
let path = std::path::PathBuf::from("nccl_id.txt");
while !path.exists() {
std::thread::sleep(std::time::Duration::from_secs(1));
}
let data = std::fs::read("nccl_id.txt")?;
let internal: [i8; 128] = data
.into_iter()
.map(|i| i as i8)
.collect::<Vec<_>>()
.try_into()
.unwrap();
let id: Id = Id::uninit(internal);
id
};
let device = CudaDevice::new(i)?;
let comm = Rc::new(Comm::from_rank(device, i, num_shards, id).unwrap());
if rank == 0 {
std::fs::remove_file("nccl_id.txt")?;
}
println!("Rank {rank:?} spawned");
let device = Device::new_cuda(i)?;
let cache = model::Cache::new(dtype, &config, &device)?;
println!("building the model");
let vb = unsafe {
candle_nn::var_builder::ShardedSafeTensors::var_builder(&filenames, dtype, &device)?
};
let llama = Llama::load(vb, &cache, &config, comm)?;
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let prompt = args.prompt.as_ref().map_or(DEFAULT_PROMPT, |p| p.as_str());
let mut tokens = tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
println!("starting the inference loop");
let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature, args.top_p);
let mut new_tokens = vec![];
let start_gen = std::time::Instant::now();
let mut index_pos = 0;
for index in 0..args.sample_len {
let start_gen = std::time::Instant::now();
let context_size = if index > 0 { 1 } else { tokens.len() };
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
let logits = llama.forward(&input, index_pos)?;
let logits = logits.squeeze(0)?;
index_pos += ctxt.len();
let next_token = logits_processor.sample(&logits)?;
tokens.push(next_token);
new_tokens.push(next_token);
if rank == 0 {
println!("> {:?}", start_gen.elapsed());
println!(
"{} token: {} '{}'",
index + 1,
next_token,
tokenizer.decode(&[next_token], true).map_err(E::msg)?
);
}
}
let dt = start_gen.elapsed();
if rank == 0 {
println!(
"{} tokens generated ({} token/s)\n----\n{}\n----",
args.sample_len,
args.sample_len as f64 / dt.as_secs_f64(),
tokenizer
.decode(new_tokens.as_slice(), true)
.map_err(E::msg)?
);
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama_multiprocess/model.rs | use candle::backend::BackendStorage;
use candle::{CpuStorage, CustomOp1, DType, Device, IndexOp, Layout, Result, Shape, Tensor, D};
use candle_nn::{Embedding, Linear, Module, RmsNorm};
use cudarc::nccl::safe::{Comm, ReduceOp};
use half::f16;
use serde::Deserialize;
use std::rc::Rc;
use std::sync::{Arc, Mutex};
use super::MAX_SEQ_LEN;
use candle_nn::var_builder::ShardedVarBuilder as VarBuilder;
struct TensorParallelColumnLinear {
linear: Linear,
}
impl TensorParallelColumnLinear {
fn new(linear: Linear) -> Self {
Self { linear }
}
fn forward(&self, x: &Tensor) -> Result<Tensor> {
self.linear.forward(x)
}
}
struct TensorParallelRowLinear {
linear: Linear,
comm: Rc<Comm>,
}
struct AllReduce {
comm: Rc<Comm>,
}
/// This is actually not safe: https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/threadsafety.html
/// But for this example purposes, this will work
unsafe impl Sync for AllReduce {}
/// This is actually not safe: https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/threadsafety.html
/// But for this example purposes, this will work
unsafe impl Send for AllReduce {}
impl CustomOp1 for AllReduce {
fn name(&self) -> &'static str {
"allreduce"
}
fn cpu_fwd(&self, _s: &CpuStorage, _l: &Layout) -> Result<(CpuStorage, Shape)> {
todo!("implement allreduce for cpu is not necessary for single node");
}
#[cfg(feature = "cuda")]
fn cuda_fwd(
&self,
s: &candle::CudaStorage,
l: &Layout,
) -> Result<(candle::CudaStorage, Shape)> {
use candle::cuda_backend::WrapErr;
let elem_count = l.shape().elem_count();
let dev = s.device().clone();
let s = s.as_cuda_slice::<f16>()?;
// let s = match l.contiguous_offsets() {
// None => Err(Error::Wrapped("input has to be contiguous".into()))?,
// Some((o1, o2)) => s.slice(o1..o2),
// };
let mut dst = unsafe { dev.alloc::<f16>(elem_count) }.w()?;
self.comm.all_reduce(s, &mut dst, &ReduceOp::Sum).unwrap();
let dst = candle::CudaStorage::wrap_cuda_slice(dst, dev);
Ok((dst, l.shape().clone()))
}
}
fn all_reduce_sum(x: &Tensor, comm: &Rc<Comm>) -> Result<Tensor> {
x.apply_op1(AllReduce { comm: comm.clone() })
}
impl TensorParallelRowLinear {
fn new(linear: Linear, comm: Rc<Comm>) -> Self {
Self { linear, comm }
}
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let x = self.linear.forward(x)?;
all_reduce_sum(&x, &self.comm)
}
}
fn shard(dim: usize, rank: usize, world_size: usize) -> candle_nn::var_builder::Shard {
candle_nn::var_builder::Shard {
dim,
rank,
world_size,
}
}
impl TensorParallelColumnLinear {
fn load(vb: VarBuilder, comm: Rc<Comm>) -> Result<Self> {
let rank = comm.rank();
let size = comm.world_size();
let weight = vb.get_with_hints((), "weight", shard(0, rank, size))?;
Ok(Self::new(Linear::new(weight, None)))
}
fn load_multi(vb: VarBuilder, prefixes: &[&str], comm: Rc<Comm>) -> Result<Self> {
let rank = comm.rank();
let size = comm.world_size();
let weights: Vec<_> = prefixes
.iter()
.map(|p| vb.pp(p).get_with_hints((), "weight", shard(0, rank, size)))
.collect::<Result<Vec<_>>>()?;
let weight = Tensor::cat(&weights, 0)?;
Ok(Self::new(Linear::new(weight, None)))
}
}
impl TensorParallelRowLinear {
fn load(vb: VarBuilder, comm: Rc<Comm>) -> Result<Self> {
let rank = comm.rank();
let size = comm.world_size();
let weight = vb.get_with_hints((), "weight", shard(1, rank, size))?;
Ok(Self::new(Linear::new(weight, None), comm))
}
}
#[derive(Deserialize)]
pub struct Config {
pub hidden_size: usize,
pub intermediate_size: usize,
pub vocab_size: usize,
pub num_hidden_layers: usize,
pub num_attention_heads: usize,
pub num_key_value_heads: usize,
pub rms_norm_eps: f64,
#[serde(default = "default_rope")]
pub rope_theta: f32,
}
fn default_rope() -> f32 {
10_000.0
}
#[derive(Clone)]
pub struct Cache {
#[allow(clippy::type_complexity)]
kvs: Arc<Mutex<Vec<Option<(Tensor, Tensor)>>>>,
cos: Tensor,
sin: Tensor,
}
impl Cache {
pub fn new(dtype: DType, config: &Config, device: &Device) -> Result<Self> {
// precompute freqs_cis
let n_elem = config.hidden_size / config.num_attention_heads;
let theta: Vec<_> = (0..n_elem)
.step_by(2)
.map(|i| 1f32 / config.rope_theta.powf(i as f32 / n_elem as f32))
.collect();
let theta = Tensor::new(theta.as_slice(), device)?;
let idx_theta = Tensor::arange(0, MAX_SEQ_LEN as u32, device)?
.to_dtype(DType::F32)?
.reshape((MAX_SEQ_LEN, 1))?
.matmul(&theta.reshape((1, theta.elem_count()))?)?;
// This is different from the paper, see:
// https://github.com/huggingface/transformers/blob/6112b1c6442aaf7affd2b0676a1cd4eee30c45cf/src/transformers/models/llama/modeling_llama.py#L112
let idx_theta = Tensor::cat(&[&idx_theta, &idx_theta], D::Minus1)?;
let cos = idx_theta.cos()?.to_dtype(dtype)?;
let sin = idx_theta.sin()?.to_dtype(dtype)?;
Ok(Self {
kvs: Arc::new(Mutex::new(vec![None; config.num_hidden_layers])),
cos,
sin,
})
}
}
fn silu(xs: &Tensor) -> Result<Tensor> {
xs / (xs.neg()?.exp()? + 1.0)?
}
fn linear(size1: usize, size2: usize, vb: VarBuilder) -> Result<Linear> {
let weight = vb.get((size2, size1), "weight")?;
Ok(Linear::new(weight, None))
}
fn embedding(cfg: &Config, vb: VarBuilder) -> Result<Embedding> {
let embeddings = vb.get((cfg.vocab_size, cfg.hidden_size), "weight")?;
Ok(Embedding::new(embeddings, cfg.hidden_size))
}
struct CausalSelfAttention {
qkv_proj: TensorParallelColumnLinear,
o_proj: TensorParallelRowLinear,
num_attention_heads: usize,
num_key_value_heads: usize,
head_dim: usize,
cache: Cache,
}
impl CausalSelfAttention {
fn apply_rotary_emb(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
let (b_sz, _, seq_len, hidden_size) = x.shape().dims4()?;
let cos = self.cache.cos.narrow(0, index_pos, seq_len)?;
let sin = self.cache.sin.narrow(0, index_pos, seq_len)?;
let cos = cos.broadcast_as((b_sz, 1, seq_len, hidden_size))?;
let sin = sin.broadcast_as((b_sz, 1, seq_len, hidden_size))?;
let x1 = x.narrow(D::Minus1, 0, hidden_size / 2)?;
let x2 = x.narrow(D::Minus1, hidden_size / 2, hidden_size / 2)?;
let rotate_x = Tensor::cat(&[&x2.neg()?, &x1], D::Minus1)?;
let rope = (x.broadcast_mul(&cos)? + rotate_x.broadcast_mul(&sin)?)?;
Ok(rope)
}
fn forward(&self, x: &Tensor, index_pos: usize, block_idx: usize) -> Result<Tensor> {
let (b_sz, seq_len, _) = x.shape().dims3()?;
let qkv = self.qkv_proj.forward(x)?;
let hidden_size = self.num_attention_heads * self.head_dim;
let q = qkv.i((.., .., ..self.num_attention_heads * self.head_dim))?;
let k = qkv.i((
..,
..,
self.num_attention_heads * self.head_dim
..self.num_attention_heads * self.head_dim
+ self.num_key_value_heads * self.head_dim,
))?;
let v = qkv.i((
..,
..,
self.num_attention_heads * self.head_dim + self.num_key_value_heads * self.head_dim..,
))?;
// todo!("Q {:?} K {:?} V {:?} - x {:?}", q.shape(), k.shape(), v.shape(), x.shape());
let q = q
.reshape((b_sz, seq_len, self.num_attention_heads, self.head_dim))?
.transpose(1, 2)?;
let k = k
.reshape((b_sz, seq_len, self.num_key_value_heads, self.head_dim))?
.transpose(1, 2)?;
let mut v = v
.reshape((b_sz, seq_len, self.num_key_value_heads, self.head_dim))?
.transpose(1, 2)?;
let q = self.apply_rotary_emb(&q, index_pos)?;
let mut k = self.apply_rotary_emb(&k, index_pos)?;
let mut cache = self.cache.kvs.lock().unwrap();
if let Some((cache_k, cache_v)) = &cache[block_idx] {
k = Tensor::cat(&[cache_k, &k], 2)?.contiguous()?;
v = Tensor::cat(&[cache_v, &v], 2)?.contiguous()?;
let k_seq_len = k.dims()[1];
if k_seq_len > MAX_SEQ_LEN {
k = k
.narrow(D::Minus1, k_seq_len - MAX_SEQ_LEN, MAX_SEQ_LEN)?
.contiguous()?
}
let v_seq_len = v.dims()[1];
if v_seq_len > 2 * MAX_SEQ_LEN {
v = v
.narrow(D::Minus1, v_seq_len - MAX_SEQ_LEN, MAX_SEQ_LEN)?
.contiguous()?
}
}
cache[block_idx] = Some((k.clone(), v.clone()));
let k = self.repeat_kv(k)?;
let v = self.repeat_kv(v)?;
let q = q.transpose(1, 2)?;
let k = k.transpose(1, 2)?;
let v = v.transpose(1, 2)?;
let softmax_scale = 1f32 / (self.head_dim as f32).sqrt();
let y = candle_flash_attn::flash_attn(&q, &k, &v, softmax_scale, seq_len > 1)?
.transpose(1, 2)?;
// Convert to contiguous as matmul doesn't support strided vs for now.
let y = y.transpose(1, 2)?.reshape(&[b_sz, seq_len, hidden_size])?;
let y = self.o_proj.forward(&y)?;
Ok(y)
}
fn repeat_kv(&self, x: Tensor) -> Result<Tensor> {
let n_rep = self.num_attention_heads / self.num_key_value_heads;
if n_rep == 1 {
Ok(x)
} else {
let (b_sz, n_kv_head, seq_len, head_dim) = x.shape().dims4()?;
let x = x
.unsqueeze(2)?
.expand((b_sz, n_kv_head, n_rep, seq_len, head_dim))?
.reshape((b_sz, n_kv_head, n_rep, seq_len, head_dim))?;
Ok(x)
}
}
fn load(vb: VarBuilder, cache: &Cache, cfg: &Config, comm: Rc<Comm>) -> Result<Self> {
let qkv_proj = TensorParallelColumnLinear::load_multi(
vb.clone(),
&["q_proj", "k_proj", "v_proj"],
comm.clone(),
)?;
let o_proj = TensorParallelRowLinear::load(vb.pp("o_proj"), comm.clone())?;
Ok(Self {
qkv_proj,
o_proj,
num_attention_heads: cfg.num_attention_heads / comm.world_size(),
num_key_value_heads: cfg.num_key_value_heads / comm.world_size(),
head_dim: cfg.hidden_size / cfg.num_attention_heads,
cache: cache.clone(),
})
}
}
struct Mlp {
c_fc1: TensorParallelColumnLinear,
c_fc2: TensorParallelColumnLinear,
c_proj: TensorParallelRowLinear,
}
impl Mlp {
fn new(
c_fc1: TensorParallelColumnLinear,
c_fc2: TensorParallelColumnLinear,
c_proj: TensorParallelRowLinear,
) -> Self {
Self {
c_fc1,
c_fc2,
c_proj,
}
}
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let x = (silu(&self.c_fc1.forward(x)?)? * self.c_fc2.forward(x)?)?;
self.c_proj.forward(&x)
}
fn load(vb: VarBuilder, _cfg: &Config, comm: Rc<Comm>) -> Result<Self> {
let c_fc1 = TensorParallelColumnLinear::load(vb.pp("gate_proj"), comm.clone())?;
let c_fc2 = TensorParallelColumnLinear::load(vb.pp("up_proj"), comm.clone())?;
let c_proj = TensorParallelRowLinear::load(vb.pp("down_proj"), comm)?;
Ok(Self::new(c_fc1, c_fc2, c_proj))
}
}
struct Block {
rms_1: RmsNorm,
attn: CausalSelfAttention,
rms_2: RmsNorm,
mlp: Mlp,
}
fn rms_norm(size: usize, eps: f64, vb: VarBuilder) -> Result<RmsNorm> {
let weight = vb.get_with_hints(size, "weight", shard(0, 0, 1))?;
Ok(RmsNorm::new(weight, eps))
}
impl Block {
fn new(rms_1: RmsNorm, attn: CausalSelfAttention, rms_2: RmsNorm, mlp: Mlp) -> Self {
Self {
rms_1,
attn,
rms_2,
mlp,
}
}
fn forward(&self, x: &Tensor, index_pos: usize, block_idx: usize) -> Result<Tensor> {
let residual = x;
let x = self.rms_1.forward(x)?;
let x = (self.attn.forward(&x, index_pos, block_idx)? + residual)?;
let residual = &x;
let x = (self.mlp.forward(&self.rms_2.forward(&x)?)? + residual)?;
Ok(x)
}
fn load(vb: VarBuilder, cache: &Cache, cfg: &Config, comm: Rc<Comm>) -> Result<Self> {
let attn = CausalSelfAttention::load(vb.pp("self_attn"), cache, cfg, comm.clone())?;
let mlp = Mlp::load(vb.pp("mlp"), cfg, comm)?;
let input_layernorm = rms_norm(cfg.hidden_size, 1e-5, vb.pp("input_layernorm"))?;
let post_attention_layernorm =
rms_norm(cfg.hidden_size, 1e-5, vb.pp("post_attention_layernorm"))?;
Ok(Self::new(
input_layernorm,
attn,
post_attention_layernorm,
mlp,
))
}
}
pub struct Llama {
wte: Embedding,
blocks: Vec<Block>,
ln_f: RmsNorm,
lm_head: Linear,
}
impl Llama {
fn new(wte: Embedding, blocks: Vec<Block>, ln_f: RmsNorm, lm_head: Linear) -> Self {
Self {
wte,
blocks,
ln_f,
lm_head,
}
}
pub fn forward(&self, x: &Tensor, index_pos: usize) -> Result<Tensor> {
let (_b_sz, seq_len) = x.shape().dims2()?;
let mut x = self.wte.forward(x)?;
for (block_idx, block) in self.blocks.iter().enumerate() {
x = block.forward(&x, index_pos, block_idx)?;
}
let x = self.ln_f.forward(&x)?;
let x = x.i((.., seq_len - 1, ..))?;
let logits = self.lm_head.forward(&x)?;
logits.to_dtype(DType::F32)
}
pub fn load(vb: VarBuilder, cache: &Cache, cfg: &Config, comm: Rc<Comm>) -> Result<Self> {
let wte = embedding(cfg, vb.pp("model.embed_tokens"))?;
let lm_head = linear(cfg.hidden_size, cfg.vocab_size, vb.pp("lm_head"))?;
let norm = rms_norm(cfg.hidden_size, 1e-5, vb.pp("model.norm"))?;
let blocks: Vec<_> = (0..cfg.num_hidden_layers)
.map(|i| {
Block::load(
vb.pp(&format!("model.layers.{i}")),
cache,
cfg,
comm.clone(),
)
.unwrap()
})
.collect();
Ok(Self::new(wte, blocks, norm, lm_head))
}
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama2-c/main.rs | // https://github.com/karpathy/llama2.c
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::llama2_c as model;
use candle_transformers::models::llama2_c_weights as weights;
use candle_transformers::models::quantized_llama2_c as qmodel;
mod training;
use clap::{Parser, Subcommand};
use anyhow::{Error as E, Result};
use byteorder::{LittleEndian, ReadBytesExt};
use candle::{IndexOp, Tensor};
use candle_transformers::generation::LogitsProcessor;
use std::io::Write;
use tokenizers::Tokenizer;
use model::{Config, Llama};
use qmodel::QLlama;
use weights::TransformerWeights;
#[derive(Parser, Debug, Clone)]
struct InferenceCmd {
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
#[arg(long, default_value = "")]
prompt: String,
/// Config file in binary or safetensors format.
#[arg(long)]
config: Option<String>,
#[arg(long, default_value = "karpathy/tinyllamas")]
model_id: String,
/// The model to be used when getting it from the hub. Possible
/// values are 'stories15M.bin', 'stories42M.bin', see more at:
/// https://huggingface.co/karpathy/tinyllamas/tree/main
#[arg(long, default_value = "stories15M.bin")]
which_model: String,
}
#[derive(Parser, Debug, Clone)]
struct EvaluationCmd {
/// A directory with the pre-tokenized dataset in the format generated by the tinystories.py
/// script from llama2.c https://github.com/karpathy/llama2.c
#[arg(long)]
pretokenized_dir: Option<String>,
#[arg(long, default_value_t = 32)]
batch_size: usize,
/// Config file in binary format.
#[arg(long)]
config: Option<String>,
#[arg(long, default_value = "karpathy/tinyllamas")]
model_id: String,
/// The model to be used when getting it from the hub. Possible
/// values are 'stories15M.bin', 'stories42M.bin', see more at:
/// https://huggingface.co/karpathy/tinyllamas/tree/main
#[arg(long, default_value = "stories15M.bin")]
which_model: String,
}
#[derive(Parser, Debug, Clone)]
pub struct TrainingCmd {
/// A directory with the pre-tokenized dataset in the format generated by the tinystories.py
/// script from llama2.c https://github.com/karpathy/llama2.c
#[arg(long)]
pretokenized_dir: String,
#[arg(long, default_value_t = 32)]
batch_size: usize,
#[arg(long, default_value_t = 0.001)]
learning_rate: f64,
}
#[derive(Subcommand, Debug, Clone)]
enum Task {
Inference(InferenceCmd),
Eval(EvaluationCmd),
Train(TrainingCmd),
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
pub struct Args {
/// The task to be performed, inference, training or evaluation.
#[command(subcommand)]
task: Option<Task>,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Tokenizer config file.
#[arg(long)]
tokenizer: Option<String>,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
impl Args {
fn tokenizer(&self) -> Result<Tokenizer> {
let tokenizer_path = match &self.tokenizer {
Some(config) => std::path::PathBuf::from(config),
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("hf-internal-testing/llama-tokenizer".to_string());
api.get("tokenizer.json")?
}
};
Tokenizer::from_file(tokenizer_path).map_err(E::msg)
}
}
fn main() -> anyhow::Result<()> {
let args = Args::parse();
match &args.task {
None => {
let cmd = InferenceCmd {
temperature: None,
top_p: None,
prompt: "".to_string(),
config: None,
model_id: "karpathy/tinyllamas".to_string(),
which_model: "stories15M.bin".to_string(),
};
run_inference(&cmd, &args)?
}
Some(Task::Inference(cmd)) => run_inference(cmd, &args)?,
Some(Task::Eval(cmd)) => run_eval(cmd, &args)?,
Some(Task::Train(cmd)) => training::run(cmd, &args)?,
}
Ok(())
}
enum Model {
Llama(Llama),
QLlama(QLlama),
}
impl Model {
fn forward(&self, xs: &Tensor, pos: usize) -> anyhow::Result<Tensor> {
match self {
Self::Llama(l) => Ok(l.forward(xs, pos)?),
Self::QLlama(l) => Ok(l.forward(xs, pos)?),
}
}
}
fn run_eval(args: &EvaluationCmd, common_args: &Args) -> Result<()> {
use std::io::BufRead;
let config_path = match &args.config {
Some(config) => std::path::PathBuf::from(config),
None => {
let api = hf_hub::api::sync::Api::new()?;
println!("loading the model weights from {}", args.model_id);
let api = api.model(args.model_id.clone());
api.get(&args.which_model)?
}
};
let tokenizer = common_args.tokenizer()?;
let device = candle_examples::device(common_args.cpu)?;
let mut file = std::fs::File::open(config_path)?;
let config = Config::from_reader(&mut file)?;
let weights = TransformerWeights::from_reader(&mut file, &config, &device)?;
let vb = weights.var_builder(&config, &device)?;
let cache = model::Cache::new(false, &config, vb.pp("rot"))?;
let model = Llama::load(vb, &cache, config)?;
let tokens = match &args.pretokenized_dir {
None => {
let api = hf_hub::api::sync::Api::new()?;
let model_id = "roneneldan/TinyStories"; // TODO: Make this configurable.
println!("loading the evaluation dataset from {}", model_id);
let api = api.dataset(model_id.to_string());
let dataset_path = api.get("TinyStories-valid.txt")?;
let file = std::fs::File::open(dataset_path)?;
let file = std::io::BufReader::new(file);
let mut tokens = vec![];
for line in file.lines() {
let line = line?.replace("<|endoftext|>", "<s>");
let line = tokenizer.encode(line, false).map_err(E::msg)?;
tokens.push(line.get_ids().to_vec())
}
tokens.concat()
}
Some(pretokenized_dir) => {
// Use shard 0 for the test split, similar to llama2.c
// https://github.com/karpathy/llama2.c/blob/ce05cc28cf1e3560b873bb21837638a434520a67/tinystories.py#L121
let path = std::path::PathBuf::from(pretokenized_dir).join("data00.bin");
let bytes = std::fs::read(path)?;
// Tokens are encoded as u16.
let mut tokens = vec![0u16; bytes.len() / 2];
std::io::Cursor::new(bytes).read_u16_into::<LittleEndian>(&mut tokens)?;
tokens.into_iter().map(|u| u as u32).collect::<Vec<u32>>()
}
};
println!("dataset loaded and encoded: {} tokens", tokens.len());
let seq_len = model.config.seq_len;
let iter = (0..tokens.len()).step_by(seq_len).flat_map(|start_idx| {
if start_idx + seq_len + 1 > tokens.len() {
None
} else {
let tokens = &tokens[start_idx..start_idx + seq_len + 1];
let inputs = Tensor::new(&tokens[..seq_len], &device);
let targets = Tensor::new(&tokens[1..], &device);
Some(inputs.and_then(|inputs| targets.map(|targets| (inputs, targets))))
}
});
let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
for inp_tgt in batch_iter {
let (inp, tgt) = inp_tgt?;
let logits = model.forward(&inp, 0)?;
let loss = candle_nn::loss::cross_entropy(&logits.flatten_to(1)?, &tgt.flatten_to(1)?)?;
println!("{}", loss.to_vec0::<f32>()?);
}
Ok(())
}
fn run_inference(args: &InferenceCmd, common_args: &Args) -> Result<()> {
let config_path = match &args.config {
Some(config) => std::path::PathBuf::from(config),
None => {
let api = hf_hub::api::sync::Api::new()?;
println!("loading the model weights from {}", args.model_id);
let api = api.model(args.model_id.clone());
api.get(&args.which_model)?
}
};
let tokenizer = common_args.tokenizer()?;
let device = candle_examples::device(common_args.cpu)?;
let is_gguf = config_path.extension().map_or(false, |v| v == "gguf");
let is_safetensors = config_path
.extension()
.map_or(false, |v| v == "safetensors");
let (model, config) = if is_gguf {
let vb = qmodel::VarBuilder::from_gguf(config_path, &device)?;
let (_vocab_size, dim) = vb
.get_no_shape("model.embed_tokens.weight")?
.shape()
.dims2()?;
let config = match dim {
64 => Config::tiny_260k(),
288 => Config::tiny_15m(),
512 => Config::tiny_42m(),
768 => Config::tiny_110m(),
_ => anyhow::bail!("no config for dim {dim}"),
};
let freq_cis_real = vb
.get(
(config.seq_len, config.head_size() / 2),
"rot.freq_cis_real",
)?
.dequantize(&device)?;
let freq_cis_imag = vb
.get(
(config.seq_len, config.head_size() / 2),
"rot.freq_cis_imag",
)?
.dequantize(&device)?;
let fake_vb = candle_nn::VarBuilder::from_tensors(
[
("freq_cis_real".to_string(), freq_cis_real),
("freq_cis_imag".to_string(), freq_cis_imag),
]
.into_iter()
.collect(),
candle::DType::F32,
&device,
);
let cache = model::Cache::new(true, &config, fake_vb)?;
let model = Model::QLlama(QLlama::load(vb, &cache, config.clone())?);
(model, config)
} else if is_safetensors {
let config = Config::tiny_15m();
let tensors = candle::safetensors::load(config_path, &device)?;
let vb = candle_nn::VarBuilder::from_tensors(tensors, candle::DType::F32, &device);
let cache = model::Cache::new(true, &config, vb.pp("rot"))?;
let model = Model::Llama(Llama::load(vb, &cache, config.clone())?);
(model, config)
} else {
let mut file = std::fs::File::open(config_path)?;
let config = Config::from_reader(&mut file)?;
println!("{config:?}");
let weights = TransformerWeights::from_reader(&mut file, &config, &device)?;
let vb = weights.var_builder(&config, &device)?;
let cache = model::Cache::new(true, &config, vb.pp("rot"))?;
let model = Model::Llama(Llama::load(vb, &cache, config.clone())?);
(model, config)
};
println!("starting the inference loop");
let mut logits_processor = LogitsProcessor::new(299792458, args.temperature, args.top_p);
let mut index_pos = 0;
print!("{}", args.prompt);
let mut tokens = tokenizer
.encode(args.prompt.clone(), true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let start_gen = std::time::Instant::now();
for index in 0.. {
if tokens.len() >= config.seq_len {
break;
}
let context_size = if index > 0 { 1 } else { tokens.len() };
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
let logits = model.forward(&input, index_pos)?;
let logits = logits.i((0, logits.dim(1)? - 1))?;
let logits = if common_args.repeat_penalty == 1. || tokens.is_empty() {
logits
} else {
let start_at = tokens.len().saturating_sub(common_args.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
common_args.repeat_penalty,
&tokens[start_at..],
)?
};
index_pos += ctxt.len();
let next_token = logits_processor.sample(&logits)?;
tokens.push(next_token);
// Extracting the last token as a string is complicated, here we just apply some simple
// heuristics as it seems to work well enough for this example. See the following for more
// details:
// https://github.com/huggingface/tokenizers/issues/1141#issuecomment-1562644141
if let Some(text) = tokenizer.id_to_token(next_token) {
let text = text.replace('▁', " ").replace("<0x0A>", "\n");
print!("{text}");
std::io::stdout().flush()?;
}
}
let dt = start_gen.elapsed();
println!(
"\n{} tokens generated ({:.2} token/s)\n",
tokens.len(),
tokens.len() as f64 / dt.as_secs_f64(),
);
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama2-c/training.rs | use crate::model::{Cache, Config, Llama};
use candle::{DType, Device, Result};
use candle_datasets::nlp::tinystories::{Dataset, DatasetRandomIter};
use candle_nn::Optimizer;
fn valid_loss(
dataset: &Dataset,
model: &Llama,
args: &crate::TrainingCmd,
device: &Device,
) -> Result<f64> {
let iter = DatasetRandomIter::new(dataset, true, model.config.seq_len, device.clone());
let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
let mut sum_ce = 0f64;
let mut cnt = 0usize;
for inp_tgt in batch_iter.take(50) {
let (inp, tgt) = inp_tgt?;
let logits = model.forward(&inp, 0)?;
let loss = candle_nn::loss::cross_entropy(&logits.flatten_to(1)?, &tgt.flatten_to(1)?)?;
sum_ce += loss.to_vec0::<f32>()? as f64;
cnt += 1;
}
Ok(sum_ce / cnt as f64)
}
pub fn run(args: &crate::TrainingCmd, common_args: &crate::Args) -> Result<()> {
let device = candle_examples::device(common_args.cpu)?;
let dataset = Dataset::new(&args.pretokenized_dir)?;
println!(
"loaded dataset, train: {} files, valid: {} files",
dataset.train_tokens(),
dataset.valid_tokens()
);
let varmap = candle_nn::VarMap::new();
let vb = candle_nn::VarBuilder::from_varmap(&varmap, DType::F32, &device);
let config = Config::tiny_15m();
let iter = DatasetRandomIter::new(&dataset, false, config.seq_len, device.clone());
let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
let cache = Cache::new(false, &config, vb.pp("rot"))?;
let model = Llama::load(vb, &cache, config)?;
let params = candle_nn::ParamsAdamW {
lr: args.learning_rate,
..Default::default()
};
let mut opt = candle_nn::AdamW::new(varmap.all_vars(), params)?;
for (batch_index, batch) in batch_iter.enumerate() {
let (inp, tgt) = batch?;
let logits = model.forward(&inp, 0)?;
let loss = candle_nn::loss::cross_entropy(&logits.flatten_to(1)?, &tgt.flatten_to(1)?)?;
opt.backward_step(&loss)?;
if batch_index > 0 && batch_index % 100 == 0 {
// TODO: Add a way to deactivate the backprop graph tracking when computing the
// validation loss.
let loss = valid_loss(&dataset, &model, args, &device)?;
println!("{batch_index} {loss}");
}
if batch_index > 0 && batch_index % 1000 == 0 {
varmap.save("checkpoint.safetensors")?
}
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/main.rs | #![allow(unused)]
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::Result;
use clap::{Parser, Subcommand};
mod gym_env;
mod vec_gym_env;
mod ddpg;
mod policy_gradient;
#[derive(Parser)]
struct Args {
#[command(subcommand)]
command: Command,
}
#[derive(Subcommand)]
enum Command {
Pg,
Ddpg,
}
fn main() -> Result<()> {
let args = Args::parse();
match args.command {
Command::Pg => policy_gradient::run()?,
Command::Ddpg => ddpg::run()?,
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/atari_wrappers.py | import gymnasium as gym
import numpy as np
from collections import deque
from PIL import Image
from multiprocessing import Process, Pipe
# atari_wrappers.py
class NoopResetEnv(gym.Wrapper):
def __init__(self, env, noop_max=30):
"""Sample initial states by taking random number of no-ops on reset.
No-op is assumed to be action 0.
"""
gym.Wrapper.__init__(self, env)
self.noop_max = noop_max
self.override_num_noops = None
assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
def reset(self):
""" Do no-op action for a number of steps in [1, noop_max]."""
self.env.reset()
if self.override_num_noops is not None:
noops = self.override_num_noops
else:
noops = self.unwrapped.np_random.integers(1, self.noop_max + 1) #pylint: disable=E1101
assert noops > 0
obs = None
for _ in range(noops):
obs, _, done, _ = self.env.step(0)
if done:
obs = self.env.reset()
return obs
class FireResetEnv(gym.Wrapper):
def __init__(self, env):
"""Take action on reset for environments that are fixed until firing."""
gym.Wrapper.__init__(self, env)
assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
assert len(env.unwrapped.get_action_meanings()) >= 3
def reset(self):
self.env.reset()
obs, _, done, _ = self.env.step(1)
if done:
self.env.reset()
obs, _, done, _ = self.env.step(2)
if done:
self.env.reset()
return obs
class ImageSaver(gym.Wrapper):
def __init__(self, env, img_path, rank):
gym.Wrapper.__init__(self, env)
self._cnt = 0
self._img_path = img_path
self._rank = rank
def step(self, action):
step_result = self.env.step(action)
obs, _, _, _ = step_result
img = Image.fromarray(obs, 'RGB')
img.save('%s/out%d-%05d.png' % (self._img_path, self._rank, self._cnt))
self._cnt += 1
return step_result
class EpisodicLifeEnv(gym.Wrapper):
def __init__(self, env):
"""Make end-of-life == end-of-episode, but only reset on true game over.
Done by DeepMind for the DQN and co. since it helps value estimation.
"""
gym.Wrapper.__init__(self, env)
self.lives = 0
self.was_real_done = True
def step(self, action):
obs, reward, done, info = self.env.step(action)
self.was_real_done = done
# check current lives, make loss of life terminal,
# then update lives to handle bonus lives
lives = self.env.unwrapped.ale.lives()
if lives < self.lives and lives > 0:
# for Qbert sometimes we stay in lives == 0 condition for a few frames
# so its important to keep lives > 0, so that we only reset once
# the environment advertises done.
done = True
self.lives = lives
return obs, reward, done, info
def reset(self):
"""Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
"""
if self.was_real_done:
obs = self.env.reset()
else:
# no-op step to advance from terminal/lost life state
obs, _, _, _ = self.env.step(0)
self.lives = self.env.unwrapped.ale.lives()
return obs
class MaxAndSkipEnv(gym.Wrapper):
def __init__(self, env, skip=4):
"""Return only every `skip`-th frame"""
gym.Wrapper.__init__(self, env)
# most recent raw observations (for max pooling across time steps)
self._obs_buffer = deque(maxlen=2)
self._skip = skip
def step(self, action):
"""Repeat action, sum reward, and max over last observations."""
total_reward = 0.0
done = None
for _ in range(self._skip):
obs, reward, done, info = self.env.step(action)
self._obs_buffer.append(obs)
total_reward += reward
if done:
break
max_frame = np.max(np.stack(self._obs_buffer), axis=0)
return max_frame, total_reward, done, info
def reset(self):
"""Clear past frame buffer and init. to first obs. from inner env."""
self._obs_buffer.clear()
obs = self.env.reset()
self._obs_buffer.append(obs)
return obs
class ClipRewardEnv(gym.RewardWrapper):
def reward(self, reward):
"""Bin reward to {+1, 0, -1} by its sign."""
return np.sign(reward)
class WarpFrame(gym.ObservationWrapper):
def __init__(self, env):
"""Warp frames to 84x84 as done in the Nature paper and later work."""
gym.ObservationWrapper.__init__(self, env)
self.res = 84
self.observation_space = gym.spaces.Box(low=0, high=255, shape=(self.res, self.res, 1), dtype='uint8')
def observation(self, obs):
frame = np.dot(obs.astype('float32'), np.array([0.299, 0.587, 0.114], 'float32'))
frame = np.array(Image.fromarray(frame).resize((self.res, self.res),
resample=Image.BILINEAR), dtype=np.uint8)
return frame.reshape((self.res, self.res, 1))
class FrameStack(gym.Wrapper):
def __init__(self, env, k):
"""Buffer observations and stack across channels (last axis)."""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp = env.observation_space.shape
assert shp[2] == 1 # can only stack 1-channel frames
self.observation_space = gym.spaces.Box(low=0, high=255, shape=(shp[0], shp[1], k), dtype='uint8')
def reset(self):
"""Clear buffer and re-fill by duplicating the first observation."""
ob = self.env.reset()
for _ in range(self.k): self.frames.append(ob)
return self.observation()
def step(self, action):
ob, reward, done, info = self.env.step(action)
self.frames.append(ob)
return self.observation(), reward, done, info
def observation(self):
assert len(self.frames) == self.k
return np.concatenate(self.frames, axis=2)
def wrap_deepmind(env, episode_life=True, clip_rewards=True):
"""Configure environment for DeepMind-style Atari.
Note: this does not include frame stacking!"""
assert 'NoFrameskip' in env.spec.id # required for DeepMind-style skip
if episode_life:
env = EpisodicLifeEnv(env)
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env)
if clip_rewards:
env = ClipRewardEnv(env)
return env
# envs.py
def make_env(env_id, img_dir, seed, rank):
def _thunk():
env = gym.make(env_id)
env.reset(seed=(seed + rank))
if img_dir is not None:
env = ImageSaver(env, img_dir, rank)
env = wrap_deepmind(env)
env = WrapPyTorch(env)
return env
return _thunk
class WrapPyTorch(gym.ObservationWrapper):
def __init__(self, env=None):
super(WrapPyTorch, self).__init__(env)
self.observation_space = gym.spaces.Box(0.0, 1.0, [1, 84, 84], dtype='float32')
def observation(self, observation):
return observation.transpose(2, 0, 1)
# vecenv.py
class VecEnv(object):
"""
Vectorized environment base class
"""
def step(self, vac):
"""
Apply sequence of actions to sequence of environments
actions -> (observations, rewards, news)
where 'news' is a boolean vector indicating whether each element is new.
"""
raise NotImplementedError
def reset(self):
"""
Reset all environments
"""
raise NotImplementedError
def close(self):
pass
# subproc_vec_env.py
def worker(remote, env_fn_wrapper):
env = env_fn_wrapper.x()
while True:
cmd, data = remote.recv()
if cmd == 'step':
ob, reward, done, info = env.step(data)
if done:
ob = env.reset()
remote.send((ob, reward, done, info))
elif cmd == 'reset':
ob = env.reset()
remote.send(ob)
elif cmd == 'close':
remote.close()
break
elif cmd == 'get_spaces':
remote.send((env.action_space, env.observation_space))
else:
raise NotImplementedError
class CloudpickleWrapper(object):
"""
Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
"""
def __init__(self, x):
self.x = x
def __getstate__(self):
import cloudpickle
return cloudpickle.dumps(self.x)
def __setstate__(self, ob):
import pickle
self.x = pickle.loads(ob)
class SubprocVecEnv(VecEnv):
def __init__(self, env_fns):
"""
envs: list of gym environments to run in subprocesses
"""
nenvs = len(env_fns)
self.remotes, self.work_remotes = zip(*[Pipe() for _ in range(nenvs)])
self.ps = [Process(target=worker, args=(work_remote, CloudpickleWrapper(env_fn)))
for (work_remote, env_fn) in zip(self.work_remotes, env_fns)]
for p in self.ps:
p.start()
self.remotes[0].send(('get_spaces', None))
self.action_space, self.observation_space = self.remotes[0].recv()
def step(self, actions):
for remote, action in zip(self.remotes, actions):
remote.send(('step', action))
results = [remote.recv() for remote in self.remotes]
obs, rews, dones, infos = zip(*results)
return np.stack(obs), np.stack(rews), np.stack(dones), infos
def reset(self):
for remote in self.remotes:
remote.send(('reset', None))
return np.stack([remote.recv() for remote in self.remotes])
def close(self):
for remote in self.remotes:
remote.send(('close', None))
for p in self.ps:
p.join()
@property
def num_envs(self):
return len(self.remotes)
# Create the environment.
def make(env_name, img_dir, num_processes):
envs = SubprocVecEnv([
make_env(env_name, img_dir, 1337, i) for i in range(num_processes)
])
return envs
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/README.md | # candle-reinforcement-learning
Reinforcement Learning examples for candle.
This has been tested with `gymnasium` version `0.29.1`. You can install the
Python package with:
```bash
pip install "gymnasium[accept-rom-license]"
```
In order to run the examples, use the following commands. Note the additional
`--package` flag to ensure that there is no conflict with the `candle-pyo3`
crate.
For the Policy Gradient example:
```bash
cargo run --example reinforcement-learning --features=pyo3 --package candle-examples -- pg
```
For the Deep Deterministic Policy Gradient example:
```bash
cargo run --example reinforcement-learning --features=pyo3 --package candle-examples -- ddpg
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/gym_env.rs | #![allow(unused)]
//! Wrappers around the Python API of Gymnasium (the new version of OpenAI gym)
use candle::{Device, Result, Tensor};
use pyo3::prelude::*;
use pyo3::types::PyDict;
/// The return value for a step.
#[derive(Debug)]
pub struct Step<A> {
pub state: Tensor,
pub action: A,
pub reward: f64,
pub terminated: bool,
pub truncated: bool,
}
impl<A: Copy> Step<A> {
/// Returns a copy of this step changing the observation tensor.
pub fn copy_with_obs(&self, state: &Tensor) -> Step<A> {
Step {
state: state.clone(),
action: self.action,
reward: self.reward,
terminated: self.terminated,
truncated: self.truncated,
}
}
}
/// An OpenAI Gym session.
pub struct GymEnv {
env: PyObject,
action_space: usize,
observation_space: Vec<usize>,
}
fn w(res: PyErr) -> candle::Error {
candle::Error::wrap(res)
}
impl GymEnv {
/// Creates a new session of the specified OpenAI Gym environment.
pub fn new(name: &str) -> Result<GymEnv> {
Python::with_gil(|py| {
let gym = py.import("gymnasium")?;
let make = gym.getattr("make")?;
let env = make.call1((name,))?;
let action_space = env.getattr("action_space")?;
let action_space = if let Ok(val) = action_space.getattr("n") {
val.extract()?
} else {
let action_space: Vec<usize> = action_space.getattr("shape")?.extract()?;
action_space[0]
};
let observation_space = env.getattr("observation_space")?;
let observation_space = observation_space.getattr("shape")?.extract()?;
Ok(GymEnv {
env: env.into(),
action_space,
observation_space,
})
})
.map_err(w)
}
/// Resets the environment, returning the observation tensor.
pub fn reset(&self, seed: u64) -> Result<Tensor> {
let state: Vec<f32> = Python::with_gil(|py| {
let kwargs = PyDict::new(py);
kwargs.set_item("seed", seed)?;
let state = self.env.call_method(py, "reset", (), Some(kwargs))?;
state.as_ref(py).get_item(0)?.extract()
})
.map_err(w)?;
Tensor::new(state, &Device::Cpu)
}
/// Applies an environment step using the specified action.
pub fn step<A: pyo3::IntoPy<pyo3::Py<pyo3::PyAny>> + Clone>(
&self,
action: A,
) -> Result<Step<A>> {
let (state, reward, terminated, truncated) = Python::with_gil(|py| {
let step = self.env.call_method(py, "step", (action.clone(),), None)?;
let step = step.as_ref(py);
let state: Vec<f32> = step.get_item(0)?.extract()?;
let reward: f64 = step.get_item(1)?.extract()?;
let terminated: bool = step.get_item(2)?.extract()?;
let truncated: bool = step.get_item(3)?.extract()?;
Ok((state, reward, terminated, truncated))
})
.map_err(w)?;
let state = Tensor::new(state, &Device::Cpu)?;
Ok(Step {
state,
action,
reward,
terminated,
truncated,
})
}
/// Returns the number of allowed actions for this environment.
pub fn action_space(&self) -> usize {
self.action_space
}
/// Returns the shape of the observation tensors.
pub fn observation_space(&self) -> &[usize] {
&self.observation_space
}
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/ddpg.rs | use std::collections::VecDeque;
use std::fmt::Display;
use candle::{DType, Device, Error, Module, Result, Tensor, Var};
use candle_nn::{
func, linear, sequential::seq, Activation, AdamW, Optimizer, ParamsAdamW, Sequential,
VarBuilder, VarMap,
};
use rand::{distributions::Uniform, thread_rng, Rng};
use super::gym_env::GymEnv;
pub struct OuNoise {
mu: f64,
theta: f64,
sigma: f64,
state: Tensor,
}
impl OuNoise {
pub fn new(mu: f64, theta: f64, sigma: f64, size_action: usize) -> Result<Self> {
Ok(Self {
mu,
theta,
sigma,
state: Tensor::ones(size_action, DType::F32, &Device::Cpu)?,
})
}
pub fn sample(&mut self) -> Result<Tensor> {
let rand = Tensor::randn_like(&self.state, 0.0, 1.0)?;
let dx = ((self.theta * (self.mu - &self.state)?)? + (self.sigma * rand)?)?;
self.state = (&self.state + dx)?;
Ok(self.state.clone())
}
}
#[derive(Clone)]
struct Transition {
state: Tensor,
action: Tensor,
reward: Tensor,
next_state: Tensor,
terminated: bool,
truncated: bool,
}
impl Transition {
fn new(
state: &Tensor,
action: &Tensor,
reward: &Tensor,
next_state: &Tensor,
terminated: bool,
truncated: bool,
) -> Self {
Self {
state: state.clone(),
action: action.clone(),
reward: reward.clone(),
next_state: next_state.clone(),
terminated,
truncated,
}
}
}
pub struct ReplayBuffer {
buffer: VecDeque<Transition>,
capacity: usize,
size: usize,
}
impl ReplayBuffer {
pub fn new(capacity: usize) -> Self {
Self {
buffer: VecDeque::with_capacity(capacity),
capacity,
size: 0,
}
}
pub fn push(
&mut self,
state: &Tensor,
action: &Tensor,
reward: &Tensor,
next_state: &Tensor,
terminated: bool,
truncated: bool,
) {
if self.size == self.capacity {
self.buffer.pop_front();
} else {
self.size += 1;
}
self.buffer.push_back(Transition::new(
state, action, reward, next_state, terminated, truncated,
));
}
#[allow(clippy::type_complexity)]
pub fn random_batch(
&self,
batch_size: usize,
) -> Result<Option<(Tensor, Tensor, Tensor, Tensor, Vec<bool>, Vec<bool>)>> {
if self.size < batch_size {
Ok(None)
} else {
let transitions: Vec<&Transition> = thread_rng()
.sample_iter(Uniform::from(0..self.size))
.take(batch_size)
.map(|i| self.buffer.get(i).unwrap())
.collect();
let states: Vec<Tensor> = transitions
.iter()
.map(|t| t.state.unsqueeze(0))
.collect::<Result<_>>()?;
let actions: Vec<Tensor> = transitions
.iter()
.map(|t| t.action.unsqueeze(0))
.collect::<Result<_>>()?;
let rewards: Vec<Tensor> = transitions
.iter()
.map(|t| t.reward.unsqueeze(0))
.collect::<Result<_>>()?;
let next_states: Vec<Tensor> = transitions
.iter()
.map(|t| t.next_state.unsqueeze(0))
.collect::<Result<_>>()?;
let terminateds: Vec<bool> = transitions.iter().map(|t| t.terminated).collect();
let truncateds: Vec<bool> = transitions.iter().map(|t| t.truncated).collect();
Ok(Some((
Tensor::cat(&states, 0)?,
Tensor::cat(&actions, 0)?,
Tensor::cat(&rewards, 0)?,
Tensor::cat(&next_states, 0)?,
terminateds,
truncateds,
)))
}
}
}
fn track(
varmap: &mut VarMap,
vb: &VarBuilder,
target_prefix: &str,
network_prefix: &str,
dims: &[(usize, usize)],
tau: f64,
) -> Result<()> {
for (i, &(in_dim, out_dim)) in dims.iter().enumerate() {
let target_w = vb.get((out_dim, in_dim), &format!("{target_prefix}-fc{i}.weight"))?;
let network_w = vb.get((out_dim, in_dim), &format!("{network_prefix}-fc{i}.weight"))?;
varmap.set_one(
format!("{target_prefix}-fc{i}.weight"),
((tau * network_w)? + ((1.0 - tau) * target_w)?)?,
)?;
let target_b = vb.get(out_dim, &format!("{target_prefix}-fc{i}.bias"))?;
let network_b = vb.get(out_dim, &format!("{network_prefix}-fc{i}.bias"))?;
varmap.set_one(
format!("{target_prefix}-fc{i}.bias"),
((tau * network_b)? + ((1.0 - tau) * target_b)?)?,
)?;
}
Ok(())
}
struct Actor<'a> {
varmap: VarMap,
vb: VarBuilder<'a>,
network: Sequential,
target_network: Sequential,
size_state: usize,
size_action: usize,
dims: Vec<(usize, usize)>,
}
impl Actor<'_> {
fn new(device: &Device, dtype: DType, size_state: usize, size_action: usize) -> Result<Self> {
let mut varmap = VarMap::new();
let vb = VarBuilder::from_varmap(&varmap, dtype, device);
let dims = vec![(size_state, 400), (400, 300), (300, size_action)];
let make_network = |prefix: &str| {
let seq = seq()
.add(linear(
dims[0].0,
dims[0].1,
vb.pp(format!("{prefix}-fc0")),
)?)
.add(Activation::Relu)
.add(linear(
dims[1].0,
dims[1].1,
vb.pp(format!("{prefix}-fc1")),
)?)
.add(Activation::Relu)
.add(linear(
dims[2].0,
dims[2].1,
vb.pp(format!("{prefix}-fc2")),
)?)
.add(func(|xs| xs.tanh()));
Ok::<Sequential, Error>(seq)
};
let network = make_network("actor")?;
let target_network = make_network("target-actor")?;
// this sets the two networks to be equal to each other using tau = 1.0
track(&mut varmap, &vb, "target-actor", "actor", &dims, 1.0);
Ok(Self {
varmap,
vb,
network,
target_network,
size_state,
size_action,
dims,
})
}
fn forward(&self, state: &Tensor) -> Result<Tensor> {
self.network.forward(state)
}
fn target_forward(&self, state: &Tensor) -> Result<Tensor> {
self.target_network.forward(state)
}
fn track(&mut self, tau: f64) -> Result<()> {
track(
&mut self.varmap,
&self.vb,
"target-actor",
"actor",
&self.dims,
tau,
)
}
}
struct Critic<'a> {
varmap: VarMap,
vb: VarBuilder<'a>,
network: Sequential,
target_network: Sequential,
size_state: usize,
size_action: usize,
dims: Vec<(usize, usize)>,
}
impl Critic<'_> {
fn new(device: &Device, dtype: DType, size_state: usize, size_action: usize) -> Result<Self> {
let mut varmap = VarMap::new();
let vb = VarBuilder::from_varmap(&varmap, dtype, device);
let dims: Vec<(usize, usize)> = vec![(size_state + size_action, 400), (400, 300), (300, 1)];
let make_network = |prefix: &str| {
let seq = seq()
.add(linear(
dims[0].0,
dims[0].1,
vb.pp(format!("{prefix}-fc0")),
)?)
.add(Activation::Relu)
.add(linear(
dims[1].0,
dims[1].1,
vb.pp(format!("{prefix}-fc1")),
)?)
.add(Activation::Relu)
.add(linear(
dims[2].0,
dims[2].1,
vb.pp(format!("{prefix}-fc2")),
)?);
Ok::<Sequential, Error>(seq)
};
let network = make_network("critic")?;
let target_network = make_network("target-critic")?;
// this sets the two networks to be equal to each other using tau = 1.0
track(&mut varmap, &vb, "target-critic", "critic", &dims, 1.0);
Ok(Self {
varmap,
vb,
network,
target_network,
size_state,
size_action,
dims,
})
}
fn forward(&self, state: &Tensor, action: &Tensor) -> Result<Tensor> {
let xs = Tensor::cat(&[action, state], 1)?;
self.network.forward(&xs)
}
fn target_forward(&self, state: &Tensor, action: &Tensor) -> Result<Tensor> {
let xs = Tensor::cat(&[action, state], 1)?;
self.target_network.forward(&xs)
}
fn track(&mut self, tau: f64) -> Result<()> {
track(
&mut self.varmap,
&self.vb,
"target-critic",
"critic",
&self.dims,
tau,
)
}
}
#[allow(clippy::upper_case_acronyms)]
pub struct DDPG<'a> {
actor: Actor<'a>,
actor_optim: AdamW,
critic: Critic<'a>,
critic_optim: AdamW,
gamma: f64,
tau: f64,
replay_buffer: ReplayBuffer,
ou_noise: OuNoise,
size_state: usize,
size_action: usize,
pub train: bool,
}
impl DDPG<'_> {
#[allow(clippy::too_many_arguments)]
pub fn new(
device: &Device,
size_state: usize,
size_action: usize,
train: bool,
actor_lr: f64,
critic_lr: f64,
gamma: f64,
tau: f64,
buffer_capacity: usize,
ou_noise: OuNoise,
) -> Result<Self> {
let filter_by_prefix = |varmap: &VarMap, prefix: &str| {
varmap
.data()
.lock()
.unwrap()
.iter()
.filter_map(|(name, var)| name.starts_with(prefix).then_some(var.clone()))
.collect::<Vec<Var>>()
};
let actor = Actor::new(device, DType::F32, size_state, size_action)?;
let actor_optim = AdamW::new(
filter_by_prefix(&actor.varmap, "actor"),
ParamsAdamW {
lr: actor_lr,
..Default::default()
},
)?;
let critic = Critic::new(device, DType::F32, size_state, size_action)?;
let critic_optim = AdamW::new(
filter_by_prefix(&critic.varmap, "critic"),
ParamsAdamW {
lr: critic_lr,
..Default::default()
},
)?;
Ok(Self {
actor,
actor_optim,
critic,
critic_optim,
gamma,
tau,
replay_buffer: ReplayBuffer::new(buffer_capacity),
ou_noise,
size_state,
size_action,
train,
})
}
pub fn remember(
&mut self,
state: &Tensor,
action: &Tensor,
reward: &Tensor,
next_state: &Tensor,
terminated: bool,
truncated: bool,
) {
self.replay_buffer
.push(state, action, reward, next_state, terminated, truncated)
}
pub fn actions(&mut self, state: &Tensor) -> Result<f32> {
let actions = self
.actor
.forward(&state.detach()?.unsqueeze(0)?)?
.squeeze(0)?;
let actions = if self.train {
(actions + self.ou_noise.sample()?)?
} else {
actions
};
actions.squeeze(0)?.to_scalar::<f32>()
}
pub fn train(&mut self, batch_size: usize) -> Result<()> {
let (states, actions, rewards, next_states, _, _) =
match self.replay_buffer.random_batch(batch_size)? {
Some(v) => v,
_ => return Ok(()),
};
let q_target = self
.critic
.target_forward(&next_states, &self.actor.target_forward(&next_states)?)?;
let q_target = (rewards + (self.gamma * q_target)?.detach())?;
let q = self.critic.forward(&states, &actions)?;
let diff = (q_target - q)?;
let critic_loss = diff.sqr()?.mean_all()?;
self.critic_optim.backward_step(&critic_loss)?;
let actor_loss = self
.critic
.forward(&states, &self.actor.forward(&states)?)?
.mean_all()?
.neg()?;
self.actor_optim.backward_step(&actor_loss)?;
self.critic.track(self.tau)?;
self.actor.track(self.tau)?;
Ok(())
}
}
// The impact of the q value of the next state on the current state's q value.
const GAMMA: f64 = 0.99;
// The weight for updating the target networks.
const TAU: f64 = 0.005;
// The capacity of the replay buffer used for sampling training data.
const REPLAY_BUFFER_CAPACITY: usize = 100_000;
// The training batch size for each training iteration.
const TRAINING_BATCH_SIZE: usize = 100;
// The total number of episodes.
const MAX_EPISODES: usize = 100;
// The maximum length of an episode.
const EPISODE_LENGTH: usize = 200;
// The number of training iterations after one episode finishes.
const TRAINING_ITERATIONS: usize = 200;
// Ornstein-Uhlenbeck process parameters.
const MU: f64 = 0.0;
const THETA: f64 = 0.15;
const SIGMA: f64 = 0.1;
const ACTOR_LEARNING_RATE: f64 = 1e-4;
const CRITIC_LEARNING_RATE: f64 = 1e-3;
pub fn run() -> Result<()> {
let env = GymEnv::new("Pendulum-v1")?;
println!("action space: {}", env.action_space());
println!("observation space: {:?}", env.observation_space());
let size_state = env.observation_space().iter().product::<usize>();
let size_action = env.action_space();
let mut agent = DDPG::new(
&Device::Cpu,
size_state,
size_action,
true,
ACTOR_LEARNING_RATE,
CRITIC_LEARNING_RATE,
GAMMA,
TAU,
REPLAY_BUFFER_CAPACITY,
OuNoise::new(MU, THETA, SIGMA, size_action)?,
)?;
let mut rng = rand::thread_rng();
for episode in 0..MAX_EPISODES {
// let mut state = env.reset(episode as u64)?;
let mut state = env.reset(rng.gen::<u64>())?;
let mut total_reward = 0.0;
for _ in 0..EPISODE_LENGTH {
let mut action = 2.0 * agent.actions(&state)?;
action = action.clamp(-2.0, 2.0);
let step = env.step(vec![action])?;
total_reward += step.reward;
agent.remember(
&state,
&Tensor::new(vec![action], &Device::Cpu)?,
&Tensor::new(vec![step.reward as f32], &Device::Cpu)?,
&step.state,
step.terminated,
step.truncated,
);
if step.terminated || step.truncated {
break;
}
state = step.state;
}
println!("episode {episode} with total reward of {total_reward}");
for _ in 0..TRAINING_ITERATIONS {
agent.train(TRAINING_BATCH_SIZE)?;
}
}
println!("Testing...");
agent.train = false;
for episode in 0..10 {
// let mut state = env.reset(episode as u64)?;
let mut state = env.reset(rng.gen::<u64>())?;
let mut total_reward = 0.0;
for _ in 0..EPISODE_LENGTH {
let mut action = 2.0 * agent.actions(&state)?;
action = action.clamp(-2.0, 2.0);
let step = env.step(vec![action])?;
total_reward += step.reward;
if step.terminated || step.truncated {
break;
}
state = step.state;
}
println!("episode {episode} with total reward of {total_reward}");
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/vec_gym_env.rs | #![allow(unused)]
//! Vectorized version of the gym environment.
use candle::{DType, Device, Result, Tensor};
use pyo3::prelude::*;
use pyo3::types::PyDict;
#[derive(Debug)]
pub struct Step {
pub obs: Tensor,
pub reward: Tensor,
pub is_done: Tensor,
}
pub struct VecGymEnv {
env: PyObject,
action_space: usize,
observation_space: Vec<usize>,
}
fn w(res: PyErr) -> candle::Error {
candle::Error::wrap(res)
}
impl VecGymEnv {
pub fn new(name: &str, img_dir: Option<&str>, nprocesses: usize) -> Result<VecGymEnv> {
Python::with_gil(|py| {
let sys = py.import("sys")?;
let path = sys.getattr("path")?;
let _ = path.call_method1(
"append",
("candle-examples/examples/reinforcement-learning",),
)?;
let gym = py.import("atari_wrappers")?;
let make = gym.getattr("make")?;
let env = make.call1((name, img_dir, nprocesses))?;
let action_space = env.getattr("action_space")?;
let action_space = action_space.getattr("n")?.extract()?;
let observation_space = env.getattr("observation_space")?;
let observation_space: Vec<usize> = observation_space.getattr("shape")?.extract()?;
let observation_space =
[vec![nprocesses].as_slice(), observation_space.as_slice()].concat();
Ok(VecGymEnv {
env: env.into(),
action_space,
observation_space,
})
})
.map_err(w)
}
pub fn reset(&self) -> Result<Tensor> {
let obs = Python::with_gil(|py| {
let obs = self.env.call_method0(py, "reset")?;
let obs = obs.call_method0(py, "flatten")?;
obs.extract::<Vec<f32>>(py)
})
.map_err(w)?;
Tensor::new(obs, &Device::Cpu)?.reshape(self.observation_space.as_slice())
}
pub fn step(&self, action: Vec<usize>) -> Result<Step> {
let (obs, reward, is_done) = Python::with_gil(|py| {
let step = self.env.call_method(py, "step", (action,), None)?;
let step = step.as_ref(py);
let obs = step.get_item(0)?.call_method("flatten", (), None)?;
let obs_buffer = pyo3::buffer::PyBuffer::get(obs)?;
let obs: Vec<u8> = obs_buffer.to_vec(py)?;
let reward: Vec<f32> = step.get_item(1)?.extract()?;
let is_done: Vec<f32> = step.get_item(2)?.extract()?;
Ok((obs, reward, is_done))
})
.map_err(w)?;
let obs = Tensor::from_vec(obs, self.observation_space.as_slice(), &Device::Cpu)?
.to_dtype(DType::F32)?;
let reward = Tensor::new(reward, &Device::Cpu)?;
let is_done = Tensor::new(is_done, &Device::Cpu)?;
Ok(Step {
obs,
reward,
is_done,
})
}
pub fn action_space(&self) -> usize {
self.action_space
}
pub fn observation_space(&self) -> &[usize] {
&self.observation_space
}
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/reinforcement-learning/policy_gradient.rs | use super::gym_env::{GymEnv, Step};
use candle::{DType, Device, Error, Module, Result, Tensor};
use candle_nn::{
linear, ops::log_softmax, ops::softmax, sequential::seq, Activation, AdamW, Optimizer,
ParamsAdamW, VarBuilder, VarMap,
};
use rand::{distributions::Distribution, rngs::ThreadRng, Rng};
fn new_model(
input_shape: &[usize],
num_actions: usize,
dtype: DType,
device: &Device,
) -> Result<(impl Module, VarMap)> {
let input_size = input_shape.iter().product();
let mut varmap = VarMap::new();
let var_builder = VarBuilder::from_varmap(&varmap, dtype, device);
let model = seq()
.add(linear(input_size, 32, var_builder.pp("lin1"))?)
.add(Activation::Relu)
.add(linear(32, num_actions, var_builder.pp("lin2"))?);
Ok((model, varmap))
}
fn accumulate_rewards(steps: &[Step<i64>]) -> Vec<f64> {
let mut rewards: Vec<f64> = steps.iter().map(|s| s.reward).collect();
let mut acc_reward = 0f64;
for (i, reward) in rewards.iter_mut().enumerate().rev() {
if steps[i].terminated {
acc_reward = 0.0;
}
acc_reward += *reward;
*reward = acc_reward;
}
rewards
}
fn weighted_sample(probs: Vec<f32>, rng: &mut ThreadRng) -> Result<usize> {
let distribution = rand::distributions::WeightedIndex::new(probs).map_err(Error::wrap)?;
let mut rng = rng;
Ok(distribution.sample(&mut rng))
}
pub fn run() -> Result<()> {
let env = GymEnv::new("CartPole-v1")?;
println!("action space: {:?}", env.action_space());
println!("observation space: {:?}", env.observation_space());
let (model, varmap) = new_model(
env.observation_space(),
env.action_space(),
DType::F32,
&Device::Cpu,
)?;
let optimizer_params = ParamsAdamW {
lr: 0.01,
weight_decay: 0.01,
..Default::default()
};
let mut optimizer = AdamW::new(varmap.all_vars(), optimizer_params)?;
let mut rng = rand::thread_rng();
for epoch_idx in 0..100 {
let mut state = env.reset(rng.gen::<u64>())?;
let mut steps: Vec<Step<i64>> = vec![];
loop {
let action = {
let action_probs: Vec<f32> =
softmax(&model.forward(&state.detach()?.unsqueeze(0)?)?, 1)?
.squeeze(0)?
.to_vec1()?;
weighted_sample(action_probs, &mut rng)? as i64
};
let step = env.step(action)?;
steps.push(step.copy_with_obs(&state));
if step.terminated || step.truncated {
state = env.reset(rng.gen::<u64>())?;
if steps.len() > 5000 {
break;
}
} else {
state = step.state;
}
}
let total_reward: f64 = steps.iter().map(|s| s.reward).sum();
let episodes: i64 = steps
.iter()
.map(|s| (s.terminated || s.truncated) as i64)
.sum();
println!(
"epoch: {:<3} episodes: {:<5} avg reward per episode: {:.2}",
epoch_idx,
episodes,
total_reward / episodes as f64
);
let batch_size = steps.len();
let rewards = Tensor::from_vec(accumulate_rewards(&steps), batch_size, &Device::Cpu)?
.to_dtype(DType::F32)?
.detach()?;
let actions_mask = {
let actions: Vec<i64> = steps.iter().map(|s| s.action).collect();
let actions_mask: Vec<Tensor> = actions
.iter()
.map(|&action| {
// One-hot encoding
let mut action_mask = vec![0.0; env.action_space()];
action_mask[action as usize] = 1.0;
Tensor::from_vec(action_mask, env.action_space(), &Device::Cpu)
.unwrap()
.to_dtype(DType::F32)
.unwrap()
})
.collect();
Tensor::stack(&actions_mask, 0)?.detach()?
};
let states = {
let states: Vec<Tensor> = steps.into_iter().map(|s| s.state).collect();
Tensor::stack(&states, 0)?.detach()?
};
let log_probs = actions_mask
.mul(&log_softmax(&model.forward(&states)?, 1)?)?
.sum(1)?;
let loss = rewards.mul(&log_probs)?.neg()?.mean_all()?;
optimizer.backward_step(&loss)?;
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/vgg/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, IndexOp, D};
use candle_nn::{ModuleT, VarBuilder};
use candle_transformers::models::vgg::{Models, Vgg};
use clap::{Parser, ValueEnum};
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
Vgg13,
Vgg16,
Vgg19,
}
#[derive(Parser)]
struct Args {
#[arg(long)]
image: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Variant of the model to use.
#[arg(value_enum, long, default_value_t = Which::Vgg13)]
which: Which,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let image = candle_examples::imagenet::load_image224(args.image)?;
println!("loaded image {image:?}");
let api = hf_hub::api::sync::Api::new()?;
let repo = match args.which {
Which::Vgg13 => "timm/vgg13.tv_in1k",
Which::Vgg16 => "timm/vgg16.tv_in1k",
Which::Vgg19 => "timm/vgg19.tv_in1k",
};
let api = api.model(repo.into());
let filename = "model.safetensors";
let model_file = api.get(filename)?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let model = match args.which {
Which::Vgg13 => Vgg::new(vb, Models::Vgg13)?,
Which::Vgg16 => Vgg::new(vb, Models::Vgg16)?,
Which::Vgg19 => Vgg::new(vb, Models::Vgg19)?,
};
let logits = model.forward_t(&image, /*train=*/ false)?;
let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
.i(0)?
.to_vec1::<f32>()?;
// Sort the predictions and take the top 5
let mut top: Vec<_> = prs.iter().enumerate().collect();
top.sort_by(|a, b| b.1.partial_cmp(a.1).unwrap());
let top = top.into_iter().take(5).collect::<Vec<_>>();
// Print the top predictions
for &(i, p) in &top {
println!(
"{:50}: {:.2}%",
candle_examples::imagenet::CLASSES[i],
p * 100.0
);
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/vgg/README.md | ## VGG Model Implementation
This example demonstrates the implementation of VGG models (VGG13, VGG16, VGG19) using the Candle library.
The VGG models are defined in `candle-transformers/src/models/vgg.rs`. The main function in `candle-examples/examples/vgg/main.rs` loads an image, selects the VGG model based on the provided argument, and applies the model to the loaded image.
You can run the example with the following command:
```bash
cargo run --example vgg --release -- --image ../yolo-v8/assets/bike.jpg --which vgg13
```
In the command above, `--image` specifies the path to the image file and `--which` specifies the VGG model to use (vgg13, vgg16, or vgg19).
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mixtral/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::mixtral::{Config, Model};
use candle::{DType, Device, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
struct TextGeneration {
model: Model,
device: Device,
tokenizer: TokenOutputStream,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
}
impl TextGeneration {
#[allow(clippy::too_many_arguments)]
fn new(
model: Model,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
top_p: Option<f64>,
repeat_penalty: f32,
repeat_last_n: usize,
device: &Device,
) -> Self {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer: TokenOutputStream::new(tokenizer),
logits_processor,
repeat_penalty,
repeat_last_n,
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
self.tokenizer.clear();
let mut tokens = self
.tokenizer
.tokenizer()
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
for &t in tokens.iter() {
if let Some(t) = self.tokenizer.next_token(t)? {
print!("{t}")
}
}
std::io::stdout().flush()?;
let mut generated_tokens = 0usize;
let eos_token = match self.tokenizer.get_token("</s>") {
Some(token) => token,
None => anyhow::bail!("cannot find the </s> token"),
};
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let context_size = if index > 0 { 1 } else { tokens.len() };
let start_pos = tokens.len().saturating_sub(context_size);
let ctxt = &tokens[start_pos..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = self.model.forward(&input, start_pos)?;
let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(self.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
generated_tokens += 1;
if next_token == eos_token {
break;
}
if let Some(t) = self.tokenizer.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
}
let dt = start_gen.elapsed();
if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
std::io::stdout().flush()?;
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
generated_tokens as f64 / dt.as_secs_f64(),
);
Ok(())
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
use_flash_attn: bool,
#[arg(long)]
prompt: String,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, short = 'n', default_value_t = 100)]
sample_len: usize,
#[arg(long, default_value = "mistralai/Mixtral-8x7B-v0.1")]
model_id: String,
#[arg(long, default_value = "main")]
revision: String,
#[arg(long)]
tokenizer_file: Option<String>,
#[arg(long)]
weight_files: Option<String>,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
println!(
"avx: {}, neon: {}, simd128: {}, f16c: {}",
candle::utils::with_avx(),
candle::utils::with_neon(),
candle::utils::with_simd128(),
candle::utils::with_f16c()
);
println!(
"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
args.temperature.unwrap_or(0.),
args.repeat_penalty,
args.repeat_last_n
);
let start = std::time::Instant::now();
let api = Api::new()?;
let repo = api.repo(Repo::with_revision(
args.model_id,
RepoType::Model,
args.revision,
));
let tokenizer_filename = match args.tokenizer_file {
Some(file) => std::path::PathBuf::from(file),
None => repo.get("tokenizer.json")?,
};
let filenames = match args.weight_files {
Some(files) => files
.split(',')
.map(std::path::PathBuf::from)
.collect::<Vec<_>>(),
None => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?,
};
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let config = Config::v0_1_8x7b(args.use_flash_attn);
let device = candle_examples::device(args.cpu)?;
let dtype = if device.is_cuda() {
DType::BF16
} else {
DType::F32
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
let model = Model::new(&config, vb)?;
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(
model,
tokenizer,
args.seed,
args.temperature,
args.top_p,
args.repeat_penalty,
args.repeat_last_n,
&device,
);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mixtral/README.md | # candle-mixtral: 8x7b LLM using a sparse mixture of experts.
Mixtral-8x7B-v0.1 is a pretrained generative LLM with 56 billion parameters.
- [Blog post](https://mistral.ai/news/mixtral-of-experts/) from Mistral announcing the model release.
- [Model card](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the HuggingFace Hub.
## Running the example
```bash
$ cargo run --example mixtral --release -- --prompt "def print_prime(n): "
def print_prime(n): # n is the number of prime numbers to be printed
i = 2
count = 0
while (count < n):
if (isPrime(i)):
print(i)
count += 1
i += 1
def isPrime(n):
for x in range(2, int(n**0.5)+1):
if (n % x == 0):
...
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/onnx/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{IndexOp, D};
use clap::{Parser, ValueEnum};
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
SqueezeNet,
EfficientNet,
}
#[derive(Parser)]
struct Args {
#[arg(long)]
image: String,
#[arg(long)]
model: Option<String>,
/// The model to be used.
#[arg(value_enum, long, default_value_t = Which::SqueezeNet)]
which: Which,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let image = candle_examples::imagenet::load_image224(args.image)?;
let image = match args.which {
Which::SqueezeNet => image,
Which::EfficientNet => image.permute((1, 2, 0))?,
};
println!("loaded image {image:?}");
let model = match args.model {
Some(model) => std::path::PathBuf::from(model),
None => match args.which {
Which::SqueezeNet => hf_hub::api::sync::Api::new()?
.model("lmz/candle-onnx".into())
.get("squeezenet1.1-7.onnx")?,
Which::EfficientNet => hf_hub::api::sync::Api::new()?
.model("onnx/EfficientNet-Lite4".into())
.get("efficientnet-lite4-11.onnx")?,
},
};
let model = candle_onnx::read_file(model)?;
let graph = model.graph.as_ref().unwrap();
let mut inputs = std::collections::HashMap::new();
inputs.insert(graph.input[0].name.to_string(), image.unsqueeze(0)?);
let mut outputs = candle_onnx::simple_eval(&model, inputs)?;
let output = outputs.remove(&graph.output[0].name).unwrap();
let prs = match args.which {
Which::SqueezeNet => candle_nn::ops::softmax(&output, D::Minus1)?,
Which::EfficientNet => output,
};
let prs = prs.i(0)?.to_vec1::<f32>()?;
// Sort the predictions and take the top 5
let mut top: Vec<_> = prs.iter().enumerate().collect();
top.sort_by(|a, b| b.1.partial_cmp(a.1).unwrap());
let top = top.into_iter().take(5).collect::<Vec<_>>();
// Print the top predictions
for &(i, p) in &top {
println!(
"{:50}: {:.2}%",
candle_examples::imagenet::CLASSES[i],
p * 100.0
);
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/onnx/README.md | ## Using ONNX models in Candle
This example demonstrates how to run ONNX based models in Candle, the model
being used here is a small sequeezenet variant.
You can run the example with the following command:
```bash
cargo run --example squeezenet-onnx --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/phi/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::{Parser, ValueEnum};
use candle_transformers::models::mixformer::{Config, MixFormerSequentialForCausalLM as MixFormer};
use candle_transformers::models::phi::{Config as PhiConfig, Model as Phi};
use candle_transformers::models::quantized_mixformer::MixFormerSequentialForCausalLM as QMixFormer;
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
enum Model {
MixFormer(MixFormer),
Phi(Phi),
Quantized(QMixFormer),
}
struct TextGeneration {
model: Model,
device: Device,
tokenizer: Tokenizer,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
verbose_prompt: bool,
}
impl TextGeneration {
#[allow(clippy::too_many_arguments)]
fn new(
model: Model,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
top_p: Option<f64>,
repeat_penalty: f32,
repeat_last_n: usize,
verbose_prompt: bool,
device: &Device,
) -> Self {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer,
logits_processor,
repeat_penalty,
repeat_last_n,
verbose_prompt,
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
println!("starting the inference loop");
let tokens = self.tokenizer.encode(prompt, true).map_err(E::msg)?;
if tokens.is_empty() {
anyhow::bail!("Empty prompts are not supported in the phi model.")
}
if self.verbose_prompt {
for (token, id) in tokens.get_tokens().iter().zip(tokens.get_ids().iter()) {
let token = token.replace('▁', " ").replace("<0x0A>", "\n");
println!("{id:7} -> '{token}'");
}
}
let mut tokens = tokens.get_ids().to_vec();
let mut generated_tokens = 0usize;
let eos_token = match self.tokenizer.get_vocab(true).get("<|endoftext|>") {
Some(token) => *token,
None => anyhow::bail!("cannot find the endoftext token"),
};
print!("{prompt}");
std::io::stdout().flush()?;
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let context_size = if index > 0 { 1 } else { tokens.len() };
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = match &mut self.model {
Model::MixFormer(m) => m.forward(&input)?,
Model::Phi(m) => m.forward(&input)?,
Model::Quantized(m) => m.forward(&input)?,
};
let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(self.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
generated_tokens += 1;
if next_token == eos_token {
break;
}
let token = self.tokenizer.decode(&[next_token], true).map_err(E::msg)?;
print!("{token}");
std::io::stdout().flush()?;
}
let dt = start_gen.elapsed();
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
generated_tokens as f64 / dt.as_secs_f64(),
);
Ok(())
}
}
#[derive(Clone, Copy, Debug, ValueEnum, PartialEq, Eq)]
enum WhichModel {
#[value(name = "1")]
V1,
#[value(name = "1.5")]
V1_5,
#[value(name = "2")]
V2,
#[value(name = "2-old")]
V2Old,
PuffinPhiV2,
PhiHermes,
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
/// Display the token for the specified prompt.
#[arg(long)]
verbose_prompt: bool,
#[arg(long)]
prompt: Option<String>,
#[arg(long)]
mmlu_dir: Option<String>,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, short = 'n', default_value_t = 5000)]
sample_len: usize,
#[arg(long)]
model_id: Option<String>,
#[arg(long, default_value = "2")]
model: WhichModel,
#[arg(long)]
revision: Option<String>,
#[arg(long)]
weight_file: Option<String>,
#[arg(long)]
tokenizer: Option<String>,
#[arg(long)]
quantized: bool,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
println!(
"avx: {}, neon: {}, simd128: {}, f16c: {}",
candle::utils::with_avx(),
candle::utils::with_neon(),
candle::utils::with_simd128(),
candle::utils::with_f16c()
);
println!(
"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
args.temperature.unwrap_or(0.),
args.repeat_penalty,
args.repeat_last_n
);
let start = std::time::Instant::now();
let api = Api::new()?;
let model_id = match args.model_id {
Some(model_id) => model_id.to_string(),
None => {
if args.quantized {
"lmz/candle-quantized-phi".to_string()
} else {
match args.model {
WhichModel::V1 => "microsoft/phi-1".to_string(),
WhichModel::V1_5 => "microsoft/phi-1_5".to_string(),
WhichModel::V2 | WhichModel::V2Old => "microsoft/phi-2".to_string(),
WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => {
"lmz/candle-quantized-phi".to_string()
}
}
}
}
};
let revision = match args.revision {
Some(rev) => rev.to_string(),
None => {
if args.quantized {
"main".to_string()
} else {
match args.model {
WhichModel::V1 => "refs/pr/8".to_string(),
WhichModel::V1_5 => "refs/pr/73".to_string(),
WhichModel::V2Old => "834565c23f9b28b96ccbeabe614dd906b6db551a".to_string(),
WhichModel::V2 | WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => {
"main".to_string()
}
}
}
}
};
let repo = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
let tokenizer_filename = match args.tokenizer {
Some(file) => std::path::PathBuf::from(file),
None => match args.model {
WhichModel::V1 | WhichModel::V1_5 | WhichModel::V2 | WhichModel::V2Old => {
repo.get("tokenizer.json")?
}
WhichModel::PuffinPhiV2 | WhichModel::PhiHermes => {
repo.get("tokenizer-puffin-phi-v2.json")?
}
},
};
let filenames = match args.weight_file {
Some(weight_file) => vec![std::path::PathBuf::from(weight_file)],
None => {
if args.quantized {
match args.model {
WhichModel::V1 => vec![repo.get("model-v1-q4k.gguf")?],
WhichModel::V1_5 => vec![repo.get("model-q4k.gguf")?],
WhichModel::V2 | WhichModel::V2Old => vec![repo.get("model-v2-q4k.gguf")?],
WhichModel::PuffinPhiV2 => vec![repo.get("model-puffin-phi-v2-q4k.gguf")?],
WhichModel::PhiHermes => vec![repo.get("model-phi-hermes-1_3B-q4k.gguf")?],
}
} else {
match args.model {
WhichModel::V1 | WhichModel::V1_5 => vec![repo.get("model.safetensors")?],
WhichModel::V2 | WhichModel::V2Old => candle_examples::hub_load_safetensors(
&repo,
"model.safetensors.index.json",
)?,
WhichModel::PuffinPhiV2 => vec![repo.get("model-puffin-phi-v2.safetensors")?],
WhichModel::PhiHermes => vec![repo.get("model-phi-hermes-1_3B.safetensors")?],
}
}
}
};
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let config = || match args.model {
WhichModel::V1 => Config::v1(),
WhichModel::V1_5 => Config::v1_5(),
WhichModel::V2 | WhichModel::V2Old => Config::v2(),
WhichModel::PuffinPhiV2 => Config::puffin_phi_v2(),
WhichModel::PhiHermes => Config::phi_hermes_1_3b(),
};
let device = candle_examples::device(args.cpu)?;
let model = if args.quantized {
let config = config();
let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
&filenames[0],
&device,
)?;
let model = match args.model {
WhichModel::V2 | WhichModel::V2Old => QMixFormer::new_v2(&config, vb)?,
_ => QMixFormer::new(&config, vb)?,
};
Model::Quantized(model)
} else {
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
match args.model {
WhichModel::V1 | WhichModel::V1_5 | WhichModel::V2 => {
let config_filename = repo.get("config.json")?;
let config = std::fs::read_to_string(config_filename)?;
let config: PhiConfig = serde_json::from_str(&config)?;
let phi = Phi::new(&config, vb)?;
Model::Phi(phi)
}
WhichModel::V2Old => {
let config = config();
Model::MixFormer(MixFormer::new_v2(&config, vb)?)
}
WhichModel::PhiHermes | WhichModel::PuffinPhiV2 => {
let config = config();
Model::MixFormer(MixFormer::new(&config, vb)?)
}
}
};
println!("loaded the model in {:?}", start.elapsed());
match (args.prompt, args.mmlu_dir) {
(None, None) | (Some(_), Some(_)) => {
anyhow::bail!("exactly one of --prompt and --mmlu-dir must be specified")
}
(Some(prompt), None) => {
let mut pipeline = TextGeneration::new(
model,
tokenizer,
args.seed,
args.temperature,
args.top_p,
args.repeat_penalty,
args.repeat_last_n,
args.verbose_prompt,
&device,
);
pipeline.run(&prompt, args.sample_len)?;
}
(None, Some(mmlu_dir)) => mmlu(model, tokenizer, &device, mmlu_dir)?,
}
Ok(())
}
fn mmlu<P: AsRef<std::path::Path>>(
mut model: Model,
tokenizer: Tokenizer,
device: &Device,
mmlu_dir: P,
) -> anyhow::Result<()> {
for dir_entry in mmlu_dir.as_ref().read_dir()?.flatten() {
let dir_entry = dir_entry.path();
let theme = match dir_entry.file_stem().and_then(|v| v.to_str()) {
None => "".to_string(),
Some(v) => match v.strip_suffix("_test") {
None => v.replace('_', " "),
Some(v) => v.replace('_', " "),
},
};
if dir_entry.extension().as_ref().and_then(|v| v.to_str()) != Some("csv") {
continue;
}
println!("reading {dir_entry:?}");
let dir_entry = std::fs::File::open(dir_entry)?;
let mut reader = csv::ReaderBuilder::new()
.has_headers(false)
.from_reader(dir_entry);
let token_a = tokenizer.token_to_id("A").unwrap();
let token_b = tokenizer.token_to_id("B").unwrap();
let token_c = tokenizer.token_to_id("C").unwrap();
let token_d = tokenizer.token_to_id("D").unwrap();
for row in reader.records() {
let row = match row {
Err(_) => continue,
Ok(row) => row,
};
if row.len() < 5 {
continue;
}
let question = row.get(0).unwrap();
let answer_a = row.get(1).unwrap();
let answer_b = row.get(2).unwrap();
let answer_c = row.get(3).unwrap();
let answer_d = row.get(4).unwrap();
let answer = row.get(5).unwrap();
let prompt = format!(
"{} {theme}.\n{question}\nA. {answer_a}\nB. {answer_b}\nC. {answer_c}\nD. {answer_d}\nAnswer:\n",
"The following are multiple choice questions (with answers) about"
);
let tokens = tokenizer.encode(prompt.as_str(), true).map_err(E::msg)?;
let tokens = tokens.get_ids().to_vec();
let input = Tensor::new(tokens, device)?.unsqueeze(0)?;
let logits = match &mut model {
Model::MixFormer(m) => {
m.clear_kv_cache();
m.forward(&input)?
}
Model::Phi(m) => {
m.clear_kv_cache();
m.forward(&input)?
}
Model::Quantized(m) => {
m.clear_kv_cache();
m.forward(&input)?
}
};
let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
let logits_v: Vec<f32> = logits.to_vec1()?;
let pr_a = logits_v[token_a as usize];
let pr_b = logits_v[token_b as usize];
let pr_c = logits_v[token_c as usize];
let pr_d = logits_v[token_d as usize];
let model_answer = if pr_a > pr_b && pr_a > pr_c && pr_a > pr_d {
"A"
} else if pr_b > pr_c && pr_b > pr_d {
"B"
} else if pr_c > pr_d {
"C"
} else {
"D"
};
println!("{prompt}\n -> {model_answer} vs {answer}");
}
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/phi/README.md | # candle-phi: 1.3b and 2.7b LLM with state of the art performance for <10b models.
[Phi-1.5](https://huggingface.co/microsoft/phi-1_5) and
[Phi-2](https://huggingface.co/microsoft/phi-2) are language models using
only 1.3 and 2.7 billion parameters but with state of the art performance compared to
models with up to 10 billion parameters.
The candle implementation provides both the standard version as well as a
quantized variant.
## Running some examples
For the v2 version.
```bash
$ cargo run --example phi --release -- --model 2 \
--prompt "A skier slides down a frictionless slope of height 40m and length 80m. What's the skier speed at the bottom?"
A skier slides down a frictionless slope of height 40m and length 80m. What's the skier speed at the bottom?
Solution:
The potential energy of the skier is converted into kinetic energy as it slides down the slope. The formula for potential energy is mgh, where m is mass, g is acceleration due to gravity (9.8 m/s^2), and h is height. Since there's no friction, all the potential energy is converted into kinetic energy at the bottom of the slope. The formula for kinetic energy is 1/2mv^2, where v is velocity. We can equate these two formulas:
mgh = 1/2mv^2
Solving for v, we get:
v = sqrt(2gh)
Substituting the given values, we get:
v = sqrt(2*9.8*40) = 28 m/s
Therefore, the skier speed at the bottom of the slope is 28 m/s.
```
For the v1.5 version.
```bash
$ cargo run --example phi --release -- --prompt "def print_prime(n): "
def print_prime(n):
print("Printing prime numbers")
for i in range(2, n+1):
if is_prime(i):
print(i)
def is_prime(n):
if n <= 1:
return False
for i in range(2, int(math.sqrt(n))+1):
if n % i == 0:
return False
return True
$ cargo run --example phi --release -- \
--prompt "Explain how to find the median of an array and write the corresponding python function.\nAnswer:" \
--quantized --sample-len 200
Explain how to find the median of an array and write the corresponding python function.
Answer: The median is the middle value in an array. If the array has an even number of elements, the median is the average of the two middle values.
def median(arr):
arr.sort()
n = len(arr)
if n % 2 == 0:
return (arr[n//2 - 1] + arr[n//2]) / 2
else:
return arr[n//2]
```
This also supports the [Puffin Phi v2
model](https://huggingface.co/teknium/Puffin-Phi-v2) for human interaction.
```
$ cargo run --example phi --release -- \
--prompt "USER: What would you do on a sunny day in Paris?\nASSISTANT:" \
--sample-len 200 --model puffin-phi-v2 --quantized
USER: What would you do on a sunny day in Paris?
ASSISTANT: On a sunny day in Paris, you could visit the Musée du Louvre to admire the famous
painting "Mona Lisa" by Leonardo da Vinci. You might also want to stroll along the Champs-Élysées
and enjoy the beautiful architecture of the buildings around you. Don't forget to stop by a café
for a cup of coffee and to soak up the sun!"
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/repvgg/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::repvgg;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
A0,
A1,
A2,
B0,
B1,
B2,
B3,
B1G4,
B2G4,
B3G4,
}
impl Which {
fn model_filename(&self) -> String {
let name = match self {
Self::A0 => "a0",
Self::A1 => "a1",
Self::A2 => "a2",
Self::B0 => "b0",
Self::B1 => "b1",
Self::B2 => "b2",
Self::B3 => "b3",
Self::B1G4 => "b1g4",
Self::B2G4 => "b2g4",
Self::B3G4 => "b3g4",
};
format!("timm/repvgg_{}.rvgg_in1k", name)
}
fn config(&self) -> repvgg::Config {
match self {
Self::A0 => repvgg::Config::a0(),
Self::A1 => repvgg::Config::a1(),
Self::A2 => repvgg::Config::a2(),
Self::B0 => repvgg::Config::b0(),
Self::B1 => repvgg::Config::b1(),
Self::B2 => repvgg::Config::b2(),
Self::B3 => repvgg::Config::b3(),
Self::B1G4 => repvgg::Config::b1g4(),
Self::B2G4 => repvgg::Config::b2g4(),
Self::B3G4 => repvgg::Config::b3g4(),
}
}
}
#[derive(Parser)]
struct Args {
#[arg(long)]
model: Option<String>,
#[arg(long)]
image: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
#[arg(value_enum, long, default_value_t=Which::A0)]
which: Which,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let image = candle_examples::imagenet::load_image224(args.image)?;
println!("loaded image {image:?}");
let model_file = match args.model {
None => {
let model_name = args.which.model_filename();
let api = hf_hub::api::sync::Api::new()?;
let api = api.model(model_name);
api.get("model.safetensors")?
}
Some(model) => model.into(),
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let model = repvgg::repvgg(&args.which.config(), 1000, vb)?;
println!("model built");
let logits = model.forward(&image.unsqueeze(0)?)?;
let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
.i(0)?
.to_vec1::<f32>()?;
let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
for &(category_idx, pr) in prs.iter().take(5) {
println!(
"{:24}: {:.2}%",
candle_examples::imagenet::CLASSES[category_idx],
100. * pr
);
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/repvgg/README.md | # candle-repvgg
[RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697).
This candle implementation uses a pre-trained RepVGG network for inference. The
classification head has been trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Running an example
```
$ cargo run --example repvgg --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
loaded image Tensor[dims 3, 224, 224; f32]
model built
mountain bike, all-terrain bike, off-roader: 61.70%
bicycle-built-for-two, tandem bicycle, tandem: 33.14%
unicycle, monocycle : 4.88%
crash helmet : 0.15%
moped : 0.04%
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/trocr/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Error as E;
use clap::{Parser, ValueEnum};
use candle::{DType, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::models::trocr;
use tokenizers::Tokenizer;
mod image_processor;
#[derive(Clone, Debug, Copy, ValueEnum)]
enum Which {
Base,
Large,
}
#[derive(Parser, Debug)]
struct Args {
#[arg(long)]
model: Option<String>,
/// Choose the variant of the model to run.
#[arg(long, default_value = "base")]
which: Which,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Text to be translated
#[arg(long)]
image: String,
}
pub fn main() -> anyhow::Result<()> {
use hf_hub::api::sync::Api;
let args = Args::parse();
let tokenizer_dec = {
let tokenizer = Api::new()?
.model(String::from("ToluClassics/candle-trocr-tokenizer"))
.get("tokenizer.json")?;
Tokenizer::from_file(&tokenizer).map_err(E::msg)?
};
let mut tokenizer_dec = TokenOutputStream::new(tokenizer_dec);
let device = candle_examples::device(args.cpu)?;
let vb = {
let model = match args.model {
Some(model) => std::path::PathBuf::from(model),
None => match args.which {
Which::Base => Api::new()?
.repo(hf_hub::Repo::with_revision(
"microsoft/trocr-base-handwritten".to_string(),
hf_hub::RepoType::Model,
"refs/pr/3".to_string(),
))
.get("model.safetensors")?,
Which::Large => Api::new()?
.repo(hf_hub::Repo::with_revision(
"microsoft/trocr-large-handwritten".to_string(),
hf_hub::RepoType::Model,
"refs/pr/6".to_string(),
))
.get("model.safetensors")?,
},
};
println!("model: {:?}", model);
unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? }
};
let encoder_config = match args.which {
Which::Base => candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten(),
Which::Large => {
candle_transformers::models::vit::Config::microsoft_trocr_base_handwritten()
}
};
let decoder_config = trocr::TrOCRConfig::default();
let mut model = trocr::TrOCRModel::new(&encoder_config, &decoder_config, vb)?;
let config = image_processor::ProcessorConfig::default();
let processor = image_processor::ViTImageProcessor::new(&config);
let image = vec![args.image.as_str()];
let image = processor.preprocess(image)?;
let encoder_xs = model.encoder().forward(&image)?;
let mut logits_processor =
candle_transformers::generation::LogitsProcessor::new(1337, None, None);
let mut token_ids: Vec<u32> = vec![decoder_config.decoder_start_token_id];
for index in 0..1000 {
let context_size = if index >= 1 { 1 } else { token_ids.len() };
let start_pos = token_ids.len().saturating_sub(context_size);
let input_ids = Tensor::new(&token_ids[start_pos..], &device)?.unsqueeze(0)?;
let logits = model.decode(&input_ids, &encoder_xs, start_pos)?;
let logits = logits.squeeze(0)?;
let logits = logits.get(logits.dim(0)? - 1)?;
let token = logits_processor.sample(&logits)?;
token_ids.push(token);
if let Some(t) = tokenizer_dec.next_token(token)? {
use std::io::Write;
print!("{t}");
std::io::stdout().flush()?;
}
if token == decoder_config.eos_token_id {
break;
}
}
if let Some(rest) = tokenizer_dec.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
println!();
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/trocr/readme.md | # candle-trocr
`TrOCR` is a transformer OCR Model. In this example it is used to
transcribe image text. See the associated [model
card](https://huggingface.co/microsoft/trocr-base-printed) for details on
the model itself.
## Running an example
```bash
cargo run --example trocr --release -- --which base --cpu --image candle-examples/examples/trocr/assets/trocr.png
```
```
<s> industry , Mr. Brown commented icily . " Let us have a</s>
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/trocr/image_processor.rs | use image::{DynamicImage, ImageBuffer};
use serde::Deserialize;
use std::collections::HashMap;
use candle::{DType, Device, Result, Tensor};
#[derive(Debug, Clone, PartialEq, Deserialize)]
pub struct ProcessorConfig {
do_resize: bool,
height: u32,
width: u32,
do_rescale: bool,
do_normalize: bool,
image_mean: Vec<f32>,
image_std: Vec<f32>,
}
impl Default for ProcessorConfig {
fn default() -> Self {
Self {
do_resize: true,
height: 384,
width: 384,
do_rescale: true,
do_normalize: true,
image_mean: vec![0.5, 0.5, 0.5],
image_std: vec![0.5, 0.5, 0.5],
}
}
}
pub struct ViTImageProcessor {
do_resize: bool,
height: u32,
width: u32,
do_normalize: bool,
image_mean: Vec<f32>,
image_std: Vec<f32>,
}
impl ViTImageProcessor {
pub fn new(config: &ProcessorConfig) -> Self {
Self {
do_resize: config.do_resize,
height: config.height,
width: config.width,
do_normalize: config.do_normalize,
image_mean: config.image_mean.clone(),
image_std: config.image_std.clone(),
}
}
pub fn preprocess(&self, images: Vec<&str>) -> Result<Tensor> {
let height = self.height as usize;
let width = self.width as usize;
let channels = 3;
let images = self.load_images(images)?;
let resized_images: Vec<DynamicImage> = if self.do_resize {
images
.iter()
.map(|image| self.resize(image.clone(), None).unwrap())
.collect()
} else {
images
};
let normalized_images: Vec<Tensor> = if self.do_normalize {
resized_images
.iter()
.map(|image| self.normalize(image.clone(), None, None).unwrap())
.collect()
} else {
let resized_images: Vec<ImageBuffer<image::Rgb<u8>, Vec<u8>>> =
resized_images.iter().map(|image| image.to_rgb8()).collect();
let data = resized_images
.into_iter()
.map(|image| image.into_raw())
.collect::<Vec<Vec<u8>>>();
data.iter()
.map(|image| {
Tensor::from_vec(image.clone(), (height, width, channels), &Device::Cpu)
.unwrap()
.permute((2, 0, 1))
.unwrap()
})
.collect::<Vec<Tensor>>()
};
Tensor::stack(&normalized_images, 0)
}
fn resize(
&self,
image: image::DynamicImage,
size: Option<HashMap<String, u32>>,
) -> Result<image::DynamicImage> {
let (height, width) = match &size {
Some(size) => (size.get("height").unwrap(), size.get("width").unwrap()),
None => (&self.height, &self.width),
};
let resized_image =
image.resize_exact(*width, *height, image::imageops::FilterType::Triangle);
Ok(resized_image)
}
fn normalize(
&self,
image: image::DynamicImage,
mean: Option<Vec<f32>>,
std: Option<Vec<f32>>,
) -> Result<Tensor> {
let mean = match mean {
Some(mean) => mean,
None => self.image_mean.clone(),
};
let std = match std {
Some(std) => std,
None => self.image_std.clone(),
};
let mean = Tensor::from_vec(mean, (3, 1, 1), &Device::Cpu)?;
let std = Tensor::from_vec(std, (3, 1, 1), &Device::Cpu)?;
let image = image.to_rgb8();
let data = image.into_raw();
let height = self.height as usize;
let width = self.width as usize;
let channels = 3;
let data =
Tensor::from_vec(data, &[height, width, channels], &Device::Cpu)?.permute((2, 0, 1))?;
(data.to_dtype(DType::F32)? / 255.)?
.broadcast_sub(&mean)?
.broadcast_div(&std)
}
pub fn load_images(&self, image_path: Vec<&str>) -> Result<Vec<image::DynamicImage>> {
let mut images: Vec<image::DynamicImage> = Vec::new();
for path in image_path {
let img = image::io::Reader::open(path)?.decode().unwrap();
images.push(img);
}
Ok(images)
}
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/whisper/main.rs | // https://github.com/openai/whisper/blob/main/whisper/model.py/rgs
// TODO:
// - Batch size greater than 1.
// - More token filters (SuppressBlanks, ApplyTimestampRules).
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{Error as E, Result};
use candle::{Device, IndexOp, Tensor};
use candle_nn::{ops::softmax, VarBuilder};
use clap::{Parser, ValueEnum};
use hf_hub::{api::sync::Api, Repo, RepoType};
use rand::{distributions::Distribution, SeedableRng};
use tokenizers::Tokenizer;
mod multilingual;
use candle_transformers::models::whisper::{self as m, audio, Config};
pub enum Model {
Normal(m::model::Whisper),
Quantized(m::quantized_model::Whisper),
}
// Maybe we should use some traits rather than doing the dispatch for all these.
impl Model {
pub fn config(&self) -> &Config {
match self {
Self::Normal(m) => &m.config,
Self::Quantized(m) => &m.config,
}
}
pub fn encoder_forward(&mut self, x: &Tensor, flush: bool) -> candle::Result<Tensor> {
match self {
Self::Normal(m) => m.encoder.forward(x, flush),
Self::Quantized(m) => m.encoder.forward(x, flush),
}
}
pub fn decoder_forward(
&mut self,
x: &Tensor,
xa: &Tensor,
flush: bool,
) -> candle::Result<Tensor> {
match self {
Self::Normal(m) => m.decoder.forward(x, xa, flush),
Self::Quantized(m) => m.decoder.forward(x, xa, flush),
}
}
pub fn decoder_final_linear(&self, x: &Tensor) -> candle::Result<Tensor> {
match self {
Self::Normal(m) => m.decoder.final_linear(x),
Self::Quantized(m) => m.decoder.final_linear(x),
}
}
}
#[allow(dead_code)]
#[derive(Debug, Clone)]
struct DecodingResult {
tokens: Vec<u32>,
text: String,
avg_logprob: f64,
no_speech_prob: f64,
temperature: f64,
compression_ratio: f64,
}
#[allow(dead_code)]
#[derive(Debug, Clone)]
struct Segment {
start: f64,
duration: f64,
dr: DecodingResult,
}
struct Decoder {
model: Model,
rng: rand::rngs::StdRng,
task: Option<Task>,
timestamps: bool,
verbose: bool,
tokenizer: Tokenizer,
suppress_tokens: Tensor,
sot_token: u32,
transcribe_token: u32,
translate_token: u32,
eot_token: u32,
no_speech_token: u32,
no_timestamps_token: u32,
language_token: Option<u32>,
}
impl Decoder {
#[allow(clippy::too_many_arguments)]
fn new(
model: Model,
tokenizer: Tokenizer,
seed: u64,
device: &Device,
language_token: Option<u32>,
task: Option<Task>,
timestamps: bool,
verbose: bool,
) -> Result<Self> {
let no_timestamps_token = token_id(&tokenizer, m::NO_TIMESTAMPS_TOKEN)?;
// Suppress the notimestamps token when in timestamps mode.
// https://github.com/openai/whisper/blob/e8622f9afc4eba139bf796c210f5c01081000472/whisper/decoding.py#L452
let suppress_tokens: Vec<f32> = (0..model.config().vocab_size as u32)
.map(|i| {
if model.config().suppress_tokens.contains(&i)
|| timestamps && i == no_timestamps_token
{
f32::NEG_INFINITY
} else {
0f32
}
})
.collect();
let suppress_tokens = Tensor::new(suppress_tokens.as_slice(), device)?;
let sot_token = token_id(&tokenizer, m::SOT_TOKEN)?;
let transcribe_token = token_id(&tokenizer, m::TRANSCRIBE_TOKEN)?;
let translate_token = token_id(&tokenizer, m::TRANSLATE_TOKEN)?;
let eot_token = token_id(&tokenizer, m::EOT_TOKEN)?;
let no_speech_token = m::NO_SPEECH_TOKENS
.iter()
.find_map(|token| token_id(&tokenizer, token).ok());
let no_speech_token = match no_speech_token {
None => anyhow::bail!("unable to find any non-speech token"),
Some(n) => n,
};
Ok(Self {
model,
rng: rand::rngs::StdRng::seed_from_u64(seed),
tokenizer,
task,
timestamps,
verbose,
suppress_tokens,
sot_token,
transcribe_token,
translate_token,
eot_token,
no_speech_token,
language_token,
no_timestamps_token,
})
}
fn decode(&mut self, mel: &Tensor, t: f64) -> Result<DecodingResult> {
let model = &mut self.model;
let audio_features = model.encoder_forward(mel, true)?;
if self.verbose {
println!("audio features: {:?}", audio_features.dims());
}
let sample_len = model.config().max_target_positions / 2;
let mut sum_logprob = 0f64;
let mut no_speech_prob = f64::NAN;
let mut tokens = vec![self.sot_token];
if let Some(language_token) = self.language_token {
tokens.push(language_token);
}
match self.task {
None | Some(Task::Transcribe) => tokens.push(self.transcribe_token),
Some(Task::Translate) => tokens.push(self.translate_token),
}
if !self.timestamps {
tokens.push(self.no_timestamps_token);
}
for i in 0..sample_len {
let tokens_t = Tensor::new(tokens.as_slice(), mel.device())?;
// The model expects a batch dim but this inference loop does not handle
// it so we add it at this point.
let tokens_t = tokens_t.unsqueeze(0)?;
let ys = model.decoder_forward(&tokens_t, &audio_features, i == 0)?;
// Extract the no speech probability on the first iteration by looking at the first
// token logits and the probability for the according token.
if i == 0 {
let logits = model.decoder_final_linear(&ys.i(..1)?)?.i(0)?.i(0)?;
no_speech_prob = softmax(&logits, 0)?
.i(self.no_speech_token as usize)?
.to_scalar::<f32>()? as f64;
}
let (_, seq_len, _) = ys.dims3()?;
let logits = model
.decoder_final_linear(&ys.i((..1, seq_len - 1..))?)?
.i(0)?
.i(0)?;
// TODO: Besides suppress tokens, we should apply the heuristics from
// ApplyTimestampRules, i.e.:
// - Timestamps come in pairs, except before EOT.
// - Timestamps should be non-decreasing.
// - If the sum of the probabilities of timestamps is higher than any other tokens,
// only consider timestamps when sampling.
// https://github.com/openai/whisper/blob/e8622f9afc4eba139bf796c210f5c01081000472/whisper/decoding.py#L439
let logits = logits.broadcast_add(&self.suppress_tokens)?;
let next_token = if t > 0f64 {
let prs = softmax(&(&logits / t)?, 0)?;
let logits_v: Vec<f32> = prs.to_vec1()?;
let distr = rand::distributions::WeightedIndex::new(&logits_v)?;
distr.sample(&mut self.rng) as u32
} else {
let logits_v: Vec<f32> = logits.to_vec1()?;
logits_v
.iter()
.enumerate()
.max_by(|(_, u), (_, v)| u.total_cmp(v))
.map(|(i, _)| i as u32)
.unwrap()
};
tokens.push(next_token);
let prob = softmax(&logits, candle::D::Minus1)?
.i(next_token as usize)?
.to_scalar::<f32>()? as f64;
if next_token == self.eot_token || tokens.len() > model.config().max_target_positions {
break;
}
sum_logprob += prob.ln();
}
let text = self.tokenizer.decode(&tokens, true).map_err(E::msg)?;
let avg_logprob = sum_logprob / tokens.len() as f64;
Ok(DecodingResult {
tokens,
text,
avg_logprob,
no_speech_prob,
temperature: t,
compression_ratio: f64::NAN,
})
}
fn decode_with_fallback(&mut self, segment: &Tensor) -> Result<DecodingResult> {
for (i, &t) in m::TEMPERATURES.iter().enumerate() {
let dr: Result<DecodingResult> = self.decode(segment, t);
if i == m::TEMPERATURES.len() - 1 {
return dr;
}
// On errors, we try again with a different temperature.
match dr {
Ok(dr) => {
let needs_fallback = dr.compression_ratio > m::COMPRESSION_RATIO_THRESHOLD
|| dr.avg_logprob < m::LOGPROB_THRESHOLD;
if !needs_fallback || dr.no_speech_prob > m::NO_SPEECH_THRESHOLD {
return Ok(dr);
}
}
Err(err) => {
println!("Error running at {t}: {err}")
}
}
}
unreachable!()
}
fn run(&mut self, mel: &Tensor) -> Result<Vec<Segment>> {
let (_, _, content_frames) = mel.dims3()?;
let mut seek = 0;
let mut segments = vec![];
while seek < content_frames {
let start = std::time::Instant::now();
let time_offset = (seek * m::HOP_LENGTH) as f64 / m::SAMPLE_RATE as f64;
let segment_size = usize::min(content_frames - seek, m::N_FRAMES);
let mel_segment = mel.narrow(2, seek, segment_size)?;
let segment_duration = (segment_size * m::HOP_LENGTH) as f64 / m::SAMPLE_RATE as f64;
let dr = self.decode_with_fallback(&mel_segment)?;
seek += segment_size;
if dr.no_speech_prob > m::NO_SPEECH_THRESHOLD && dr.avg_logprob < m::LOGPROB_THRESHOLD {
println!("no speech detected, skipping {seek} {dr:?}");
continue;
}
let segment = Segment {
start: time_offset,
duration: segment_duration,
dr,
};
if self.timestamps {
println!(
"{:.1}s -- {:.1}s",
segment.start,
segment.start + segment.duration,
);
let mut tokens_to_decode = vec![];
let mut prev_timestamp_s = 0f32;
for &token in segment.dr.tokens.iter() {
if token == self.sot_token || token == self.eot_token {
continue;
}
// The no_timestamp_token is the last before the timestamp ones.
if token > self.no_timestamps_token {
let timestamp_s = (token - self.no_timestamps_token + 1) as f32 / 50.;
if !tokens_to_decode.is_empty() {
let text = self
.tokenizer
.decode(&tokens_to_decode, true)
.map_err(E::msg)?;
println!(" {:.1}s-{:.1}s: {}", prev_timestamp_s, timestamp_s, text);
tokens_to_decode.clear()
}
prev_timestamp_s = timestamp_s;
} else {
tokens_to_decode.push(token)
}
}
if !tokens_to_decode.is_empty() {
let text = self
.tokenizer
.decode(&tokens_to_decode, true)
.map_err(E::msg)?;
if !text.is_empty() {
println!(" {:.1}s-...: {}", prev_timestamp_s, text);
}
tokens_to_decode.clear()
}
} else {
println!(
"{:.1}s -- {:.1}s: {}",
segment.start,
segment.start + segment.duration,
segment.dr.text,
)
}
if self.verbose {
println!("{seek}: {segment:?}, in {:?}", start.elapsed());
}
segments.push(segment)
}
Ok(segments)
}
}
pub fn token_id(tokenizer: &Tokenizer, token: &str) -> candle::Result<u32> {
match tokenizer.token_to_id(token) {
None => candle::bail!("no token-id for {token}"),
Some(id) => Ok(id),
}
}
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Task {
Transcribe,
Translate,
}
#[derive(Clone, Copy, Debug, PartialEq, Eq, ValueEnum)]
enum WhichModel {
Tiny,
#[value(name = "tiny.en")]
TinyEn,
Base,
#[value(name = "base.en")]
BaseEn,
Small,
#[value(name = "small.en")]
SmallEn,
Medium,
#[value(name = "medium.en")]
MediumEn,
Large,
LargeV2,
LargeV3,
#[value(name = "distil-medium.en")]
DistilMediumEn,
#[value(name = "distil-large-v2")]
DistilLargeV2,
}
impl WhichModel {
fn is_multilingual(&self) -> bool {
match self {
Self::Tiny
| Self::Base
| Self::Small
| Self::Medium
| Self::Large
| Self::LargeV2
| Self::LargeV3
| Self::DistilLargeV2 => true,
Self::TinyEn | Self::BaseEn | Self::SmallEn | Self::MediumEn | Self::DistilMediumEn => {
false
}
}
}
fn model_and_revision(&self) -> (&'static str, &'static str) {
match self {
Self::Tiny => ("openai/whisper-tiny", "main"),
Self::TinyEn => ("openai/whisper-tiny.en", "refs/pr/15"),
Self::Base => ("openai/whisper-base", "refs/pr/22"),
Self::BaseEn => ("openai/whisper-base.en", "refs/pr/13"),
Self::Small => ("openai/whisper-small", "main"),
Self::SmallEn => ("openai/whisper-small.en", "refs/pr/10"),
Self::Medium => ("openai/whisper-medium", "main"),
Self::MediumEn => ("openai/whisper-medium.en", "main"),
Self::Large => ("openai/whisper-large", "refs/pr/36"),
Self::LargeV2 => ("openai/whisper-large-v2", "refs/pr/57"),
Self::LargeV3 => ("openai/whisper-large-v3", "main"),
Self::DistilMediumEn => ("distil-whisper/distil-medium.en", "main"),
Self::DistilLargeV2 => ("distil-whisper/distil-large-v2", "main"),
}
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
#[arg(long)]
model_id: Option<String>,
/// The model to use, check out available models:
/// https://huggingface.co/models?search=whisper
#[arg(long)]
revision: Option<String>,
/// The model to be used, can be tiny, small, medium.
#[arg(long, default_value = "tiny.en")]
model: WhichModel,
/// The input to be processed, in wav format, will default to `jfk.wav`. Alternatively
/// this can be set to sample:jfk, sample:gb1, ... to fetch a sample from the following
/// repo: https://huggingface.co/datasets/Narsil/candle_demo/
#[arg(long)]
input: Option<String>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
quantized: bool,
/// Language.
#[arg(long)]
language: Option<String>,
/// Task, when no task is specified, the input tokens contain only the sot token which can
/// improve things when in no-timestamp mode.
#[arg(long)]
task: Option<Task>,
/// Timestamps mode, this is not fully implemented yet.
#[arg(long)]
timestamps: bool,
/// Print the full DecodingResult structure rather than just the text.
#[arg(long)]
verbose: bool,
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
let device = candle_examples::device(args.cpu)?;
let (default_model, default_revision) = if args.quantized {
("lmz/candle-whisper", "main")
} else {
args.model.model_and_revision()
};
let default_model = default_model.to_string();
let default_revision = default_revision.to_string();
let (model_id, revision) = match (args.model_id, args.revision) {
(Some(model_id), Some(revision)) => (model_id, revision),
(Some(model_id), None) => (model_id, "main".to_string()),
(None, Some(revision)) => (default_model, revision),
(None, None) => (default_model, default_revision),
};
let (config_filename, tokenizer_filename, weights_filename, input) = {
let api = Api::new()?;
let dataset = api.dataset("Narsil/candle-examples".to_string());
let repo = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
let sample = if let Some(input) = args.input {
if let Some(sample) = input.strip_prefix("sample:") {
dataset.get(&format!("samples_{sample}.wav"))?
} else {
std::path::PathBuf::from(input)
}
} else {
println!("No audio file submitted: Downloading https://huggingface.co/datasets/Narsil/candle_demo/blob/main/samples_jfk.wav");
dataset.get("samples_jfk.wav")?
};
let (config, tokenizer, model) = if args.quantized {
let ext = match args.model {
WhichModel::TinyEn => "tiny-en",
WhichModel::Tiny => "tiny",
_ => unimplemented!("no quantized support for {:?}", args.model),
};
(
repo.get(&format!("config-{ext}.json"))?,
repo.get(&format!("tokenizer-{ext}.json"))?,
repo.get(&format!("model-{ext}-q80.gguf"))?,
)
} else {
let config = repo.get("config.json")?;
let tokenizer = repo.get("tokenizer.json")?;
let model = repo.get("model.safetensors")?;
(config, tokenizer, model)
};
(config, tokenizer, model, sample)
};
let config: Config = serde_json::from_str(&std::fs::read_to_string(config_filename)?)?;
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let mel_bytes = match config.num_mel_bins {
80 => include_bytes!("melfilters.bytes").as_slice(),
128 => include_bytes!("melfilters128.bytes").as_slice(),
nmel => anyhow::bail!("unexpected num_mel_bins {nmel}"),
};
let mut mel_filters = vec![0f32; mel_bytes.len() / 4];
<byteorder::LittleEndian as byteorder::ByteOrder>::read_f32_into(mel_bytes, &mut mel_filters);
let mut input = std::fs::File::open(input)?;
let (header, data) = wav::read(&mut input)?;
println!("loaded wav data: {header:?}");
if header.sampling_rate != m::SAMPLE_RATE as u32 {
anyhow::bail!("wav file must have a {} sampling rate", m::SAMPLE_RATE)
}
let data = data.as_sixteen().expect("expected 16 bit wav file");
let pcm_data: Vec<_> = data[..data.len() / header.channel_count as usize]
.iter()
.map(|v| *v as f32 / 32768.)
.collect();
println!("pcm data loaded {}", pcm_data.len());
let mel = audio::pcm_to_mel(&config, &pcm_data, &mel_filters);
let mel_len = mel.len();
let mel = Tensor::from_vec(
mel,
(1, config.num_mel_bins, mel_len / config.num_mel_bins),
&device,
)?;
println!("loaded mel: {:?}", mel.dims());
let mut model = if args.quantized {
let vb = candle_transformers::quantized_var_builder::VarBuilder::from_gguf(
&weights_filename,
&device,
)?;
Model::Quantized(m::quantized_model::Whisper::load(&vb, config)?)
} else {
let vb =
unsafe { VarBuilder::from_mmaped_safetensors(&[weights_filename], m::DTYPE, &device)? };
Model::Normal(m::model::Whisper::load(&vb, config)?)
};
let language_token = match (args.model.is_multilingual(), args.language) {
(true, None) => Some(multilingual::detect_language(&mut model, &tokenizer, &mel)?),
(false, None) => None,
(true, Some(language)) => match token_id(&tokenizer, &format!("<|{language}|>")) {
Ok(token_id) => Some(token_id),
Err(_) => anyhow::bail!("language {language} is not supported"),
},
(false, Some(_)) => {
anyhow::bail!("a language cannot be set for non-multilingual models")
}
};
let mut dc = Decoder::new(
model,
tokenizer,
args.seed,
&device,
language_token,
args.task,
args.timestamps,
args.verbose,
)?;
dc.run(&mel)?;
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/whisper/multilingual.rs | use candle::{IndexOp, Result, Tensor, D};
use tokenizers::Tokenizer;
const LANGUAGES: [(&str, &str); 99] = [
("en", "english"),
("zh", "chinese"),
("de", "german"),
("es", "spanish"),
("ru", "russian"),
("ko", "korean"),
("fr", "french"),
("ja", "japanese"),
("pt", "portuguese"),
("tr", "turkish"),
("pl", "polish"),
("ca", "catalan"),
("nl", "dutch"),
("ar", "arabic"),
("sv", "swedish"),
("it", "italian"),
("id", "indonesian"),
("hi", "hindi"),
("fi", "finnish"),
("vi", "vietnamese"),
("he", "hebrew"),
("uk", "ukrainian"),
("el", "greek"),
("ms", "malay"),
("cs", "czech"),
("ro", "romanian"),
("da", "danish"),
("hu", "hungarian"),
("ta", "tamil"),
("no", "norwegian"),
("th", "thai"),
("ur", "urdu"),
("hr", "croatian"),
("bg", "bulgarian"),
("lt", "lithuanian"),
("la", "latin"),
("mi", "maori"),
("ml", "malayalam"),
("cy", "welsh"),
("sk", "slovak"),
("te", "telugu"),
("fa", "persian"),
("lv", "latvian"),
("bn", "bengali"),
("sr", "serbian"),
("az", "azerbaijani"),
("sl", "slovenian"),
("kn", "kannada"),
("et", "estonian"),
("mk", "macedonian"),
("br", "breton"),
("eu", "basque"),
("is", "icelandic"),
("hy", "armenian"),
("ne", "nepali"),
("mn", "mongolian"),
("bs", "bosnian"),
("kk", "kazakh"),
("sq", "albanian"),
("sw", "swahili"),
("gl", "galician"),
("mr", "marathi"),
("pa", "punjabi"),
("si", "sinhala"),
("km", "khmer"),
("sn", "shona"),
("yo", "yoruba"),
("so", "somali"),
("af", "afrikaans"),
("oc", "occitan"),
("ka", "georgian"),
("be", "belarusian"),
("tg", "tajik"),
("sd", "sindhi"),
("gu", "gujarati"),
("am", "amharic"),
("yi", "yiddish"),
("lo", "lao"),
("uz", "uzbek"),
("fo", "faroese"),
("ht", "haitian creole"),
("ps", "pashto"),
("tk", "turkmen"),
("nn", "nynorsk"),
("mt", "maltese"),
("sa", "sanskrit"),
("lb", "luxembourgish"),
("my", "myanmar"),
("bo", "tibetan"),
("tl", "tagalog"),
("mg", "malagasy"),
("as", "assamese"),
("tt", "tatar"),
("haw", "hawaiian"),
("ln", "lingala"),
("ha", "hausa"),
("ba", "bashkir"),
("jw", "javanese"),
("su", "sundanese"),
];
/// Returns the token id for the selected language.
pub fn detect_language(
model: &mut super::Model,
tokenizer: &Tokenizer,
mel: &Tensor,
) -> Result<u32> {
let (_bsize, _, seq_len) = mel.dims3()?;
let mel = mel.narrow(
2,
0,
usize::min(seq_len, model.config().max_source_positions),
)?;
let device = mel.device();
let language_token_ids = LANGUAGES
.iter()
.map(|(t, _)| crate::token_id(tokenizer, &format!("<|{t}|>")))
.collect::<Result<Vec<_>>>()?;
let sot_token = crate::token_id(tokenizer, crate::m::SOT_TOKEN)?;
let audio_features = model.encoder_forward(&mel, true)?;
let tokens = Tensor::new(&[[sot_token]], device)?;
let language_token_ids = Tensor::new(language_token_ids.as_slice(), device)?;
let ys = model.decoder_forward(&tokens, &audio_features, true)?;
let logits = model.decoder_final_linear(&ys.i(..1)?)?.i(0)?.i(0)?;
let logits = logits.index_select(&language_token_ids, 0)?;
let probs = candle_nn::ops::softmax(&logits, D::Minus1)?;
let probs = probs.to_vec1::<f32>()?;
let mut probs = LANGUAGES.iter().zip(probs.iter()).collect::<Vec<_>>();
probs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
for ((_, language), p) in probs.iter().take(5) {
println!("{language}: {p}")
}
let language = crate::token_id(tokenizer, &format!("<|{}|>", probs[0].0 .0))?;
Ok(language)
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/whisper/README.md | # candle-whisper: speech recognition
An implementation of [OpenAI Whisper](https://github.com/openai/whisper) using
candle. Whisper is a general purpose speech recognition model, it can be used to
convert audio files (in the `.wav` format) to text. Supported features include
language detection as well as multilingual speech recognition.
## Running some example
If no audio file is passed as input, a [sample
file](https://huggingface.co/datasets/Narsil/candle-examples/resolve/main/samples_jfk.wav) is automatically downloaded
from the hub.
```bash
cargo run --example whisper --release
> No audio file submitted: Downloading https://huggingface.co/datasets/Narsil/candle_demo/blob/main/samples_jfk.wav
> loaded wav data: Header { audio_format: 1, channel_count: 1, sampling_rate: 16000, bytes_per_second: 32000, bytes_per_sample: 2, bits_per_sample: 16 }
> pcm data loaded 176000
> loaded mel: [1, 80, 3000]
> 0.0s -- 30.0s: And so my fellow Americans ask not what your country can do for you ask what you can do for your country
```
In order to use the multilingual mode, specify a multilingual model via the
`--model` flag, see the details below.
## Command line flags
- `--input`: the audio file to be converted to text, in wav format.
- `--language`: force the language to some specific value rather than being
detected, e.g. `en`.
- `--task`: the task to be performed, can be `transcribe` (return the text data
in the original language) or `translate` (translate the text to English).
- `--timestamps`: enable the timestamp mode where some timestamps are reported
for each recognized audio extracts.
- `--model`: the model to be used. Models that do not end with `-en` are
multilingual models, other ones are English only models. The supported models
are `tiny`, `tiny.en`, `base`, `base.en`, `small`, `small.en`, `medium`,
`medium.en`, `large`, and `large-v2`.
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/whisper/extract_weights.py | # Get the checkpoint from
# https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt
import torch
from safetensors.torch import save_file
data = torch.load("tiny.en.pt")
weights = {}
for k, v in data["model_state_dict"].items():
weights[k] = v.contiguous()
print(k, v.shape, v.dtype)
save_file(weights, "tiny.en.safetensors")
print(data["dims"])
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yi/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::{Parser, ValueEnum};
use candle_transformers::models::yi::{Config, Model};
use candle::{DType, Device, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
enum Which {
#[value(name = "6b")]
L6b,
#[value(name = "34b")]
L34b,
}
struct TextGeneration {
model: Model,
device: Device,
tokenizer: TokenOutputStream,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
}
impl TextGeneration {
#[allow(clippy::too_many_arguments)]
fn new(
model: Model,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
top_p: Option<f64>,
repeat_penalty: f32,
repeat_last_n: usize,
device: &Device,
) -> Self {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer: TokenOutputStream::new(tokenizer),
logits_processor,
repeat_penalty,
repeat_last_n,
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
self.tokenizer.clear();
let mut tokens = self
.tokenizer
.tokenizer()
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
for &t in tokens.iter() {
if let Some(t) = self.tokenizer.next_token(t)? {
print!("{t}")
}
}
std::io::stdout().flush()?;
let mut generated_tokens = 0usize;
let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
Some(token) => token,
None => anyhow::bail!("cannot find the <|endoftext|> token"),
};
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let context_size = if index > 0 { 1 } else { tokens.len() };
let start_pos = tokens.len().saturating_sub(context_size);
let ctxt = &tokens[start_pos..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = self.model.forward(&input, start_pos)?;
let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(self.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
generated_tokens += 1;
if next_token == eos_token {
break;
}
if let Some(t) = self.tokenizer.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
}
let dt = start_gen.elapsed();
if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
std::io::stdout().flush()?;
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
generated_tokens as f64 / dt.as_secs_f64(),
);
Ok(())
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
prompt: String,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, short = 'n', default_value_t = 100)]
sample_len: usize,
#[arg(long, default_value = "01-ai/Yi-6B")]
model_id: String,
#[arg(long, default_value = "main")]
revision: String,
#[arg(long)]
tokenizer_file: Option<String>,
#[arg(long)]
weight_files: Option<String>,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
/// The model size to use.
#[arg(long, default_value = "6b")]
which: Which,
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
println!(
"avx: {}, neon: {}, simd128: {}, f16c: {}",
candle::utils::with_avx(),
candle::utils::with_neon(),
candle::utils::with_simd128(),
candle::utils::with_f16c()
);
println!(
"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
args.temperature.unwrap_or(0.),
args.repeat_penalty,
args.repeat_last_n
);
let start = std::time::Instant::now();
let api = Api::new()?;
let repo = api.repo(Repo::with_revision(
args.model_id,
RepoType::Model,
args.revision,
));
let tokenizer_filename = match args.tokenizer_file {
Some(file) => std::path::PathBuf::from(file),
None => repo.get("tokenizer.json")?,
};
let filenames = match args.weight_files {
Some(files) => files
.split(',')
.map(std::path::PathBuf::from)
.collect::<Vec<_>>(),
None => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?,
};
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let config = match args.which {
Which::L6b => Config::config_6b(),
Which::L34b => Config::config_34b(),
};
let device = candle_examples::device(args.cpu)?;
let dtype = if device.is_cuda() {
DType::BF16
} else {
DType::F32
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
let model = Model::new(&config, vb)?;
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(
model,
tokenizer,
args.seed,
args.temperature,
args.top_p,
args.repeat_penalty,
args.repeat_last_n,
&device,
);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/dinov2/main.rs | //! DINOv2: Learning Robust Visual Features without Supervision
//! https://github.com/facebookresearch/dinov2
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::Parser;
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::dinov2;
#[derive(Parser)]
struct Args {
#[arg(long)]
model: Option<String>,
#[arg(long)]
image: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let image = candle_examples::imagenet::load_image224(args.image)?;
println!("loaded image {image:?}");
let model_file = match args.model {
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("lmz/candle-dino-v2".into());
api.get("dinov2_vits14.safetensors")?
}
Some(model) => model.into(),
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let model = dinov2::vit_small(vb)?;
println!("model built");
let logits = model.forward(&image.unsqueeze(0)?)?;
let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
.i(0)?
.to_vec1::<f32>()?;
let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
for &(category_idx, pr) in prs.iter().take(5) {
println!(
"{:24}: {:.2}%",
candle_examples::imagenet::CLASSES[category_idx],
100. * pr
);
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/dinov2/README.md | # candle-dinov2
[DINOv2](https://github.com/facebookresearch/dinov2) is a computer vision model.
In this example, it is used as an ImageNet classifier: the model returns the
probability for the image to belong to each of the 1000 ImageNet categories.
## Running some example
```bash
cargo run --example dinov2 --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
> mountain bike, all-terrain bike, off-roader: 43.67%
> bicycle-built-for-two, tandem bicycle, tandem: 33.20%
> crash helmet : 13.23%
> unicycle, monocycle : 2.44%
> maillot : 2.42%
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v3/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::object_detection::{non_maximum_suppression, Bbox};
mod darknet;
use anyhow::Result;
use candle::{DType, Device, Tensor};
use candle_nn::{Module, VarBuilder};
use clap::Parser;
use image::{DynamicImage, ImageBuffer};
// Assumes x1 <= x2 and y1 <= y2
pub fn draw_rect(
img: &mut ImageBuffer<image::Rgb<u8>, Vec<u8>>,
x1: u32,
x2: u32,
y1: u32,
y2: u32,
) {
for x in x1..=x2 {
let pixel = img.get_pixel_mut(x, y1);
*pixel = image::Rgb([255, 0, 0]);
let pixel = img.get_pixel_mut(x, y2);
*pixel = image::Rgb([255, 0, 0]);
}
for y in y1..=y2 {
let pixel = img.get_pixel_mut(x1, y);
*pixel = image::Rgb([255, 0, 0]);
let pixel = img.get_pixel_mut(x2, y);
*pixel = image::Rgb([255, 0, 0]);
}
}
pub fn report(
pred: &Tensor,
img: DynamicImage,
w: usize,
h: usize,
confidence_threshold: f32,
nms_threshold: f32,
) -> Result<DynamicImage> {
let pred = pred.to_device(&Device::Cpu)?;
let (npreds, pred_size) = pred.dims2()?;
let nclasses = pred_size - 5;
// The bounding boxes grouped by (maximum) class index.
let mut bboxes: Vec<Vec<Bbox<()>>> = (0..nclasses).map(|_| vec![]).collect();
// Extract the bounding boxes for which confidence is above the threshold.
for index in 0..npreds {
let pred = Vec::<f32>::try_from(pred.get(index)?)?;
let confidence = pred[4];
if confidence > confidence_threshold {
let mut class_index = 0;
for i in 0..nclasses {
if pred[5 + i] > pred[5 + class_index] {
class_index = i
}
}
if pred[class_index + 5] > 0. {
let bbox = Bbox {
xmin: pred[0] - pred[2] / 2.,
ymin: pred[1] - pred[3] / 2.,
xmax: pred[0] + pred[2] / 2.,
ymax: pred[1] + pred[3] / 2.,
confidence,
data: (),
};
bboxes[class_index].push(bbox)
}
}
}
non_maximum_suppression(&mut bboxes, nms_threshold);
// Annotate the original image and print boxes information.
let (initial_h, initial_w) = (img.height(), img.width());
let w_ratio = initial_w as f32 / w as f32;
let h_ratio = initial_h as f32 / h as f32;
let mut img = img.to_rgb8();
for (class_index, bboxes_for_class) in bboxes.iter().enumerate() {
for b in bboxes_for_class.iter() {
println!(
"{}: {:?}",
candle_examples::coco_classes::NAMES[class_index],
b
);
let xmin = ((b.xmin * w_ratio) as u32).clamp(0, initial_w - 1);
let ymin = ((b.ymin * h_ratio) as u32).clamp(0, initial_h - 1);
let xmax = ((b.xmax * w_ratio) as u32).clamp(0, initial_w - 1);
let ymax = ((b.ymax * h_ratio) as u32).clamp(0, initial_h - 1);
draw_rect(&mut img, xmin, xmax, ymin, ymax);
}
}
Ok(DynamicImage::ImageRgb8(img))
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Model weights, in safetensors format.
#[arg(long)]
model: Option<String>,
#[arg(long)]
config: Option<String>,
images: Vec<String>,
/// Threshold for the model confidence level.
#[arg(long, default_value_t = 0.5)]
confidence_threshold: f32,
/// Threshold for non-maximum suppression.
#[arg(long, default_value_t = 0.4)]
nms_threshold: f32,
}
impl Args {
fn config(&self) -> anyhow::Result<std::path::PathBuf> {
let path = match &self.config {
Some(config) => std::path::PathBuf::from(config),
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("lmz/candle-yolo-v3".to_string());
api.get("yolo-v3.cfg")?
}
};
Ok(path)
}
fn model(&self) -> anyhow::Result<std::path::PathBuf> {
let path = match &self.model {
Some(model) => std::path::PathBuf::from(model),
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("lmz/candle-yolo-v3".to_string());
api.get("yolo-v3.safetensors")?
}
};
Ok(path)
}
}
pub fn main() -> Result<()> {
let args = Args::parse();
// Create the model and load the weights from the file.
let model = args.model()?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &Device::Cpu)? };
let config = args.config()?;
let darknet = darknet::parse_config(config)?;
let model = darknet.build_model(vb)?;
for image_name in args.images.iter() {
println!("processing {image_name}");
let mut image_name = std::path::PathBuf::from(image_name);
// Load the image file and resize it.
let net_width = darknet.width()?;
let net_height = darknet.height()?;
let original_image = image::io::Reader::open(&image_name)?
.decode()
.map_err(candle::Error::wrap)?;
let image = {
let data = original_image
.resize_exact(
net_width as u32,
net_height as u32,
image::imageops::FilterType::Triangle,
)
.to_rgb8()
.into_raw();
Tensor::from_vec(data, (net_width, net_height, 3), &Device::Cpu)?.permute((2, 0, 1))?
};
let image = (image.unsqueeze(0)?.to_dtype(DType::F32)? * (1. / 255.))?;
let predictions = model.forward(&image)?.squeeze(0)?;
println!("generated predictions {predictions:?}");
let image = report(
&predictions,
original_image,
net_width,
net_height,
args.confidence_threshold,
args.nms_threshold,
)?;
image_name.set_extension("pp.jpg");
println!("writing {image_name:?}");
image.save(image_name)?
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v3/yolo-v3.cfg | [net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=16
width= 416
height = 416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v3/darknet.rs | use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{batch_norm, conv2d, conv2d_no_bias, Func, Module, VarBuilder};
use std::collections::BTreeMap;
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::Path;
#[derive(Debug)]
struct Block {
block_type: String,
parameters: BTreeMap<String, String>,
}
impl Block {
fn get(&self, key: &str) -> Result<&str> {
match self.parameters.get(&key.to_string()) {
None => candle::bail!("cannot find {} in {}", key, self.block_type),
Some(value) => Ok(value),
}
}
}
#[derive(Debug)]
pub struct Darknet {
blocks: Vec<Block>,
parameters: BTreeMap<String, String>,
}
impl Darknet {
fn get(&self, key: &str) -> Result<&str> {
match self.parameters.get(&key.to_string()) {
None => candle::bail!("cannot find {} in net parameters", key),
Some(value) => Ok(value),
}
}
}
struct Accumulator {
block_type: Option<String>,
parameters: BTreeMap<String, String>,
net: Darknet,
}
impl Accumulator {
fn new() -> Accumulator {
Accumulator {
block_type: None,
parameters: BTreeMap::new(),
net: Darknet {
blocks: vec![],
parameters: BTreeMap::new(),
},
}
}
fn finish_block(&mut self) {
match &self.block_type {
None => (),
Some(block_type) => {
if block_type == "net" {
self.net.parameters = self.parameters.clone();
} else {
let block = Block {
block_type: block_type.to_string(),
parameters: self.parameters.clone(),
};
self.net.blocks.push(block);
}
self.parameters.clear();
}
}
self.block_type = None;
}
}
pub fn parse_config<T: AsRef<Path>>(path: T) -> Result<Darknet> {
let file = File::open(path.as_ref())?;
let mut acc = Accumulator::new();
for line in BufReader::new(file).lines() {
let line = line?;
if line.is_empty() || line.starts_with('#') {
continue;
}
let line = line.trim();
if line.starts_with('[') {
if !line.ends_with(']') {
candle::bail!("line does not end with ']' {line}")
}
let line = &line[1..line.len() - 1];
acc.finish_block();
acc.block_type = Some(line.to_string());
} else {
let key_value: Vec<&str> = line.splitn(2, '=').collect();
if key_value.len() != 2 {
candle::bail!("missing equal {line}")
}
let prev = acc.parameters.insert(
key_value[0].trim().to_owned(),
key_value[1].trim().to_owned(),
);
if prev.is_some() {
candle::bail!("multiple value for key {}", line)
}
}
}
acc.finish_block();
Ok(acc.net)
}
enum Bl {
Layer(Box<dyn candle_nn::Module + Send + Sync>),
Route(Vec<usize>),
Shortcut(usize),
Yolo(usize, Vec<(usize, usize)>),
}
fn conv(vb: VarBuilder, index: usize, p: usize, b: &Block) -> Result<(usize, Bl)> {
let activation = b.get("activation")?;
let filters = b.get("filters")?.parse::<usize>()?;
let pad = b.get("pad")?.parse::<usize>()?;
let size = b.get("size")?.parse::<usize>()?;
let stride = b.get("stride")?.parse::<usize>()?;
let padding = if pad != 0 { (size - 1) / 2 } else { 0 };
let (bn, bias) = match b.parameters.get("batch_normalize") {
Some(p) if p.parse::<usize>()? != 0 => {
let bn = batch_norm(filters, 1e-5, vb.pp(&format!("batch_norm_{index}")))?;
(Some(bn), false)
}
Some(_) | None => (None, true),
};
let conv_cfg = candle_nn::Conv2dConfig {
stride,
padding,
groups: 1,
dilation: 1,
};
let conv = if bias {
conv2d(p, filters, size, conv_cfg, vb.pp(&format!("conv_{index}")))?
} else {
conv2d_no_bias(p, filters, size, conv_cfg, vb.pp(&format!("conv_{index}")))?
};
let leaky = match activation {
"leaky" => true,
"linear" => false,
otherwise => candle::bail!("unsupported activation {}", otherwise),
};
let func = candle_nn::func(move |xs| {
let xs = conv.forward(xs)?;
let xs = match &bn {
Some(bn) => xs.apply_t(bn, false)?,
None => xs,
};
let xs = if leaky {
xs.maximum(&(&xs * 0.1)?)?
} else {
xs
};
Ok(xs)
});
Ok((filters, Bl::Layer(Box::new(func))))
}
fn upsample(prev_channels: usize) -> Result<(usize, Bl)> {
let layer = candle_nn::func(|xs| {
let (_n, _c, h, w) = xs.dims4()?;
xs.upsample_nearest2d(2 * h, 2 * w)
});
Ok((prev_channels, Bl::Layer(Box::new(layer))))
}
fn int_list_of_string(s: &str) -> Result<Vec<i64>> {
let res: std::result::Result<Vec<_>, _> =
s.split(',').map(|xs| xs.trim().parse::<i64>()).collect();
Ok(res?)
}
fn usize_of_index(index: usize, i: i64) -> usize {
if i >= 0 {
i as usize
} else {
(index as i64 + i) as usize
}
}
fn route(index: usize, p: &[(usize, Bl)], block: &Block) -> Result<(usize, Bl)> {
let layers = int_list_of_string(block.get("layers")?)?;
let layers: Vec<usize> = layers
.into_iter()
.map(|l| usize_of_index(index, l))
.collect();
let channels = layers.iter().map(|&l| p[l].0).sum();
Ok((channels, Bl::Route(layers)))
}
fn shortcut(index: usize, p: usize, block: &Block) -> Result<(usize, Bl)> {
let from = block.get("from")?.parse::<i64>()?;
Ok((p, Bl::Shortcut(usize_of_index(index, from))))
}
fn yolo(p: usize, block: &Block) -> Result<(usize, Bl)> {
let classes = block.get("classes")?.parse::<usize>()?;
let flat = int_list_of_string(block.get("anchors")?)?;
if flat.len() % 2 != 0 {
candle::bail!("even number of anchors");
}
let flat = flat.into_iter().map(|i| i as usize).collect::<Vec<_>>();
let anchors: Vec<_> = (0..(flat.len() / 2))
.map(|i| (flat[2 * i], flat[2 * i + 1]))
.collect();
let mask = int_list_of_string(block.get("mask")?)?;
let anchors = mask.into_iter().map(|i| anchors[i as usize]).collect();
Ok((p, Bl::Yolo(classes, anchors)))
}
fn detect(
xs: &Tensor,
image_height: usize,
classes: usize,
anchors: &Vec<(usize, usize)>,
) -> Result<Tensor> {
let (bsize, _channels, height, _width) = xs.dims4()?;
let stride = image_height / height;
let grid_size = image_height / stride;
let bbox_attrs = 5 + classes;
let nanchors = anchors.len();
let xs = xs
.reshape((bsize, bbox_attrs * nanchors, grid_size * grid_size))?
.transpose(1, 2)?
.contiguous()?
.reshape((bsize, grid_size * grid_size * nanchors, bbox_attrs))?;
let grid = Tensor::arange(0u32, grid_size as u32, &Device::Cpu)?;
let a = grid.repeat((grid_size, 1))?;
let b = a.t()?.contiguous()?;
let x_offset = a.flatten_all()?.unsqueeze(1)?;
let y_offset = b.flatten_all()?.unsqueeze(1)?;
let xy_offset = Tensor::cat(&[&x_offset, &y_offset], 1)?
.repeat((1, nanchors))?
.reshape((grid_size * grid_size * nanchors, 2))?
.unsqueeze(0)?
.to_dtype(DType::F32)?;
let anchors: Vec<f32> = anchors
.iter()
.flat_map(|&(x, y)| vec![x as f32 / stride as f32, y as f32 / stride as f32].into_iter())
.collect();
let anchors = Tensor::new(anchors.as_slice(), &Device::Cpu)?
.reshape((anchors.len() / 2, 2))?
.repeat((grid_size * grid_size, 1))?
.unsqueeze(0)?;
let ys02 = xs.i((.., .., 0..2))?;
let ys24 = xs.i((.., .., 2..4))?;
let ys4 = xs.i((.., .., 4..))?;
let ys02 = (candle_nn::ops::sigmoid(&ys02)?.add(&xy_offset)? * stride as f64)?;
let ys24 = (ys24.exp()?.mul(&anchors)? * stride as f64)?;
let ys4 = candle_nn::ops::sigmoid(&ys4)?;
let ys = Tensor::cat(&[ys02, ys24, ys4], 2)?;
Ok(ys)
}
impl Darknet {
pub fn height(&self) -> Result<usize> {
let image_height = self.get("height")?.parse::<usize>()?;
Ok(image_height)
}
pub fn width(&self) -> Result<usize> {
let image_width = self.get("width")?.parse::<usize>()?;
Ok(image_width)
}
pub fn build_model(&self, vb: VarBuilder) -> Result<Func> {
let mut blocks: Vec<(usize, Bl)> = vec![];
let mut prev_channels: usize = 3;
for (index, block) in self.blocks.iter().enumerate() {
let channels_and_bl = match block.block_type.as_str() {
"convolutional" => conv(vb.pp(&index.to_string()), index, prev_channels, block)?,
"upsample" => upsample(prev_channels)?,
"shortcut" => shortcut(index, prev_channels, block)?,
"route" => route(index, &blocks, block)?,
"yolo" => yolo(prev_channels, block)?,
otherwise => candle::bail!("unsupported block type {}", otherwise),
};
prev_channels = channels_and_bl.0;
blocks.push(channels_and_bl);
}
let image_height = self.height()?;
let func = candle_nn::func(move |xs| {
let mut prev_ys: Vec<Tensor> = vec![];
let mut detections: Vec<Tensor> = vec![];
for (_, b) in blocks.iter() {
let ys = match b {
Bl::Layer(l) => {
let xs = prev_ys.last().unwrap_or(xs);
l.forward(xs)?
}
Bl::Route(layers) => {
let layers: Vec<_> = layers.iter().map(|&i| &prev_ys[i]).collect();
Tensor::cat(&layers, 1)?
}
Bl::Shortcut(from) => (prev_ys.last().unwrap() + prev_ys.get(*from).unwrap())?,
Bl::Yolo(classes, anchors) => {
let xs = prev_ys.last().unwrap_or(xs);
detections.push(detect(xs, image_height, *classes, anchors)?);
Tensor::new(&[0u32], &Device::Cpu)?
}
};
prev_ys.push(ys);
}
Tensor::cat(&detections, 1)
});
Ok(func)
}
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v3/extract-weights.py | def remove_prefix(text, prefix):
return text[text.startswith(prefix) and len(prefix):]
nps = {}
for k, v in model.state_dict().items():
k = remove_prefix(k, 'module_list.')
nps[k] = v.detach().numpy()
np.savez('yolo-v3.ot', **nps)
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/marian-mt/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Error as E;
use clap::{Parser, ValueEnum};
use candle::{DType, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::models::marian;
use tokenizers::Tokenizer;
#[derive(Clone, Debug, Copy, ValueEnum)]
enum Which {
Base,
Big,
}
// TODO: Maybe add support for the conditional prompt.
#[derive(Parser)]
struct Args {
#[arg(long)]
model: Option<String>,
#[arg(long)]
tokenizer: Option<String>,
#[arg(long)]
tokenizer_dec: Option<String>,
/// Choose the variant of the model to run.
#[arg(long, default_value = "big")]
which: Which,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Use the quantized version of the model.
#[arg(long)]
quantized: bool,
/// Text to be translated
#[arg(long)]
text: String,
}
pub fn main() -> anyhow::Result<()> {
use hf_hub::api::sync::Api;
let args = Args::parse();
let config = match args.which {
Which::Base => marian::Config::opus_mt_fr_en(),
Which::Big => marian::Config::opus_mt_tc_big_fr_en(),
};
let tokenizer = {
let tokenizer = match args.tokenizer {
Some(tokenizer) => std::path::PathBuf::from(tokenizer),
None => {
let name = match args.which {
Which::Base => "tokenizer-marian-base-fr.json",
Which::Big => "tokenizer-marian-fr.json",
};
Api::new()?
.model("lmz/candle-marian".to_string())
.get(name)?
}
};
Tokenizer::from_file(&tokenizer).map_err(E::msg)?
};
let tokenizer_dec = {
let tokenizer = match args.tokenizer_dec {
Some(tokenizer) => std::path::PathBuf::from(tokenizer),
None => {
let name = match args.which {
Which::Base => "tokenizer-marian-base-en.json",
Which::Big => "tokenizer-marian-en.json",
};
Api::new()?
.model("lmz/candle-marian".to_string())
.get(name)?
}
};
Tokenizer::from_file(&tokenizer).map_err(E::msg)?
};
let mut tokenizer_dec = TokenOutputStream::new(tokenizer_dec);
let device = candle_examples::device(args.cpu)?;
let vb = {
let model = match args.model {
Some(model) => std::path::PathBuf::from(model),
None => match args.which {
Which::Base => Api::new()?
.repo(hf_hub::Repo::with_revision(
"Helsinki-NLP/opus-mt-fr-en".to_string(),
hf_hub::RepoType::Model,
"refs/pr/4".to_string(),
))
.get("model.safetensors")?,
Which::Big => Api::new()?
.model("Helsinki-NLP/opus-mt-tc-big-fr-en".to_string())
.get("model.safetensors")?,
},
};
unsafe { VarBuilder::from_mmaped_safetensors(&[&model], DType::F32, &device)? }
};
let mut model = marian::MTModel::new(&config, vb)?;
let mut logits_processor =
candle_transformers::generation::LogitsProcessor::new(1337, None, None);
let encoder_xs = {
let mut tokens = tokenizer
.encode(args.text, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
tokens.push(config.eos_token_id);
let tokens = Tensor::new(tokens.as_slice(), &device)?.unsqueeze(0)?;
model.encoder().forward(&tokens, 0)?
};
let mut token_ids = vec![config.decoder_start_token_id];
for index in 0..1000 {
let context_size = if index >= 1 { 1 } else { token_ids.len() };
let start_pos = token_ids.len().saturating_sub(context_size);
let input_ids = Tensor::new(&token_ids[start_pos..], &device)?.unsqueeze(0)?;
let logits = model.decode(&input_ids, &encoder_xs, start_pos)?;
let logits = logits.squeeze(0)?;
let logits = logits.get(logits.dim(0)? - 1)?;
let token = logits_processor.sample(&logits)?;
token_ids.push(token);
if let Some(t) = tokenizer_dec.next_token(token)? {
use std::io::Write;
print!("{t}");
std::io::stdout().flush()?;
}
if token == config.eos_token_id || token == config.forced_eos_token_id {
break;
}
}
if let Some(rest) = tokenizer_dec.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
println!();
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/marian-mt/README.md | # candle-marian-mt
`marian-mt` is a neural machine translation model. In this example it is used to
translate text from French to English. See the associated [model
card](https://huggingface.co/Helsinki-NLP/opus-mt-tc-big-fr-en) for details on
the model itself.
## Running an example
```bash
cargo run --example marian-mt --release -- \
--text "Demain, dès l'aube, à l'heure où blanchit la campagne, Je partirai. Vois-tu, je sais que tu m'attends. J'irai par la forêt, j'irai par la montagne. Je ne puis demeurer loin de toi plus longtemps."
```
```
<NIL> Tomorrow, at dawn, at the time when the country is whitening, I will go. See,
I know you are waiting for me. I will go through the forest, I will go through the
mountain. I cannot stay far from you any longer.</s>
```
## Generating the tokenizer.json files
You can use the following script to generate the `tokenizer.json` config files
from the hf-hub repos. This requires the `tokenizers` and `sentencepiece`
packages to be install and use the `convert_slow_tokenizer.py` script from this
directory.
```python
from convert_slow_tokenizer import MarianConverter
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-fr-en", use_fast=False)
fast_tokenizer = MarianConverter(tokenizer, index=0).converted()
fast_tokenizer.save(f"tokenizer-marian-base-fr.json")
fast_tokenizer = MarianConverter(tokenizer, index=1).converted()
fast_tokenizer.save(f"tokenizer-marian-base-en.json")
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/marian-mt/convert_slow_tokenizer.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Utilities to convert slow tokenizers in their fast tokenizers counterparts.
All the conversions are grouped here to gather SentencePiece dependencies outside of the fast tokenizers files and
allow to make our dependency on SentencePiece optional.
"""
import warnings
from typing import Dict, List, Tuple
from packaging import version
from pathlib import Path
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import BPE, Unigram, WordPiece
from transformers.utils import is_protobuf_available, requires_backends
from transformers.utils.import_utils import PROTOBUF_IMPORT_ERROR
def import_protobuf(error_message=""):
if is_protobuf_available():
import google.protobuf
if version.parse(google.protobuf.__version__) < version.parse("4.0.0"):
from transformers.utils import sentencepiece_model_pb2
else:
from transformers.utils import sentencepiece_model_pb2_new as sentencepiece_model_pb2
return sentencepiece_model_pb2
else:
raise ImportError(PROTOBUF_IMPORT_ERROR.format(error_message))
class SentencePieceExtractor:
"""
Extractor implementation for SentencePiece trained models. https://github.com/google/sentencepiece
"""
def __init__(self, model: str):
requires_backends(self, "sentencepiece")
from sentencepiece import SentencePieceProcessor
self.sp = SentencePieceProcessor()
self.sp.Load(model)
def extract(self, vocab_scores=None) -> Tuple[Dict[str, int], List[Tuple]]:
"""
By default will return vocab and merges with respect to their order, by sending `vocab_scores` we're going to
order the merges with respect to the piece scores instead.
"""
sp = self.sp
vocab = {sp.id_to_piece(index): index for index in range(sp.GetPieceSize())}
if vocab_scores is not None:
vocab_scores, reverse = dict(vocab_scores), True
else:
vocab_scores, reverse = vocab, False
# Merges
merges = []
for merge, piece_score in vocab_scores.items():
local = []
for index in range(1, len(merge)):
piece_l, piece_r = merge[:index], merge[index:]
if piece_l in vocab and piece_r in vocab:
local.append((piece_l, piece_r, piece_score))
local = sorted(local, key=lambda x: (vocab[x[0]], vocab[x[1]]))
merges.extend(local)
merges = sorted(merges, key=lambda val: val[2], reverse=reverse)
merges = [(val[0], val[1]) for val in merges]
return vocab, merges
def check_number_comma(piece: str) -> bool:
return len(piece) < 2 or piece[-1] != "," or not piece[-2].isdigit()
class Converter:
def __init__(self, original_tokenizer):
self.original_tokenizer = original_tokenizer
def converted(self) -> Tokenizer:
raise NotImplementedError()
class BertConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = False
if hasattr(self.original_tokenizer, "basic_tokenizer"):
tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars
strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents
do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case
tokenizer.normalizer = normalizers.BertNormalizer(
clean_text=True,
handle_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
lowercase=do_lower_case,
)
tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {sep}:0",
pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1",
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
],
)
tokenizer.decoder = decoders.WordPiece(prefix="##")
return tokenizer
class SplinterConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = False
if hasattr(self.original_tokenizer, "basic_tokenizer"):
tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars
strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents
do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case
tokenizer.normalizer = normalizers.BertNormalizer(
clean_text=True,
handle_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
lowercase=do_lower_case,
)
tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
question = str(self.original_tokenizer.question_token)
dot = "."
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
question_token_id = self.original_tokenizer.question_token_id
dot_token_id = self.original_tokenizer.convert_tokens_to_ids(".")
if self.original_tokenizer.padding_side == "right":
pair = f"{cls}:0 $A:0 {question} {dot} {sep}:0 $B:1 {sep}:1"
else:
pair = f"{cls}:0 $A:0 {sep}:0 $B:1 {question} {dot} {sep}:1"
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {sep}:0",
pair=pair,
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
(question, question_token_id),
(dot, dot_token_id),
],
)
tokenizer.decoder = decoders.WordPiece(prefix="##")
return tokenizer
class FunnelConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = False
if hasattr(self.original_tokenizer, "basic_tokenizer"):
tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars
strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents
do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case
tokenizer.normalizer = normalizers.BertNormalizer(
clean_text=True,
handle_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
lowercase=do_lower_case,
)
tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:2 $A:0 {sep}:0", # token_type_id is 2 for Funnel transformer
pair=f"{cls}:2 $A:0 {sep}:0 $B:1 {sep}:1",
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
],
)
tokenizer.decoder = decoders.WordPiece(prefix="##")
return tokenizer
class MPNetConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = False
if hasattr(self.original_tokenizer, "basic_tokenizer"):
tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars
strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents
do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case
tokenizer.normalizer = normalizers.BertNormalizer(
clean_text=True,
handle_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
lowercase=do_lower_case,
)
tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {sep}:0",
pair=f"{cls}:0 $A:0 {sep}:0 {sep}:0 $B:1 {sep}:1", # MPNet uses two [SEP] tokens
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
],
)
tokenizer.decoder = decoders.WordPiece(prefix="##")
return tokenizer
class OpenAIGPTConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
unk_token = self.original_tokenizer.unk_token
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
unk_token=str(unk_token),
end_of_word_suffix="</w>",
fuse_unk=False,
)
)
if tokenizer.token_to_id(str(unk_token)) is not None:
tokenizer.add_special_tokens([str(unk_token)])
tokenizer.normalizer = normalizers.BertNormalizer(lowercase=True)
tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
tokenizer.decoder = decoders.BPEDecoder(suffix="</w>")
return tokenizer
class GPT2Converter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
continuing_subword_prefix="",
end_of_word_suffix="",
fuse_unk=False,
)
)
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=self.original_tokenizer.add_prefix_space)
tokenizer.decoder = decoders.ByteLevel()
if self.original_tokenizer.add_bos_token:
bos = self.original_tokenizer.bos_token
bos_token_id = self.original_tokenizer.bos_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{bos}:0 $A:0",
pair=f"{bos}:0 $A:0 $B:1",
special_tokens=[
(bos, bos_token_id),
],
)
else:
# XXX trim_offsets=False actually means this post_processor doesn't
# really do anything.
tokenizer.post_processor = processors.ByteLevel(trim_offsets=False)
return tokenizer
class HerbertConverter(Converter):
def converted(self) -> Tokenizer:
tokenizer_info_str = "#version:"
token_suffix = "</w>"
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
if tokenizer_info_str in merges[0][0]:
merges = merges[1:]
tokenizer = Tokenizer(
BPE(
vocab,
merges,
dropout=None,
unk_token=self.original_tokenizer.unk_token,
end_of_word_suffix=token_suffix,
)
)
tokenizer.normalizer = normalizers.BertNormalizer(lowercase=False, strip_accents=False)
tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
tokenizer.decoder = decoders.BPEDecoder(suffix=token_suffix)
tokenizer.post_processor = processors.BertProcessing(
sep=(self.original_tokenizer.sep_token, self.original_tokenizer.sep_token_id),
cls=(self.original_tokenizer.cls_token, self.original_tokenizer.cls_token_id),
)
return tokenizer
class RobertaConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
continuing_subword_prefix="",
end_of_word_suffix="",
fuse_unk=False,
)
)
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space)
tokenizer.decoder = decoders.ByteLevel()
tokenizer.post_processor = processors.RobertaProcessing(
sep=(ot.sep_token, ot.sep_token_id),
cls=(ot.cls_token, ot.cls_token_id),
add_prefix_space=ot.add_prefix_space,
trim_offsets=True, # True by default on Roberta (historical)
)
return tokenizer
class RoFormerConverter(Converter):
def converted(self) -> Tokenizer:
from .models.roformer.tokenization_utils import JiebaPreTokenizer
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
strip_accents = False
do_lower_case = False
if hasattr(self.original_tokenizer, "basic_tokenizer"):
strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents
do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case
tokenizer.normalizer = normalizers.BertNormalizer(
clean_text=True,
handle_chinese_chars=False,
strip_accents=strip_accents,
lowercase=do_lower_case,
)
tokenizer.pre_tokenizer = pre_tokenizers.PreTokenizer.custom(JiebaPreTokenizer(vocab))
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {sep}:0",
pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1",
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
],
)
tokenizer.decoder = decoders.WordPiece(prefix="##")
return tokenizer
class DebertaConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
continuing_subword_prefix="",
end_of_word_suffix="",
fuse_unk=False,
)
)
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space)
tokenizer.decoder = decoders.ByteLevel()
tokenizer.post_processor = processors.TemplateProcessing(
single="[CLS]:0 $A:0 [SEP]:0",
pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
special_tokens=[
("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")),
("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")),
],
)
return tokenizer
class SpmConverter(Converter):
def __init__(self, *args):
requires_backends(self, "protobuf")
super().__init__(*args)
# from .utils import sentencepiece_model_pb2 as model_pb2
model_pb2 = import_protobuf()
m = model_pb2.ModelProto()
with open(self.original_tokenizer.vocab_file, "rb") as f:
m.ParseFromString(f.read())
self.proto = m
if self.proto.trainer_spec.byte_fallback:
if not getattr(self, "handle_byte_fallback", None):
warnings.warn(
"The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
" which is not implemented in the fast tokenizers. In practice this means that the fast version of the"
" tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these "
"unknown tokens into a sequence of byte tokens matching the original piece of text."
)
def vocab(self, proto):
return [(piece.piece, piece.score) for piece in proto.pieces]
def unk_id(self, proto):
return proto.trainer_spec.unk_id
def tokenizer(self, proto):
model_type = proto.trainer_spec.model_type
vocab_scores = self.vocab(proto)
unk_id = self.unk_id(proto)
if model_type == 1:
tokenizer = Tokenizer(Unigram(vocab_scores, unk_id))
elif model_type == 2:
_, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract()
bpe_vocab = {word: i for i, (word, score) in enumerate(vocab_scores)}
tokenizer = Tokenizer(
BPE(
bpe_vocab,
merges,
unk_token=proto.trainer_spec.unk_piece,
fuse_unk=True,
)
)
else:
raise Exception(
"You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
)
return tokenizer
def normalizer(self, proto):
precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap
if not precompiled_charsmap:
return normalizers.Sequence([normalizers.Replace(Regex(" {2,}"), " ")])
else:
return normalizers.Sequence(
[normalizers.Precompiled(precompiled_charsmap), normalizers.Replace(Regex(" {2,}"), " ")]
)
def pre_tokenizer(self, replacement, add_prefix_space):
return pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space)
def post_processor(self):
return None
def decoder(self, replacement, add_prefix_space):
return decoders.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space)
def converted(self) -> Tokenizer:
tokenizer = self.tokenizer(self.proto)
# Tokenizer assemble
normalizer = self.normalizer(self.proto)
if normalizer is not None:
tokenizer.normalizer = normalizer
replacement = "▁"
add_prefix_space = True
pre_tokenizer = self.pre_tokenizer(replacement, add_prefix_space)
if pre_tokenizer is not None:
tokenizer.pre_tokenizer = pre_tokenizer
tokenizer.decoder = self.decoder(replacement, add_prefix_space)
post_processor = self.post_processor()
if post_processor:
tokenizer.post_processor = post_processor
return tokenizer
class AlbertConverter(SpmConverter):
def vocab(self, proto):
return [
(piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100)
for piece in proto.pieces
]
def normalizer(self, proto):
list_normalizers = [
normalizers.Replace("``", '"'),
normalizers.Replace("''", '"'),
]
if not self.original_tokenizer.keep_accents:
list_normalizers.append(normalizers.NFKD())
list_normalizers.append(normalizers.StripAccents())
if self.original_tokenizer.do_lower_case:
list_normalizers.append(normalizers.Lowercase())
precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap
if precompiled_charsmap:
list_normalizers.append(normalizers.Precompiled(precompiled_charsmap))
list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " "))
return normalizers.Sequence(list_normalizers)
def post_processor(self):
return processors.TemplateProcessing(
single="[CLS]:0 $A:0 [SEP]:0",
pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
special_tokens=[
("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")),
("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")),
],
)
class BarthezConverter(SpmConverter):
def unk_id(self, proto):
unk_id = 3
return unk_id
def post_processor(self):
return processors.TemplateProcessing(
single="<s> $A </s>",
pair="<s> $A </s> </s> $B </s>",
special_tokens=[
("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")),
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
)
class CamembertConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>NOTUSED", 0.0),
("<pad>", 0.0),
("</s>NOTUSED", 0.0),
("<unk>", 0.0),
("<unk>NOTUSED", -100),
]
# We down-grade the original SentencePiece by -100 to avoid using it and use our added token instead
vocab += [(piece.piece, piece.score) for piece in proto.pieces[1:]]
vocab += [("<mask>", 0.0)]
return vocab
def unk_id(self, proto):
# See vocab unk position
return 3
def post_processor(self):
return processors.TemplateProcessing(
single="<s> $A </s>",
pair="<s> $A </s> </s> $B </s>",
special_tokens=[
("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")),
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
)
class DebertaV2Converter(SpmConverter):
def pre_tokenizer(self, replacement, add_prefix_space):
list_pretokenizers = []
if self.original_tokenizer.split_by_punct:
list_pretokenizers.append(pre_tokenizers.Punctuation(behavior="isolated"))
list_pretokenizers.append(pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space))
return pre_tokenizers.Sequence(list_pretokenizers)
def normalizer(self, proto):
list_normalizers = []
if self.original_tokenizer.do_lower_case:
list_normalizers.append(normalizers.Lowercase())
list_normalizers.append(normalizers.Strip())
precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap
if precompiled_charsmap:
list_normalizers.append(normalizers.Precompiled(precompiled_charsmap))
list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " "))
return normalizers.Sequence(list_normalizers)
def post_processor(self):
return processors.TemplateProcessing(
single="[CLS]:0 $A:0 [SEP]:0",
pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
special_tokens=[
("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")),
("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")),
],
)
class MBartConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [
("ar_AR", 0.0),
("cs_CZ", 0.0),
("de_DE", 0.0),
("en_XX", 0.0),
("es_XX", 0.0),
("et_EE", 0.0),
("fi_FI", 0.0),
("fr_XX", 0.0),
("gu_IN", 0.0),
("hi_IN", 0.0),
("it_IT", 0.0),
("ja_XX", 0.0),
("kk_KZ", 0.0),
("ko_KR", 0.0),
("lt_LT", 0.0),
("lv_LV", 0.0),
("my_MM", 0.0),
("ne_NP", 0.0),
("nl_XX", 0.0),
("ro_RO", 0.0),
("ru_RU", 0.0),
("si_LK", 0.0),
("tr_TR", 0.0),
("vi_VN", 0.0),
("zh_CN", 0.0),
]
vocab += [("<mask>", 0.0)]
return vocab
def unk_id(self, proto):
return 3
def post_processor(self):
return processors.TemplateProcessing(
single="$A </s> en_XX",
pair="$A $B </s> en_XX",
special_tokens=[
("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")),
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
)
class MBart50Converter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
# fmt: off
vocab += [("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0), ("ro_RO", 0.0), ("ru_RU", 0.0), ("si_LK", 0.0), ("tr_TR", 0.0), ("vi_VN", 0.0), ("zh_CN", 0.0), ("af_ZA", 0.0), ("az_AZ", 0.0), ("bn_IN", 0.0), ("fa_IR", 0.0), ("he_IL", 0.0), ("hr_HR", 0.0), ("id_ID", 0.0), ("ka_GE", 0.0), ("km_KH", 0.0), ("mk_MK", 0.0), ("ml_IN", 0.0), ("mn_MN", 0.0), ("mr_IN", 0.0), ("pl_PL", 0.0), ("ps_AF", 0.0), ("pt_XX", 0.0), ("sv_SE", 0.0), ("sw_KE", 0.0), ("ta_IN", 0.0), ("te_IN", 0.0), ("th_TH", 0.0), ("tl_XX", 0.0), ("uk_UA", 0.0), ("ur_PK", 0.0), ("xh_ZA", 0.0), ("gl_ES", 0.0), ("sl_SI", 0.0)]
# fmt: on
vocab += [("<mask>", 0.0)]
return vocab
def unk_id(self, proto):
return 3
def post_processor(self):
return processors.TemplateProcessing(
single="en_XX $A </s>",
pair="en_XX $A $B </s>",
special_tokens=[
("en_XX", self.original_tokenizer.convert_tokens_to_ids("en_XX")),
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
)
class NllbConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [
# fmt: off
('ace_Arab', 0.0), ('ace_Latn', 0.0), ('acm_Arab', 0.0), ('acq_Arab', 0.0), ('aeb_Arab', 0.0), ('afr_Latn', 0.0), ('ajp_Arab', 0.0), ('aka_Latn', 0.0), ('amh_Ethi', 0.0), ('apc_Arab', 0.0), ('arb_Arab', 0.0), ('ars_Arab', 0.0), ('ary_Arab', 0.0), ('arz_Arab', 0.0), ('asm_Beng', 0.0), ('ast_Latn', 0.0), ('awa_Deva', 0.0), ('ayr_Latn', 0.0), ('azb_Arab', 0.0), ('azj_Latn', 0.0), ('bak_Cyrl', 0.0), ('bam_Latn', 0.0), ('ban_Latn', 0.0), ('bel_Cyrl', 0.0), ('bem_Latn', 0.0), ('ben_Beng', 0.0), ('bho_Deva', 0.0), ('bjn_Arab', 0.0), ('bjn_Latn', 0.0), ('bod_Tibt', 0.0), ('bos_Latn', 0.0), ('bug_Latn', 0.0), ('bul_Cyrl', 0.0), ('cat_Latn', 0.0), ('ceb_Latn', 0.0), ('ces_Latn', 0.0), ('cjk_Latn', 0.0), ('ckb_Arab', 0.0), ('crh_Latn', 0.0), ('cym_Latn', 0.0), ('dan_Latn', 0.0), ('deu_Latn', 0.0), ('dik_Latn', 0.0), ('dyu_Latn', 0.0), ('dzo_Tibt', 0.0), ('ell_Grek', 0.0), ('eng_Latn', 0.0), ('epo_Latn', 0.0), ('est_Latn', 0.0), ('eus_Latn', 0.0), ('ewe_Latn', 0.0), ('fao_Latn', 0.0), ('pes_Arab', 0.0), ('fij_Latn', 0.0), ('fin_Latn', 0.0), ('fon_Latn', 0.0), ('fra_Latn', 0.0), ('fur_Latn', 0.0), ('fuv_Latn', 0.0), ('gla_Latn', 0.0), ('gle_Latn', 0.0), ('glg_Latn', 0.0), ('grn_Latn', 0.0), ('guj_Gujr', 0.0), ('hat_Latn', 0.0), ('hau_Latn', 0.0), ('heb_Hebr', 0.0), ('hin_Deva', 0.0), ('hne_Deva', 0.0), ('hrv_Latn', 0.0), ('hun_Latn', 0.0), ('hye_Armn', 0.0), ('ibo_Latn', 0.0), ('ilo_Latn', 0.0), ('ind_Latn', 0.0), ('isl_Latn', 0.0), ('ita_Latn', 0.0), ('jav_Latn', 0.0), ('jpn_Jpan', 0.0), ('kab_Latn', 0.0), ('kac_Latn', 0.0), ('kam_Latn', 0.0), ('kan_Knda', 0.0), ('kas_Arab', 0.0), ('kas_Deva', 0.0), ('kat_Geor', 0.0), ('knc_Arab', 0.0), ('knc_Latn', 0.0), ('kaz_Cyrl', 0.0), ('kbp_Latn', 0.0), ('kea_Latn', 0.0), ('khm_Khmr', 0.0), ('kik_Latn', 0.0), ('kin_Latn', 0.0), ('kir_Cyrl', 0.0), ('kmb_Latn', 0.0), ('kon_Latn', 0.0), ('kor_Hang', 0.0), ('kmr_Latn', 0.0), ('lao_Laoo', 0.0), ('lvs_Latn', 0.0), ('lij_Latn', 0.0), ('lim_Latn', 0.0), ('lin_Latn', 0.0), ('lit_Latn', 0.0), ('lmo_Latn', 0.0), ('ltg_Latn', 0.0), ('ltz_Latn', 0.0), ('lua_Latn', 0.0), ('lug_Latn', 0.0), ('luo_Latn', 0.0), ('lus_Latn', 0.0), ('mag_Deva', 0.0), ('mai_Deva', 0.0), ('mal_Mlym', 0.0), ('mar_Deva', 0.0), ('min_Latn', 0.0), ('mkd_Cyrl', 0.0), ('plt_Latn', 0.0), ('mlt_Latn', 0.0), ('mni_Beng', 0.0), ('khk_Cyrl', 0.0), ('mos_Latn', 0.0), ('mri_Latn', 0.0), ('zsm_Latn', 0.0), ('mya_Mymr', 0.0), ('nld_Latn', 0.0), ('nno_Latn', 0.0), ('nob_Latn', 0.0), ('npi_Deva', 0.0), ('nso_Latn', 0.0), ('nus_Latn', 0.0), ('nya_Latn', 0.0), ('oci_Latn', 0.0), ('gaz_Latn', 0.0), ('ory_Orya', 0.0), ('pag_Latn', 0.0), ('pan_Guru', 0.0), ('pap_Latn', 0.0), ('pol_Latn', 0.0), ('por_Latn', 0.0), ('prs_Arab', 0.0), ('pbt_Arab', 0.0), ('quy_Latn', 0.0), ('ron_Latn', 0.0), ('run_Latn', 0.0), ('rus_Cyrl', 0.0), ('sag_Latn', 0.0), ('san_Deva', 0.0), ('sat_Beng', 0.0), ('scn_Latn', 0.0), ('shn_Mymr', 0.0), ('sin_Sinh', 0.0), ('slk_Latn', 0.0), ('slv_Latn', 0.0), ('smo_Latn', 0.0), ('sna_Latn', 0.0), ('snd_Arab', 0.0), ('som_Latn', 0.0), ('sot_Latn', 0.0), ('spa_Latn', 0.0), ('als_Latn', 0.0), ('srd_Latn', 0.0), ('srp_Cyrl', 0.0), ('ssw_Latn', 0.0), ('sun_Latn', 0.0), ('swe_Latn', 0.0), ('swh_Latn', 0.0), ('szl_Latn', 0.0), ('tam_Taml', 0.0), ('tat_Cyrl', 0.0), ('tel_Telu', 0.0), ('tgk_Cyrl', 0.0), ('tgl_Latn', 0.0), ('tha_Thai', 0.0), ('tir_Ethi', 0.0), ('taq_Latn', 0.0), ('taq_Tfng', 0.0), ('tpi_Latn', 0.0), ('tsn_Latn', 0.0), ('tso_Latn', 0.0), ('tuk_Latn', 0.0), ('tum_Latn', 0.0), ('tur_Latn', 0.0), ('twi_Latn', 0.0), ('tzm_Tfng', 0.0), ('uig_Arab', 0.0), ('ukr_Cyrl', 0.0), ('umb_Latn', 0.0), ('urd_Arab', 0.0), ('uzn_Latn', 0.0), ('vec_Latn', 0.0), ('vie_Latn', 0.0), ('war_Latn', 0.0), ('wol_Latn', 0.0), ('xho_Latn', 0.0), ('ydd_Hebr', 0.0), ('yor_Latn', 0.0), ('yue_Hant', 0.0), ('zho_Hans', 0.0), ('zho_Hant', 0.0), ('zul_Latn', 0.0)
# fmt: on
]
vocab += [("<mask>", 0.0)]
return vocab
def unk_id(self, proto):
return 3
def post_processor(self):
return processors.TemplateProcessing(
single="eng_Latn $A </s>",
pair="eng_Latn $A $B </s>",
special_tokens=[
("eng_Latn", self.original_tokenizer.convert_tokens_to_ids("eng_Latn")),
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
)
class SeamlessM4TConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<pad>", 0.0),
("<unk>", 0.0),
("<s>", 0.0),
("</s>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
return vocab
def unk_id(self, proto):
return self.original_tokenizer.unk_token_id
def post_processor(self):
return processors.TemplateProcessing(
single="__eng__ $A </s>",
pair="__eng__ $A $B </s>",
special_tokens=[
("__eng__", self.original_tokenizer.convert_tokens_to_ids("__eng__")),
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
)
class XLMRobertaConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
vocab += [("<mask>", 0.0)]
return vocab
def unk_id(self, proto):
unk_id = 3
return unk_id
def post_processor(self):
return processors.TemplateProcessing(
single="<s> $A </s>",
pair="<s> $A </s> </s> $B </s>",
special_tokens=[
("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")),
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
)
class XLNetConverter(SpmConverter):
def vocab(self, proto):
return [
(piece.piece, piece.score) if check_number_comma(piece.piece) else (piece.piece, piece.score - 100)
for piece in proto.pieces
]
def normalizer(self, proto):
list_normalizers = [
normalizers.Replace("``", '"'),
normalizers.Replace("''", '"'),
]
if not self.original_tokenizer.keep_accents:
list_normalizers.append(normalizers.NFKD())
list_normalizers.append(normalizers.StripAccents())
if self.original_tokenizer.do_lower_case:
list_normalizers.append(normalizers.Lowercase())
precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap
if precompiled_charsmap:
list_normalizers.append(normalizers.Precompiled(precompiled_charsmap))
list_normalizers.append(normalizers.Replace(Regex(" {2,}"), " "))
return normalizers.Sequence(list_normalizers)
def post_processor(self):
return processors.TemplateProcessing(
single="$A:0 <sep>:0 <cls>:2",
pair="$A:0 <sep>:0 $B:1 <sep>:1 <cls>:2",
special_tokens=[
("<sep>", self.original_tokenizer.convert_tokens_to_ids("<sep>")),
("<cls>", self.original_tokenizer.convert_tokens_to_ids("<cls>")),
],
)
class ReformerConverter(SpmConverter):
pass
class RemBertConverter(SpmConverter):
# Inspired from AlbertConverter
def normalizer(self, proto):
list_normalizers = [
normalizers.Replace("``", '"'),
normalizers.Replace("''", '"'),
normalizers.Replace(Regex(" {2,}"), " "),
]
if not self.original_tokenizer.keep_accents:
list_normalizers.append(normalizers.NFKD())
list_normalizers.append(normalizers.StripAccents())
if self.original_tokenizer.do_lower_case:
list_normalizers.append(normalizers.Lowercase())
precompiled_charsmap = proto.normalizer_spec.precompiled_charsmap
if precompiled_charsmap:
list_normalizers.append(normalizers.Precompiled(precompiled_charsmap))
return normalizers.Sequence(list_normalizers)
def post_processor(self):
return processors.TemplateProcessing(
single="[CLS]:0 $A:0 [SEP]:0",
pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
special_tokens=[
("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")),
("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")),
],
)
class BertGenerationConverter(SpmConverter):
pass
class PegasusConverter(SpmConverter):
def vocab(self, proto):
vocab = [
(self.original_tokenizer.pad_token, 0.0),
(self.original_tokenizer.eos_token, 0.0),
]
if self.original_tokenizer.mask_token_sent is not None:
vocab += [(self.original_tokenizer.mask_token_sent, 0.0)]
if (
self.original_tokenizer.mask_token is not None
and self.original_tokenizer.mask_token_id < self.original_tokenizer.offset
):
vocab += [(self.original_tokenizer.mask_token, 0.0)]
vocab += [(f"<unk_{i}>", -100.0) for i in range(2, self.original_tokenizer.offset)]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[2:]]
return vocab
def unk_id(self, proto):
return proto.trainer_spec.unk_id + self.original_tokenizer.offset
def pre_tokenizer(self, replacement, add_prefix_space):
return pre_tokenizers.Sequence(
[
pre_tokenizers.WhitespaceSplit(),
pre_tokenizers.Metaspace(replacement=replacement, add_prefix_space=add_prefix_space),
]
)
def post_processor(self):
eos = self.original_tokenizer.eos_token
special_tokens = [
(eos, self.original_tokenizer.eos_token_id),
]
return processors.TemplateProcessing(single=["$A", eos], pair=["$A", "$B", eos], special_tokens=special_tokens)
class T5Converter(SpmConverter):
def vocab(self, proto):
num_extra_ids = self.original_tokenizer._extra_ids
vocab = [(piece.piece, piece.score) for piece in proto.pieces]
vocab += [(f"<extra_id_{i}>", 0.0) for i in range(num_extra_ids - 1, -1, -1)]
return vocab
def post_processor(self):
return processors.TemplateProcessing(
single=["$A", "</s>"],
pair=["$A", "</s>", "$B", "</s>"],
special_tokens=[
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
)
class WhisperConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
continuing_subword_prefix="",
end_of_word_suffix="",
fuse_unk=False,
)
)
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=self.original_tokenizer.add_prefix_space)
tokenizer.decoder = decoders.ByteLevel()
prefix_token_ids = self.original_tokenizer.prefix_tokens
prefixes = self.original_tokenizer.convert_ids_to_tokens(prefix_token_ids)
eos = self.original_tokenizer.eos_token
eos_token_id = self.original_tokenizer.eos_token_id
prefix_template = " ".join([f"{token}:0" for token in prefixes])
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{prefix_template} $A:0 {eos}:0",
pair=f"{prefix_template} $A:0 $B:1 {eos}:1",
special_tokens=[
(eos, eos_token_id),
*zip(prefixes, prefix_token_ids),
],
)
return tokenizer
class BigBirdConverter(SpmConverter):
def post_processor(self):
return processors.TemplateProcessing(
single="[CLS]:0 $A:0 [SEP]:0",
pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
special_tokens=[
("[CLS]", self.original_tokenizer.convert_tokens_to_ids("[CLS]")),
("[SEP]", self.original_tokenizer.convert_tokens_to_ids("[SEP]")),
],
)
class CLIPConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
unk_token = self.original_tokenizer.unk_token
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
continuing_subword_prefix="",
end_of_word_suffix="</w>",
fuse_unk=False,
unk_token=str(unk_token),
)
)
tokenizer.normalizer = normalizers.Sequence(
[normalizers.NFC(), normalizers.Replace(Regex(r"\s+"), " "), normalizers.Lowercase()]
)
tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
[
pre_tokenizers.Split(
Regex(r"""'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+"""),
behavior="removed",
invert=True,
),
pre_tokenizers.ByteLevel(add_prefix_space=False),
]
)
tokenizer.decoder = decoders.ByteLevel()
# Hack to have a ByteLevel and TemplaceProcessor
tokenizer.post_processor = processors.RobertaProcessing(
sep=(self.original_tokenizer.eos_token, self.original_tokenizer.eos_token_id),
cls=(self.original_tokenizer.bos_token, self.original_tokenizer.bos_token_id),
add_prefix_space=False,
trim_offsets=False,
)
return tokenizer
class LayoutLMv2Converter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = True
if hasattr(self.original_tokenizer, "basic_tokenizer"):
tokenize_chinese_chars = self.original_tokenizer.basic_tokenizer.tokenize_chinese_chars
strip_accents = self.original_tokenizer.basic_tokenizer.strip_accents
do_lower_case = self.original_tokenizer.basic_tokenizer.do_lower_case
tokenizer.normalizer = normalizers.BertNormalizer(
clean_text=True,
handle_chinese_chars=tokenize_chinese_chars,
strip_accents=strip_accents,
lowercase=do_lower_case,
)
tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls}:0 $A:0 {sep}:0",
pair=f"{cls}:0 $A:0 {sep}:0 $B:1 {sep}:1",
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
],
)
tokenizer.decoder = decoders.WordPiece(prefix="##")
return tokenizer
class BlenderbotConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
continuing_subword_prefix="",
end_of_word_suffix="",
fuse_unk=False,
)
)
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space)
tokenizer.decoder = decoders.ByteLevel()
tokenizer.post_processor = processors.TemplateProcessing(
single=f"$A:0 {ot.eos_token}:0",
special_tokens=[
(ot.eos_token, ot.eos_token_id),
],
)
return tokenizer
class XGLMConverter(SpmConverter):
def vocab(self, proto):
vocab = [
("<s>", 0.0),
("<pad>", 0.0),
("</s>", 0.0),
("<unk>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
# fmt: off
vocab += [("<madeupword0>", 0.0), ("<madeupword1>", 0.0), ("<madeupword2>", 0.0), ("<madeupword3>", 0.0), ("<madeupword4>", 0.0), ("<madeupword5>", 0.0), ("<madeupword6>", 0.0)]
# fmt: on
return vocab
def unk_id(self, proto):
unk_id = 3
return unk_id
def post_processor(self):
return processors.TemplateProcessing(
single="</s> $A",
pair="</s> $A </s> </s> $B",
special_tokens=[
("<s>", self.original_tokenizer.convert_tokens_to_ids("<s>")),
("</s>", self.original_tokenizer.convert_tokens_to_ids("</s>")),
],
)
class LlamaConverter(SpmConverter):
handle_byte_fallback = True
def vocab(self, proto):
vocab = [
("<unk>", 0.0),
("<s>", 0.0),
("</s>", 0.0),
]
vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
return vocab
def unk_id(self, proto):
unk_id = 0
return unk_id
def decoder(self, replacement, add_prefix_space):
return decoders.Sequence(
[
decoders.Replace("▁", " "),
decoders.ByteFallback(),
decoders.Fuse(),
decoders.Strip(content=" ", left=1),
]
)
def tokenizer(self, proto):
model_type = proto.trainer_spec.model_type
vocab_scores = self.vocab(proto)
if model_type == 1:
import tokenizers
if version.parse(tokenizers.__version__) < version.parse("0.14.0"):
tokenizer = Tokenizer(Unigram(vocab_scores, 0))
else:
tokenizer = Tokenizer(Unigram(vocab_scores, 0, byte_fallback=True))
elif model_type == 2:
_, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
tokenizer = Tokenizer(
BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
)
tokenizer.add_special_tokens(
[
AddedToken("<unk>", normalized=False, special=True),
AddedToken("<s>", normalized=False, special=True),
AddedToken("</s>", normalized=False, special=True),
]
)
else:
raise Exception(
"You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
)
return tokenizer
def normalizer(self, proto):
return normalizers.Sequence(
[
normalizers.Prepend(prepend="▁"),
normalizers.Replace(pattern=" ", content="▁"),
]
)
def pre_tokenizer(self, replacement, add_prefix_space):
return None
def post_processor(self):
# the processor is defined in the LlamaTokenizerFast class.
return None
class MarkupLMConverter(Converter):
def converted(self) -> Tokenizer:
ot = self.original_tokenizer
vocab = ot.encoder
merges = list(ot.bpe_ranks.keys())
tokenizer = Tokenizer(
BPE(
vocab=vocab,
merges=merges,
dropout=None,
continuing_subword_prefix="",
end_of_word_suffix="",
fuse_unk=False,
unk_token=self.original_tokenizer.unk_token,
)
)
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=ot.add_prefix_space)
tokenizer.decoder = decoders.ByteLevel()
cls = str(self.original_tokenizer.cls_token)
sep = str(self.original_tokenizer.sep_token)
cls_token_id = self.original_tokenizer.cls_token_id
sep_token_id = self.original_tokenizer.sep_token_id
tokenizer.post_processor = processors.TemplateProcessing(
single=f"{cls} $A {sep}",
pair=f"{cls} $A {sep} $B {sep}",
special_tokens=[
(cls, cls_token_id),
(sep, sep_token_id),
],
)
return tokenizer
class MarianConverter(SpmConverter):
def __init__(self, *args, index: int = 0):
requires_backends(self, "protobuf")
super(SpmConverter, self).__init__(*args)
# from .utils import sentencepiece_model_pb2 as model_pb2
model_pb2 = import_protobuf()
m = model_pb2.ModelProto()
print(self.original_tokenizer.spm_files)
with open(self.original_tokenizer.spm_files[index], "rb") as f:
m.ParseFromString(f.read())
self.proto = m
print(self.original_tokenizer)
#with open(self.original_tokenizer.vocab_path, "r") as f:
dir_path = Path(self.original_tokenizer.spm_files[0]).parents[0]
with open(dir_path / "vocab.json", "r") as f:
import json
self._vocab = json.load(f)
if self.proto.trainer_spec.byte_fallback:
if not getattr(self, "handle_byte_fallback", None):
warnings.warn(
"The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
" which is not implemented in the fast tokenizers. In practice this means that the fast version of the"
" tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these "
"unknown tokens into a sequence of byte tokens matching the original piece of text."
)
def vocab(self, proto):
vocab_size = max(self._vocab.values()) + 1
vocab = [("<NIL>", -100) for _ in range(vocab_size)]
for piece in proto.pieces:
try:
index = self._vocab[piece.piece]
except Exception:
print(f"Ignored missing piece {piece.piece}")
vocab[index] = (piece.piece, piece.score)
return vocab
SLOW_TO_FAST_CONVERTERS = {
"AlbertTokenizer": AlbertConverter,
"BartTokenizer": RobertaConverter,
"BarthezTokenizer": BarthezConverter,
"BertTokenizer": BertConverter,
"BigBirdTokenizer": BigBirdConverter,
"BlenderbotTokenizer": BlenderbotConverter,
"CamembertTokenizer": CamembertConverter,
"CLIPTokenizer": CLIPConverter,
"CodeGenTokenizer": GPT2Converter,
"ConvBertTokenizer": BertConverter,
"DebertaTokenizer": DebertaConverter,
"DebertaV2Tokenizer": DebertaV2Converter,
"DistilBertTokenizer": BertConverter,
"DPRReaderTokenizer": BertConverter,
"DPRQuestionEncoderTokenizer": BertConverter,
"DPRContextEncoderTokenizer": BertConverter,
"ElectraTokenizer": BertConverter,
"FNetTokenizer": AlbertConverter,
"FunnelTokenizer": FunnelConverter,
"GPT2Tokenizer": GPT2Converter,
"HerbertTokenizer": HerbertConverter,
"LayoutLMTokenizer": BertConverter,
"LayoutLMv2Tokenizer": BertConverter,
"LayoutLMv3Tokenizer": RobertaConverter,
"LayoutXLMTokenizer": XLMRobertaConverter,
"LongformerTokenizer": RobertaConverter,
"LEDTokenizer": RobertaConverter,
"LxmertTokenizer": BertConverter,
"MarkupLMTokenizer": MarkupLMConverter,
"MBartTokenizer": MBartConverter,
"MBart50Tokenizer": MBart50Converter,
"MPNetTokenizer": MPNetConverter,
"MobileBertTokenizer": BertConverter,
"MvpTokenizer": RobertaConverter,
"NllbTokenizer": NllbConverter,
"OpenAIGPTTokenizer": OpenAIGPTConverter,
"PegasusTokenizer": PegasusConverter,
"RealmTokenizer": BertConverter,
"ReformerTokenizer": ReformerConverter,
"RemBertTokenizer": RemBertConverter,
"RetriBertTokenizer": BertConverter,
"RobertaTokenizer": RobertaConverter,
"RoFormerTokenizer": RoFormerConverter,
"SeamlessM4TTokenizer": SeamlessM4TConverter,
"SqueezeBertTokenizer": BertConverter,
"T5Tokenizer": T5Converter,
"WhisperTokenizer": WhisperConverter,
"XLMRobertaTokenizer": XLMRobertaConverter,
"XLNetTokenizer": XLNetConverter,
"SplinterTokenizer": SplinterConverter,
"XGLMTokenizer": XGLMConverter,
"LlamaTokenizer": LlamaConverter,
"CodeLlamaTokenizer": LlamaConverter,
}
def convert_slow_tokenizer(transformer_tokenizer) -> Tokenizer:
"""
Utilities to convert a slow tokenizer instance in a fast tokenizer instance.
Args:
transformer_tokenizer ([`~tokenization_utils_base.PreTrainedTokenizer`]):
Instance of a slow tokenizer to convert in the backend tokenizer for
[`~tokenization_utils_base.PreTrainedTokenizerFast`].
Return:
A instance of [`~tokenizers.Tokenizer`] to be used as the backend tokenizer of a
[`~tokenization_utils_base.PreTrainedTokenizerFast`]
"""
tokenizer_class_name = transformer_tokenizer.__class__.__name__
if tokenizer_class_name not in SLOW_TO_FAST_CONVERTERS:
raise ValueError(
f"An instance of tokenizer class {tokenizer_class_name} cannot be converted in a Fast tokenizer instance."
" No converter was found. Currently available slow->fast convertors:"
f" {list(SLOW_TO_FAST_CONVERTERS.keys())}"
)
converter_class = SLOW_TO_FAST_CONVERTERS[tokenizer_class_name]
return converter_class(transformer_tokenizer).converted()
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/stable-lm/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::quantized_stable_lm::Model as QStableLM;
use candle_transformers::models::stable_lm::{Config, Model as StableLM};
use candle::{DType, Device, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
enum Model {
StableLM(StableLM),
Quantized(QStableLM),
}
struct TextGeneration {
model: Model,
device: Device,
tokenizer: TokenOutputStream,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
}
impl TextGeneration {
#[allow(clippy::too_many_arguments)]
fn new(
model: Model,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
top_p: Option<f64>,
repeat_penalty: f32,
repeat_last_n: usize,
device: &Device,
) -> Self {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer: TokenOutputStream::new(tokenizer),
logits_processor,
repeat_penalty,
repeat_last_n,
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
self.tokenizer.clear();
let mut tokens = self
.tokenizer
.tokenizer()
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
for &t in tokens.iter() {
if let Some(t) = self.tokenizer.next_token(t)? {
print!("{t}")
}
}
std::io::stdout().flush()?;
let mut generated_tokens = 0usize;
let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
Some(token) => token,
None => anyhow::bail!("cannot find the <|endoftext|> token"),
};
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let context_size = if index > 0 { 1 } else { tokens.len() };
let start_pos = tokens.len().saturating_sub(context_size);
let ctxt = &tokens[start_pos..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = match &mut self.model {
Model::StableLM(m) => m.forward(&input, start_pos)?,
Model::Quantized(m) => m.forward(&input, start_pos)?,
};
let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(self.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
generated_tokens += 1;
if next_token == eos_token {
break;
}
if let Some(t) = self.tokenizer.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
}
let dt = start_gen.elapsed();
if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
std::io::stdout().flush()?;
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
generated_tokens as f64 / dt.as_secs_f64(),
);
Ok(())
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
use_flash_attn: bool,
#[arg(long)]
prompt: String,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, short = 'n', default_value_t = 100)]
sample_len: usize,
#[arg(long, default_value = "lmz/candle-stablelm-3b-4e1t")]
model_id: String,
#[arg(long, default_value = "main")]
revision: String,
#[arg(long)]
tokenizer_file: Option<String>,
#[arg(long)]
weight_files: Option<String>,
#[arg(long)]
quantized: bool,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
println!(
"avx: {}, neon: {}, simd128: {}, f16c: {}",
candle::utils::with_avx(),
candle::utils::with_neon(),
candle::utils::with_simd128(),
candle::utils::with_f16c()
);
println!(
"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
args.temperature.unwrap_or(0.),
args.repeat_penalty,
args.repeat_last_n
);
let start = std::time::Instant::now();
let api = Api::new()?;
let repo = api.repo(Repo::with_revision(
args.model_id,
RepoType::Model,
args.revision,
));
let tokenizer_filename = match args.tokenizer_file {
Some(file) => std::path::PathBuf::from(file),
None => repo.get("tokenizer.json")?,
};
let filenames = match args.weight_files {
Some(files) => files
.split(',')
.map(std::path::PathBuf::from)
.collect::<Vec<_>>(),
None => {
if args.quantized {
vec![repo.get("model-q4k.gguf")?]
} else {
vec![repo.get("model.safetensors")?]
}
}
};
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let config = Config::stablelm_3b_4e1t(args.use_flash_attn);
let device = candle_examples::device(args.cpu)?;
let (model, device) = if args.quantized {
let filename = &filenames[0];
let vb =
candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
let model = QStableLM::new(&config, vb)?;
(Model::Quantized(model), Device::Cpu)
} else {
let dtype = if device.is_cuda() {
DType::BF16
} else {
DType::F32
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
let model = StableLM::new(&config, vb)?;
(Model::StableLM(model), device)
};
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(
model,
tokenizer,
args.seed,
args.temperature,
args.top_p,
args.repeat_penalty,
args.repeat_last_n,
&device,
);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/stable-lm/README.md | # candle-stable-lm
StableLM-3B-4E1T is a 3 billion parameter decoder-only language model
pre-trained on 1 trillion tokens of diverse English and code datasets for 4
epochs. See the [HuggingFace Hub Model
Card](https://huggingface.co/stabilityai/stablelm-3b-4e1t).
Note that this model is gated so you will have to request access on the Hub in
order to be able to use it.
## Running some example
```bash
$ cargo run --example stable-lm --release --features cuda -- --prompt 'What is the most efficient programming language in use?' --sample-len 150
avx: true, neon: false, simd128: false, f16c: true
temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
retrieved the files in 126.593µs
loaded the model in 3.474148965s
What is the most efficient programming language in use?
The answer to this question depends on what you mean by "efficient". If you're talking about speed, then C++ and Java are probably your best bets. But if you're talking about ease of development, then Python is probably the way to go.
Python is a high-level, interpreted language that is easy to learn and use. It has a large community of developers who are always working on new features and improvements.
C++ is a low-level, compiled language that can be used for both desktop applications and web development. It's more difficult to learn than Python but offers greater control over the code.
Java is another high-level language that is popular with programmers because it runs on many different platforms (including Android phones
150 tokens generated (37.61 token/s)
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v8/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
mod model;
use model::{Multiples, YoloV8, YoloV8Pose};
use candle::{DType, Device, IndexOp, Result, Tensor};
use candle_nn::{Module, VarBuilder};
use candle_transformers::object_detection::{non_maximum_suppression, Bbox, KeyPoint};
use clap::{Parser, ValueEnum};
use image::DynamicImage;
// Keypoints as reported by ChatGPT :)
// Nose
// Left Eye
// Right Eye
// Left Ear
// Right Ear
// Left Shoulder
// Right Shoulder
// Left Elbow
// Right Elbow
// Left Wrist
// Right Wrist
// Left Hip
// Right Hip
// Left Knee
// Right Knee
// Left Ankle
// Right Ankle
const KP_CONNECTIONS: [(usize, usize); 16] = [
(0, 1),
(0, 2),
(1, 3),
(2, 4),
(5, 6),
(5, 11),
(6, 12),
(11, 12),
(5, 7),
(6, 8),
(7, 9),
(8, 10),
(11, 13),
(12, 14),
(13, 15),
(14, 16),
];
// Model architecture from https://github.com/ultralytics/ultralytics/issues/189
// https://github.com/tinygrad/tinygrad/blob/master/examples/yolov8.py
pub fn report_detect(
pred: &Tensor,
img: DynamicImage,
w: usize,
h: usize,
confidence_threshold: f32,
nms_threshold: f32,
legend_size: u32,
) -> Result<DynamicImage> {
let pred = pred.to_device(&Device::Cpu)?;
let (pred_size, npreds) = pred.dims2()?;
let nclasses = pred_size - 4;
// The bounding boxes grouped by (maximum) class index.
let mut bboxes: Vec<Vec<Bbox<Vec<KeyPoint>>>> = (0..nclasses).map(|_| vec![]).collect();
// Extract the bounding boxes for which confidence is above the threshold.
for index in 0..npreds {
let pred = Vec::<f32>::try_from(pred.i((.., index))?)?;
let confidence = *pred[4..].iter().max_by(|x, y| x.total_cmp(y)).unwrap();
if confidence > confidence_threshold {
let mut class_index = 0;
for i in 0..nclasses {
if pred[4 + i] > pred[4 + class_index] {
class_index = i
}
}
if pred[class_index + 4] > 0. {
let bbox = Bbox {
xmin: pred[0] - pred[2] / 2.,
ymin: pred[1] - pred[3] / 2.,
xmax: pred[0] + pred[2] / 2.,
ymax: pred[1] + pred[3] / 2.,
confidence,
data: vec![],
};
bboxes[class_index].push(bbox)
}
}
}
non_maximum_suppression(&mut bboxes, nms_threshold);
// Annotate the original image and print boxes information.
let (initial_h, initial_w) = (img.height(), img.width());
let w_ratio = initial_w as f32 / w as f32;
let h_ratio = initial_h as f32 / h as f32;
let mut img = img.to_rgb8();
let font = Vec::from(include_bytes!("roboto-mono-stripped.ttf") as &[u8]);
let font = rusttype::Font::try_from_vec(font);
for (class_index, bboxes_for_class) in bboxes.iter().enumerate() {
for b in bboxes_for_class.iter() {
println!(
"{}: {:?}",
candle_examples::coco_classes::NAMES[class_index],
b
);
let xmin = (b.xmin * w_ratio) as i32;
let ymin = (b.ymin * h_ratio) as i32;
let dx = (b.xmax - b.xmin) * w_ratio;
let dy = (b.ymax - b.ymin) * h_ratio;
if dx >= 0. && dy >= 0. {
imageproc::drawing::draw_hollow_rect_mut(
&mut img,
imageproc::rect::Rect::at(xmin, ymin).of_size(dx as u32, dy as u32),
image::Rgb([255, 0, 0]),
);
}
if legend_size > 0 {
if let Some(font) = font.as_ref() {
imageproc::drawing::draw_filled_rect_mut(
&mut img,
imageproc::rect::Rect::at(xmin, ymin).of_size(dx as u32, legend_size),
image::Rgb([170, 0, 0]),
);
let legend = format!(
"{} {:.0}%",
candle_examples::coco_classes::NAMES[class_index],
100. * b.confidence
);
imageproc::drawing::draw_text_mut(
&mut img,
image::Rgb([255, 255, 255]),
xmin,
ymin,
rusttype::Scale::uniform(legend_size as f32 - 1.),
font,
&legend,
)
}
}
}
}
Ok(DynamicImage::ImageRgb8(img))
}
pub fn report_pose(
pred: &Tensor,
img: DynamicImage,
w: usize,
h: usize,
confidence_threshold: f32,
nms_threshold: f32,
) -> Result<DynamicImage> {
let pred = pred.to_device(&Device::Cpu)?;
let (pred_size, npreds) = pred.dims2()?;
if pred_size != 17 * 3 + 4 + 1 {
candle::bail!("unexpected pred-size {pred_size}");
}
let mut bboxes = vec![];
// Extract the bounding boxes for which confidence is above the threshold.
for index in 0..npreds {
let pred = Vec::<f32>::try_from(pred.i((.., index))?)?;
let confidence = pred[4];
if confidence > confidence_threshold {
let keypoints = (0..17)
.map(|i| KeyPoint {
x: pred[3 * i + 5],
y: pred[3 * i + 6],
mask: pred[3 * i + 7],
})
.collect::<Vec<_>>();
let bbox = Bbox {
xmin: pred[0] - pred[2] / 2.,
ymin: pred[1] - pred[3] / 2.,
xmax: pred[0] + pred[2] / 2.,
ymax: pred[1] + pred[3] / 2.,
confidence,
data: keypoints,
};
bboxes.push(bbox)
}
}
let mut bboxes = vec![bboxes];
non_maximum_suppression(&mut bboxes, nms_threshold);
let bboxes = &bboxes[0];
// Annotate the original image and print boxes information.
let (initial_h, initial_w) = (img.height(), img.width());
let w_ratio = initial_w as f32 / w as f32;
let h_ratio = initial_h as f32 / h as f32;
let mut img = img.to_rgb8();
for b in bboxes.iter() {
println!("{b:?}");
let xmin = (b.xmin * w_ratio) as i32;
let ymin = (b.ymin * h_ratio) as i32;
let dx = (b.xmax - b.xmin) * w_ratio;
let dy = (b.ymax - b.ymin) * h_ratio;
if dx >= 0. && dy >= 0. {
imageproc::drawing::draw_hollow_rect_mut(
&mut img,
imageproc::rect::Rect::at(xmin, ymin).of_size(dx as u32, dy as u32),
image::Rgb([255, 0, 0]),
);
}
for kp in b.data.iter() {
if kp.mask < 0.6 {
continue;
}
let x = (kp.x * w_ratio) as i32;
let y = (kp.y * h_ratio) as i32;
imageproc::drawing::draw_filled_circle_mut(
&mut img,
(x, y),
2,
image::Rgb([0, 255, 0]),
);
}
for &(idx1, idx2) in KP_CONNECTIONS.iter() {
let kp1 = &b.data[idx1];
let kp2 = &b.data[idx2];
if kp1.mask < 0.6 || kp2.mask < 0.6 {
continue;
}
imageproc::drawing::draw_line_segment_mut(
&mut img,
(kp1.x * w_ratio, kp1.y * h_ratio),
(kp2.x * w_ratio, kp2.y * h_ratio),
image::Rgb([255, 255, 0]),
);
}
}
Ok(DynamicImage::ImageRgb8(img))
}
#[derive(Clone, Copy, ValueEnum, Debug)]
enum Which {
N,
S,
M,
L,
X,
}
#[derive(Clone, Copy, ValueEnum, Debug)]
enum YoloTask {
Detect,
Pose,
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
pub struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
/// Model weights, in safetensors format.
#[arg(long)]
model: Option<String>,
/// Which model variant to use.
#[arg(long, value_enum, default_value_t = Which::S)]
which: Which,
images: Vec<String>,
/// Threshold for the model confidence level.
#[arg(long, default_value_t = 0.25)]
confidence_threshold: f32,
/// Threshold for non-maximum suppression.
#[arg(long, default_value_t = 0.45)]
nms_threshold: f32,
/// The task to be run.
#[arg(long, default_value = "detect")]
task: YoloTask,
/// The size for the legend, 0 means no legend.
#[arg(long, default_value_t = 14)]
legend_size: u32,
}
impl Args {
fn model(&self) -> anyhow::Result<std::path::PathBuf> {
let path = match &self.model {
Some(model) => std::path::PathBuf::from(model),
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("lmz/candle-yolo-v8".to_string());
let size = match self.which {
Which::N => "n",
Which::S => "s",
Which::M => "m",
Which::L => "l",
Which::X => "x",
};
let task = match self.task {
YoloTask::Pose => "-pose",
YoloTask::Detect => "",
};
api.get(&format!("yolov8{size}{task}.safetensors"))?
}
};
Ok(path)
}
}
pub trait Task: Module + Sized {
fn load(vb: VarBuilder, multiples: Multiples) -> Result<Self>;
fn report(
pred: &Tensor,
img: DynamicImage,
w: usize,
h: usize,
confidence_threshold: f32,
nms_threshold: f32,
legend_size: u32,
) -> Result<DynamicImage>;
}
impl Task for YoloV8 {
fn load(vb: VarBuilder, multiples: Multiples) -> Result<Self> {
YoloV8::load(vb, multiples, /* num_classes=*/ 80)
}
fn report(
pred: &Tensor,
img: DynamicImage,
w: usize,
h: usize,
confidence_threshold: f32,
nms_threshold: f32,
legend_size: u32,
) -> Result<DynamicImage> {
report_detect(
pred,
img,
w,
h,
confidence_threshold,
nms_threshold,
legend_size,
)
}
}
impl Task for YoloV8Pose {
fn load(vb: VarBuilder, multiples: Multiples) -> Result<Self> {
YoloV8Pose::load(vb, multiples, /* num_classes=*/ 1, (17, 3))
}
fn report(
pred: &Tensor,
img: DynamicImage,
w: usize,
h: usize,
confidence_threshold: f32,
nms_threshold: f32,
_legend_size: u32,
) -> Result<DynamicImage> {
report_pose(pred, img, w, h, confidence_threshold, nms_threshold)
}
}
pub fn run<T: Task>(args: Args) -> anyhow::Result<()> {
let device = candle_examples::device(args.cpu)?;
// Create the model and load the weights from the file.
let multiples = match args.which {
Which::N => Multiples::n(),
Which::S => Multiples::s(),
Which::M => Multiples::m(),
Which::L => Multiples::l(),
Which::X => Multiples::x(),
};
let model = args.model()?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? };
let model = T::load(vb, multiples)?;
println!("model loaded");
for image_name in args.images.iter() {
println!("processing {image_name}");
let mut image_name = std::path::PathBuf::from(image_name);
let original_image = image::io::Reader::open(&image_name)?
.decode()
.map_err(candle::Error::wrap)?;
let (width, height) = {
let w = original_image.width() as usize;
let h = original_image.height() as usize;
if w < h {
let w = w * 640 / h;
// Sizes have to be divisible by 32.
(w / 32 * 32, 640)
} else {
let h = h * 640 / w;
(640, h / 32 * 32)
}
};
let image_t = {
let img = original_image.resize_exact(
width as u32,
height as u32,
image::imageops::FilterType::CatmullRom,
);
let data = img.to_rgb8().into_raw();
Tensor::from_vec(
data,
(img.height() as usize, img.width() as usize, 3),
&device,
)?
.permute((2, 0, 1))?
};
let image_t = (image_t.unsqueeze(0)?.to_dtype(DType::F32)? * (1. / 255.))?;
let predictions = model.forward(&image_t)?.squeeze(0)?;
println!("generated predictions {predictions:?}");
let image_t = T::report(
&predictions,
original_image,
width,
height,
args.confidence_threshold,
args.nms_threshold,
args.legend_size,
)?;
image_name.set_extension("pp.jpg");
println!("writing {image_name:?}");
image_t.save(image_name)?
}
Ok(())
}
pub fn main() -> anyhow::Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
match args.task {
YoloTask::Detect => run::<YoloV8>(args)?,
YoloTask::Pose => run::<YoloV8Pose>(args)?,
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v8/README.md | # candle-yolo-v8: Object Detection and Pose Estimation
This is a port of [Ultralytics
YOLOv8](https://github.com/ultralytics/ultralytics). The implementation is based
on the [tinygrad
version](https://github.com/tinygrad/tinygrad/blob/master/examples/yolov8.py)
and on the model architecture described in this
[issue](https://github.com/ultralytics/ultralytics/issues/189). The supported
tasks are object detection and pose estimation.
You can try this model online on the [Candle YOLOv8
Space](https://huggingface.co/spaces/lmz/candle-yolo). The model then fully runs
in your browser using WebAssembly - if you use a custom image it will never
leave your phone/computer!
## Running some example
### Object Detection
```bash
cargo run --example yolo-v8 --release -- candle-examples/examples/yolo-v8/assets/bike.jpg
```
This prints details about the detected objects and generates a `bike.pp.jpg` file.
Image source:
[wikimedia](https://commons.wikimedia.org/wiki/File:Leading_group,_Giro_d%27Italia_2021,_Stage_15.jpg).
### Pose Estimation
```bash
cargo run --example yolo-v8 --release -- \
candle-examples/examples/yolo-v8/assets/bike.jpg --task pose
```
### Command-line flags
- `--which`: select the model variant to be used, `n`, `s` , `m`, `l`, or `x` by
increasing size and quality.
- `--task`: `detect` for object detection and `pose` for pose estimation.
- `--legend-size`: the size of the characters to print.
- `--model`: use a local model file rather than downloading it from the hub.
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/yolo-v8/model.rs | use candle::{DType, IndexOp, Result, Tensor, D};
use candle_nn::{batch_norm, conv2d, conv2d_no_bias, Conv2d, Conv2dConfig, Module, VarBuilder};
#[derive(Clone, Copy, PartialEq, Debug)]
pub struct Multiples {
depth: f64,
width: f64,
ratio: f64,
}
impl Multiples {
pub fn n() -> Self {
Self {
depth: 0.33,
width: 0.25,
ratio: 2.0,
}
}
pub fn s() -> Self {
Self {
depth: 0.33,
width: 0.50,
ratio: 2.0,
}
}
pub fn m() -> Self {
Self {
depth: 0.67,
width: 0.75,
ratio: 1.5,
}
}
pub fn l() -> Self {
Self {
depth: 1.00,
width: 1.00,
ratio: 1.0,
}
}
pub fn x() -> Self {
Self {
depth: 1.00,
width: 1.25,
ratio: 1.0,
}
}
fn filters(&self) -> (usize, usize, usize) {
let f1 = (256. * self.width) as usize;
let f2 = (512. * self.width) as usize;
let f3 = (512. * self.width * self.ratio) as usize;
(f1, f2, f3)
}
}
#[derive(Debug)]
struct Upsample {
scale_factor: usize,
}
impl Upsample {
fn new(scale_factor: usize) -> Result<Self> {
Ok(Upsample { scale_factor })
}
}
impl Module for Upsample {
fn forward(&self, xs: &Tensor) -> candle::Result<Tensor> {
let (_b_size, _channels, h, w) = xs.dims4()?;
xs.upsample_nearest2d(self.scale_factor * h, self.scale_factor * w)
}
}
#[derive(Debug)]
struct ConvBlock {
conv: Conv2d,
span: tracing::Span,
}
impl ConvBlock {
fn load(
vb: VarBuilder,
c1: usize,
c2: usize,
k: usize,
stride: usize,
padding: Option<usize>,
) -> Result<Self> {
let padding = padding.unwrap_or(k / 2);
let cfg = Conv2dConfig {
padding,
stride,
groups: 1,
dilation: 1,
};
let bn = batch_norm(c2, 1e-3, vb.pp("bn"))?;
let conv = conv2d_no_bias(c1, c2, k, cfg, vb.pp("conv"))?.absorb_bn(&bn)?;
Ok(Self {
conv,
span: tracing::span!(tracing::Level::TRACE, "conv-block"),
})
}
}
impl Module for ConvBlock {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let xs = self.conv.forward(xs)?;
candle_nn::ops::silu(&xs)
}
}
#[derive(Debug)]
struct Bottleneck {
cv1: ConvBlock,
cv2: ConvBlock,
residual: bool,
span: tracing::Span,
}
impl Bottleneck {
fn load(vb: VarBuilder, c1: usize, c2: usize, shortcut: bool) -> Result<Self> {
let channel_factor = 1.;
let c_ = (c2 as f64 * channel_factor) as usize;
let cv1 = ConvBlock::load(vb.pp("cv1"), c1, c_, 3, 1, None)?;
let cv2 = ConvBlock::load(vb.pp("cv2"), c_, c2, 3, 1, None)?;
let residual = c1 == c2 && shortcut;
Ok(Self {
cv1,
cv2,
residual,
span: tracing::span!(tracing::Level::TRACE, "bottleneck"),
})
}
}
impl Module for Bottleneck {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let ys = self.cv2.forward(&self.cv1.forward(xs)?)?;
if self.residual {
xs + ys
} else {
Ok(ys)
}
}
}
#[derive(Debug)]
struct C2f {
cv1: ConvBlock,
cv2: ConvBlock,
bottleneck: Vec<Bottleneck>,
span: tracing::Span,
}
impl C2f {
fn load(vb: VarBuilder, c1: usize, c2: usize, n: usize, shortcut: bool) -> Result<Self> {
let c = (c2 as f64 * 0.5) as usize;
let cv1 = ConvBlock::load(vb.pp("cv1"), c1, 2 * c, 1, 1, None)?;
let cv2 = ConvBlock::load(vb.pp("cv2"), (2 + n) * c, c2, 1, 1, None)?;
let mut bottleneck = Vec::with_capacity(n);
for idx in 0..n {
let b = Bottleneck::load(vb.pp(&format!("bottleneck.{idx}")), c, c, shortcut)?;
bottleneck.push(b)
}
Ok(Self {
cv1,
cv2,
bottleneck,
span: tracing::span!(tracing::Level::TRACE, "c2f"),
})
}
}
impl Module for C2f {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let ys = self.cv1.forward(xs)?;
let mut ys = ys.chunk(2, 1)?;
for m in self.bottleneck.iter() {
ys.push(m.forward(ys.last().unwrap())?)
}
let zs = Tensor::cat(ys.as_slice(), 1)?;
self.cv2.forward(&zs)
}
}
#[derive(Debug)]
struct Sppf {
cv1: ConvBlock,
cv2: ConvBlock,
k: usize,
span: tracing::Span,
}
impl Sppf {
fn load(vb: VarBuilder, c1: usize, c2: usize, k: usize) -> Result<Self> {
let c_ = c1 / 2;
let cv1 = ConvBlock::load(vb.pp("cv1"), c1, c_, 1, 1, None)?;
let cv2 = ConvBlock::load(vb.pp("cv2"), c_ * 4, c2, 1, 1, None)?;
Ok(Self {
cv1,
cv2,
k,
span: tracing::span!(tracing::Level::TRACE, "sppf"),
})
}
}
impl Module for Sppf {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let (_, _, _, _) = xs.dims4()?;
let xs = self.cv1.forward(xs)?;
let xs2 = xs
.pad_with_zeros(2, self.k / 2, self.k / 2)?
.pad_with_zeros(3, self.k / 2, self.k / 2)?
.max_pool2d_with_stride(self.k, 1)?;
let xs3 = xs2
.pad_with_zeros(2, self.k / 2, self.k / 2)?
.pad_with_zeros(3, self.k / 2, self.k / 2)?
.max_pool2d_with_stride(self.k, 1)?;
let xs4 = xs3
.pad_with_zeros(2, self.k / 2, self.k / 2)?
.pad_with_zeros(3, self.k / 2, self.k / 2)?
.max_pool2d_with_stride(self.k, 1)?;
self.cv2.forward(&Tensor::cat(&[&xs, &xs2, &xs3, &xs4], 1)?)
}
}
#[derive(Debug)]
struct Dfl {
conv: Conv2d,
num_classes: usize,
span: tracing::Span,
}
impl Dfl {
fn load(vb: VarBuilder, num_classes: usize) -> Result<Self> {
let conv = conv2d_no_bias(num_classes, 1, 1, Default::default(), vb.pp("conv"))?;
Ok(Self {
conv,
num_classes,
span: tracing::span!(tracing::Level::TRACE, "dfl"),
})
}
}
impl Module for Dfl {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let (b_sz, _channels, anchors) = xs.dims3()?;
let xs = xs
.reshape((b_sz, 4, self.num_classes, anchors))?
.transpose(2, 1)?;
let xs = candle_nn::ops::softmax(&xs, 1)?;
self.conv.forward(&xs)?.reshape((b_sz, 4, anchors))
}
}
#[derive(Debug)]
struct DarkNet {
b1_0: ConvBlock,
b1_1: ConvBlock,
b2_0: C2f,
b2_1: ConvBlock,
b2_2: C2f,
b3_0: ConvBlock,
b3_1: C2f,
b4_0: ConvBlock,
b4_1: C2f,
b5: Sppf,
span: tracing::Span,
}
impl DarkNet {
fn load(vb: VarBuilder, m: Multiples) -> Result<Self> {
let (w, r, d) = (m.width, m.ratio, m.depth);
let b1_0 = ConvBlock::load(vb.pp("b1.0"), 3, (64. * w) as usize, 3, 2, Some(1))?;
let b1_1 = ConvBlock::load(
vb.pp("b1.1"),
(64. * w) as usize,
(128. * w) as usize,
3,
2,
Some(1),
)?;
let b2_0 = C2f::load(
vb.pp("b2.0"),
(128. * w) as usize,
(128. * w) as usize,
(3. * d).round() as usize,
true,
)?;
let b2_1 = ConvBlock::load(
vb.pp("b2.1"),
(128. * w) as usize,
(256. * w) as usize,
3,
2,
Some(1),
)?;
let b2_2 = C2f::load(
vb.pp("b2.2"),
(256. * w) as usize,
(256. * w) as usize,
(6. * d).round() as usize,
true,
)?;
let b3_0 = ConvBlock::load(
vb.pp("b3.0"),
(256. * w) as usize,
(512. * w) as usize,
3,
2,
Some(1),
)?;
let b3_1 = C2f::load(
vb.pp("b3.1"),
(512. * w) as usize,
(512. * w) as usize,
(6. * d).round() as usize,
true,
)?;
let b4_0 = ConvBlock::load(
vb.pp("b4.0"),
(512. * w) as usize,
(512. * w * r) as usize,
3,
2,
Some(1),
)?;
let b4_1 = C2f::load(
vb.pp("b4.1"),
(512. * w * r) as usize,
(512. * w * r) as usize,
(3. * d).round() as usize,
true,
)?;
let b5 = Sppf::load(
vb.pp("b5.0"),
(512. * w * r) as usize,
(512. * w * r) as usize,
5,
)?;
Ok(Self {
b1_0,
b1_1,
b2_0,
b2_1,
b2_2,
b3_0,
b3_1,
b4_0,
b4_1,
b5,
span: tracing::span!(tracing::Level::TRACE, "darknet"),
})
}
fn forward(&self, xs: &Tensor) -> Result<(Tensor, Tensor, Tensor)> {
let _enter = self.span.enter();
let x1 = self.b1_1.forward(&self.b1_0.forward(xs)?)?;
let x2 = self
.b2_2
.forward(&self.b2_1.forward(&self.b2_0.forward(&x1)?)?)?;
let x3 = self.b3_1.forward(&self.b3_0.forward(&x2)?)?;
let x4 = self.b4_1.forward(&self.b4_0.forward(&x3)?)?;
let x5 = self.b5.forward(&x4)?;
Ok((x2, x3, x5))
}
}
#[derive(Debug)]
struct YoloV8Neck {
up: Upsample,
n1: C2f,
n2: C2f,
n3: ConvBlock,
n4: C2f,
n5: ConvBlock,
n6: C2f,
span: tracing::Span,
}
impl YoloV8Neck {
fn load(vb: VarBuilder, m: Multiples) -> Result<Self> {
let up = Upsample::new(2)?;
let (w, r, d) = (m.width, m.ratio, m.depth);
let n = (3. * d).round() as usize;
let n1 = C2f::load(
vb.pp("n1"),
(512. * w * (1. + r)) as usize,
(512. * w) as usize,
n,
false,
)?;
let n2 = C2f::load(
vb.pp("n2"),
(768. * w) as usize,
(256. * w) as usize,
n,
false,
)?;
let n3 = ConvBlock::load(
vb.pp("n3"),
(256. * w) as usize,
(256. * w) as usize,
3,
2,
Some(1),
)?;
let n4 = C2f::load(
vb.pp("n4"),
(768. * w) as usize,
(512. * w) as usize,
n,
false,
)?;
let n5 = ConvBlock::load(
vb.pp("n5"),
(512. * w) as usize,
(512. * w) as usize,
3,
2,
Some(1),
)?;
let n6 = C2f::load(
vb.pp("n6"),
(512. * w * (1. + r)) as usize,
(512. * w * r) as usize,
n,
false,
)?;
Ok(Self {
up,
n1,
n2,
n3,
n4,
n5,
n6,
span: tracing::span!(tracing::Level::TRACE, "neck"),
})
}
fn forward(&self, p3: &Tensor, p4: &Tensor, p5: &Tensor) -> Result<(Tensor, Tensor, Tensor)> {
let _enter = self.span.enter();
let x = self
.n1
.forward(&Tensor::cat(&[&self.up.forward(p5)?, p4], 1)?)?;
let head_1 = self
.n2
.forward(&Tensor::cat(&[&self.up.forward(&x)?, p3], 1)?)?;
let head_2 = self
.n4
.forward(&Tensor::cat(&[&self.n3.forward(&head_1)?, &x], 1)?)?;
let head_3 = self
.n6
.forward(&Tensor::cat(&[&self.n5.forward(&head_2)?, p5], 1)?)?;
Ok((head_1, head_2, head_3))
}
}
#[derive(Debug)]
struct DetectionHead {
dfl: Dfl,
cv2: [(ConvBlock, ConvBlock, Conv2d); 3],
cv3: [(ConvBlock, ConvBlock, Conv2d); 3],
ch: usize,
no: usize,
span: tracing::Span,
}
#[derive(Debug)]
struct PoseHead {
detect: DetectionHead,
cv4: [(ConvBlock, ConvBlock, Conv2d); 3],
kpt: (usize, usize),
span: tracing::Span,
}
fn make_anchors(
xs0: &Tensor,
xs1: &Tensor,
xs2: &Tensor,
(s0, s1, s2): (usize, usize, usize),
grid_cell_offset: f64,
) -> Result<(Tensor, Tensor)> {
let dev = xs0.device();
let mut anchor_points = vec![];
let mut stride_tensor = vec![];
for (xs, stride) in [(xs0, s0), (xs1, s1), (xs2, s2)] {
// xs is only used to extract the h and w dimensions.
let (_, _, h, w) = xs.dims4()?;
let sx = (Tensor::arange(0, w as u32, dev)?.to_dtype(DType::F32)? + grid_cell_offset)?;
let sy = (Tensor::arange(0, h as u32, dev)?.to_dtype(DType::F32)? + grid_cell_offset)?;
let sx = sx
.reshape((1, sx.elem_count()))?
.repeat((h, 1))?
.flatten_all()?;
let sy = sy
.reshape((sy.elem_count(), 1))?
.repeat((1, w))?
.flatten_all()?;
anchor_points.push(Tensor::stack(&[&sx, &sy], D::Minus1)?);
stride_tensor.push((Tensor::ones(h * w, DType::F32, dev)? * stride as f64)?);
}
let anchor_points = Tensor::cat(anchor_points.as_slice(), 0)?;
let stride_tensor = Tensor::cat(stride_tensor.as_slice(), 0)?.unsqueeze(1)?;
Ok((anchor_points, stride_tensor))
}
fn dist2bbox(distance: &Tensor, anchor_points: &Tensor) -> Result<Tensor> {
let chunks = distance.chunk(2, 1)?;
let lt = &chunks[0];
let rb = &chunks[1];
let x1y1 = anchor_points.sub(lt)?;
let x2y2 = anchor_points.add(rb)?;
let c_xy = ((&x1y1 + &x2y2)? * 0.5)?;
let wh = (&x2y2 - &x1y1)?;
Tensor::cat(&[c_xy, wh], 1)
}
struct DetectionHeadOut {
pred: Tensor,
anchors: Tensor,
strides: Tensor,
}
impl DetectionHead {
fn load(vb: VarBuilder, nc: usize, filters: (usize, usize, usize)) -> Result<Self> {
let ch = 16;
let dfl = Dfl::load(vb.pp("dfl"), ch)?;
let c1 = usize::max(filters.0, nc);
let c2 = usize::max(filters.0 / 4, ch * 4);
let cv3 = [
Self::load_cv3(vb.pp("cv3.0"), c1, nc, filters.0)?,
Self::load_cv3(vb.pp("cv3.1"), c1, nc, filters.1)?,
Self::load_cv3(vb.pp("cv3.2"), c1, nc, filters.2)?,
];
let cv2 = [
Self::load_cv2(vb.pp("cv2.0"), c2, ch, filters.0)?,
Self::load_cv2(vb.pp("cv2.1"), c2, ch, filters.1)?,
Self::load_cv2(vb.pp("cv2.2"), c2, ch, filters.2)?,
];
let no = nc + ch * 4;
Ok(Self {
dfl,
cv2,
cv3,
ch,
no,
span: tracing::span!(tracing::Level::TRACE, "detection-head"),
})
}
fn load_cv3(
vb: VarBuilder,
c1: usize,
nc: usize,
filter: usize,
) -> Result<(ConvBlock, ConvBlock, Conv2d)> {
let block0 = ConvBlock::load(vb.pp("0"), filter, c1, 3, 1, None)?;
let block1 = ConvBlock::load(vb.pp("1"), c1, c1, 3, 1, None)?;
let conv = conv2d(c1, nc, 1, Default::default(), vb.pp("2"))?;
Ok((block0, block1, conv))
}
fn load_cv2(
vb: VarBuilder,
c2: usize,
ch: usize,
filter: usize,
) -> Result<(ConvBlock, ConvBlock, Conv2d)> {
let block0 = ConvBlock::load(vb.pp("0"), filter, c2, 3, 1, None)?;
let block1 = ConvBlock::load(vb.pp("1"), c2, c2, 3, 1, None)?;
let conv = conv2d(c2, 4 * ch, 1, Default::default(), vb.pp("2"))?;
Ok((block0, block1, conv))
}
fn forward(&self, xs0: &Tensor, xs1: &Tensor, xs2: &Tensor) -> Result<DetectionHeadOut> {
let _enter = self.span.enter();
let forward_cv = |xs, i: usize| {
let xs_2 = self.cv2[i].0.forward(xs)?;
let xs_2 = self.cv2[i].1.forward(&xs_2)?;
let xs_2 = self.cv2[i].2.forward(&xs_2)?;
let xs_3 = self.cv3[i].0.forward(xs)?;
let xs_3 = self.cv3[i].1.forward(&xs_3)?;
let xs_3 = self.cv3[i].2.forward(&xs_3)?;
Tensor::cat(&[&xs_2, &xs_3], 1)
};
let xs0 = forward_cv(xs0, 0)?;
let xs1 = forward_cv(xs1, 1)?;
let xs2 = forward_cv(xs2, 2)?;
let (anchors, strides) = make_anchors(&xs0, &xs1, &xs2, (8, 16, 32), 0.5)?;
let anchors = anchors.transpose(0, 1)?.unsqueeze(0)?;
let strides = strides.transpose(0, 1)?;
let reshape = |xs: &Tensor| {
let d = xs.dim(0)?;
let el = xs.elem_count();
xs.reshape((d, self.no, el / (d * self.no)))
};
let ys0 = reshape(&xs0)?;
let ys1 = reshape(&xs1)?;
let ys2 = reshape(&xs2)?;
let x_cat = Tensor::cat(&[ys0, ys1, ys2], 2)?;
let box_ = x_cat.i((.., ..self.ch * 4))?;
let cls = x_cat.i((.., self.ch * 4..))?;
let dbox = dist2bbox(&self.dfl.forward(&box_)?, &anchors)?;
let dbox = dbox.broadcast_mul(&strides)?;
let pred = Tensor::cat(&[dbox, candle_nn::ops::sigmoid(&cls)?], 1)?;
Ok(DetectionHeadOut {
pred,
anchors,
strides,
})
}
}
impl PoseHead {
// kpt: keypoints, (17, 3)
// nc: num-classes, 80
fn load(
vb: VarBuilder,
nc: usize,
kpt: (usize, usize),
filters: (usize, usize, usize),
) -> Result<Self> {
let detect = DetectionHead::load(vb.clone(), nc, filters)?;
let nk = kpt.0 * kpt.1;
let c4 = usize::max(filters.0 / 4, nk);
let cv4 = [
Self::load_cv4(vb.pp("cv4.0"), c4, nk, filters.0)?,
Self::load_cv4(vb.pp("cv4.1"), c4, nk, filters.1)?,
Self::load_cv4(vb.pp("cv4.2"), c4, nk, filters.2)?,
];
Ok(Self {
detect,
cv4,
kpt,
span: tracing::span!(tracing::Level::TRACE, "pose-head"),
})
}
fn load_cv4(
vb: VarBuilder,
c1: usize,
nc: usize,
filter: usize,
) -> Result<(ConvBlock, ConvBlock, Conv2d)> {
let block0 = ConvBlock::load(vb.pp("0"), filter, c1, 3, 1, None)?;
let block1 = ConvBlock::load(vb.pp("1"), c1, c1, 3, 1, None)?;
let conv = conv2d(c1, nc, 1, Default::default(), vb.pp("2"))?;
Ok((block0, block1, conv))
}
fn forward(&self, xs0: &Tensor, xs1: &Tensor, xs2: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let d = self.detect.forward(xs0, xs1, xs2)?;
let forward_cv = |xs: &Tensor, i: usize| {
let (b_sz, _, h, w) = xs.dims4()?;
let xs = self.cv4[i].0.forward(xs)?;
let xs = self.cv4[i].1.forward(&xs)?;
let xs = self.cv4[i].2.forward(&xs)?;
xs.reshape((b_sz, self.kpt.0 * self.kpt.1, h * w))
};
let xs0 = forward_cv(xs0, 0)?;
let xs1 = forward_cv(xs1, 1)?;
let xs2 = forward_cv(xs2, 2)?;
let xs = Tensor::cat(&[xs0, xs1, xs2], D::Minus1)?;
let (b_sz, _nk, hw) = xs.dims3()?;
let xs = xs.reshape((b_sz, self.kpt.0, self.kpt.1, hw))?;
let ys01 = ((xs.i((.., .., 0..2))? * 2.)?.broadcast_add(&d.anchors)? - 0.5)?
.broadcast_mul(&d.strides)?;
let ys2 = candle_nn::ops::sigmoid(&xs.i((.., .., 2..3))?)?;
let ys = Tensor::cat(&[ys01, ys2], 2)?.flatten(1, 2)?;
Tensor::cat(&[d.pred, ys], 1)
}
}
#[derive(Debug)]
pub struct YoloV8 {
net: DarkNet,
fpn: YoloV8Neck,
head: DetectionHead,
span: tracing::Span,
}
impl YoloV8 {
pub fn load(vb: VarBuilder, m: Multiples, num_classes: usize) -> Result<Self> {
let net = DarkNet::load(vb.pp("net"), m)?;
let fpn = YoloV8Neck::load(vb.pp("fpn"), m)?;
let head = DetectionHead::load(vb.pp("head"), num_classes, m.filters())?;
Ok(Self {
net,
fpn,
head,
span: tracing::span!(tracing::Level::TRACE, "yolo-v8"),
})
}
}
impl Module for YoloV8 {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let (xs1, xs2, xs3) = self.net.forward(xs)?;
let (xs1, xs2, xs3) = self.fpn.forward(&xs1, &xs2, &xs3)?;
Ok(self.head.forward(&xs1, &xs2, &xs3)?.pred)
}
}
#[derive(Debug)]
pub struct YoloV8Pose {
net: DarkNet,
fpn: YoloV8Neck,
head: PoseHead,
span: tracing::Span,
}
impl YoloV8Pose {
pub fn load(
vb: VarBuilder,
m: Multiples,
num_classes: usize,
kpt: (usize, usize),
) -> Result<Self> {
let net = DarkNet::load(vb.pp("net"), m)?;
let fpn = YoloV8Neck::load(vb.pp("fpn"), m)?;
let head = PoseHead::load(vb.pp("head"), num_classes, kpt, m.filters())?;
Ok(Self {
net,
fpn,
head,
span: tracing::span!(tracing::Level::TRACE, "yolo-v8-pose"),
})
}
}
impl Module for YoloV8Pose {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
let (xs1, xs2, xs3) = self.net.forward(xs)?;
let (xs1, xs2, xs3) = self.fpn.forward(&xs1, &xs2, &xs3)?;
self.head.forward(&xs1, &xs2, &xs3)
}
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/efficientnet/main.rs | //! EfficientNet implementation.
//!
//! https://arxiv.org/abs/1905.11946
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::efficientnet::{EfficientNet, MBConvConfig};
use clap::{Parser, ValueEnum};
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
B0,
B1,
B2,
B3,
B4,
B5,
B6,
B7,
}
#[derive(Parser)]
struct Args {
#[arg(long)]
model: Option<String>,
#[arg(long)]
image: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Variant of the model to use.
#[arg(value_enum, long, default_value_t = Which::B2)]
which: Which,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let image = candle_examples::imagenet::load_image224(args.image)?;
println!("loaded image {image:?}");
let model_file = match args.model {
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("lmz/candle-efficientnet".into());
let filename = match args.which {
Which::B0 => "efficientnet-b0.safetensors",
Which::B1 => "efficientnet-b1.safetensors",
Which::B2 => "efficientnet-b2.safetensors",
Which::B3 => "efficientnet-b3.safetensors",
Which::B4 => "efficientnet-b4.safetensors",
Which::B5 => "efficientnet-b5.safetensors",
Which::B6 => "efficientnet-b6.safetensors",
Which::B7 => "efficientnet-b7.safetensors",
};
api.get(filename)?
}
Some(model) => model.into(),
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let cfg = match args.which {
Which::B0 => MBConvConfig::b0(),
Which::B1 => MBConvConfig::b1(),
Which::B2 => MBConvConfig::b2(),
Which::B3 => MBConvConfig::b3(),
Which::B4 => MBConvConfig::b4(),
Which::B5 => MBConvConfig::b5(),
Which::B6 => MBConvConfig::b6(),
Which::B7 => MBConvConfig::b7(),
};
let model = EfficientNet::new(vb, cfg, candle_examples::imagenet::CLASS_COUNT as usize)?;
println!("model built");
let logits = model.forward(&image.unsqueeze(0)?)?;
let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
.i(0)?
.to_vec1::<f32>()?;
let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
for &(category_idx, pr) in prs.iter().take(5) {
println!(
"{:24}: {:.2}%",
candle_examples::imagenet::CLASSES[category_idx],
100. * pr
);
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/t5/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use std::io::Write;
use std::path::PathBuf;
use candle_transformers::models::t5;
use anyhow::{Error as E, Result};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use clap::Parser;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
const DTYPE: DType = DType::F32;
#[derive(Parser, Debug, Clone)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
/// The model repository to use on the HuggingFace hub.
#[arg(long)]
model_id: Option<String>,
#[arg(long)]
revision: Option<String>,
/// Enable decoding.
#[arg(long)]
decode: bool,
// Enable/disable decoding.
#[arg(long, default_value = "false")]
disable_cache: bool,
/// Use this prompt, otherwise compute sentence similarities.
#[arg(long)]
prompt: Option<String>,
/// If set along with --decode, will use this prompt to initialize the decoder.
#[arg(long)]
decoder_prompt: Option<String>,
/// L2 normalization for embeddings.
#[arg(long, default_value = "true")]
normalize_embeddings: bool,
/// The temperature used to generate samples.
#[arg(long, default_value_t = 0.8)]
temperature: f64,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
struct T5ModelBuilder {
device: Device,
config: t5::Config,
weights_filename: Vec<PathBuf>,
}
impl T5ModelBuilder {
pub fn load(args: &Args) -> Result<(Self, Tokenizer)> {
let device = candle_examples::device(args.cpu)?;
let default_model = "t5-small".to_string();
let default_revision = "refs/pr/15".to_string();
let (model_id, revision) = match (args.model_id.to_owned(), args.revision.to_owned()) {
(Some(model_id), Some(revision)) => (model_id, revision),
(Some(model_id), None) => (model_id, "main".to_string()),
(None, Some(revision)) => (default_model, revision),
(None, None) => (default_model, default_revision),
};
let repo = Repo::with_revision(model_id.clone(), RepoType::Model, revision);
let api = Api::new()?;
let api = api.repo(repo);
let config_filename = api.get("config.json")?;
let tokenizer_filename = api.get("tokenizer.json")?;
let weights_filename = if model_id == "google/flan-t5-xxl" || model_id == "google/flan-ul2"
{
candle_examples::hub_load_safetensors(&api, "model.safetensors.index.json")?
} else {
vec![api.get("model.safetensors")?]
};
let config = std::fs::read_to_string(config_filename)?;
let mut config: t5::Config = serde_json::from_str(&config)?;
config.use_cache = !args.disable_cache;
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
Ok((
Self {
device,
config,
weights_filename,
},
tokenizer,
))
}
pub fn build_encoder(&self) -> Result<t5::T5EncoderModel> {
let vb = unsafe {
VarBuilder::from_mmaped_safetensors(&self.weights_filename, DTYPE, &self.device)?
};
Ok(t5::T5EncoderModel::load(vb, &self.config)?)
}
pub fn build_conditional_generation(&self) -> Result<t5::T5ForConditionalGeneration> {
let vb = unsafe {
VarBuilder::from_mmaped_safetensors(&self.weights_filename, DTYPE, &self.device)?
};
Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
}
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
let (builder, mut tokenizer) = T5ModelBuilder::load(&args)?;
let device = &builder.device;
let tokenizer = tokenizer
.with_padding(None)
.with_truncation(None)
.map_err(E::msg)?;
match args.prompt {
Some(prompt) => {
let tokens = tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
if !args.decode {
let mut model = builder.build_encoder()?;
let start = std::time::Instant::now();
let ys = model.forward(&input_token_ids)?;
println!("{ys}");
println!("Took {:?}", start.elapsed());
} else {
let mut model = builder.build_conditional_generation()?;
let mut output_token_ids = [builder
.config
.decoder_start_token_id
.unwrap_or(builder.config.pad_token_id)
as u32]
.to_vec();
if let Some(decoder_prompt) = &args.decoder_prompt {
print!("{decoder_prompt}");
output_token_ids.extend(
tokenizer
.encode(decoder_prompt.to_string(), false)
.map_err(E::msg)?
.get_ids()
.to_vec(),
);
}
let temperature = if args.temperature <= 0. {
None
} else {
Some(args.temperature)
};
let mut logits_processor = LogitsProcessor::new(299792458, temperature, args.top_p);
let encoder_output = model.encode(&input_token_ids)?;
let start = std::time::Instant::now();
for index in 0.. {
if output_token_ids.len() > 512 {
break;
}
let decoder_token_ids = if index == 0 || !builder.config.use_cache {
Tensor::new(output_token_ids.as_slice(), device)?.unsqueeze(0)?
} else {
let last_token = *output_token_ids.last().unwrap();
Tensor::new(&[last_token], device)?.unsqueeze(0)?
};
let logits = model
.decode(&decoder_token_ids, &encoder_output)?
.squeeze(0)?;
let logits = if args.repeat_penalty == 1. {
logits
} else {
let start_at = output_token_ids.len().saturating_sub(args.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
args.repeat_penalty,
&output_token_ids[start_at..],
)?
};
let next_token_id = logits_processor.sample(&logits)?;
if next_token_id as usize == builder.config.eos_token_id {
break;
}
output_token_ids.push(next_token_id);
if let Some(text) = tokenizer.id_to_token(next_token_id) {
let text = text.replace('▁', " ").replace("<0x0A>", "\n");
print!("{text}");
std::io::stdout().flush()?;
}
}
let dt = start.elapsed();
println!(
"\n{} tokens generated ({:.2} token/s)\n",
output_token_ids.len(),
output_token_ids.len() as f64 / dt.as_secs_f64(),
);
}
}
None => {
let mut model = builder.build_encoder()?;
let sentences = [
"The cat sits outside",
"A man is playing guitar",
"I love pasta",
"The new movie is awesome",
"The cat plays in the garden",
"A woman watches TV",
"The new movie is so great",
"Do you like pizza?",
];
let n_sentences = sentences.len();
let mut all_embeddings = Vec::with_capacity(n_sentences);
for sentence in sentences {
let tokens = tokenizer
.encode(sentence, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let token_ids = Tensor::new(&tokens[..], model.device())?.unsqueeze(0)?;
let embeddings = model.forward(&token_ids)?;
println!("generated embeddings {:?}", embeddings.shape());
// Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
let embeddings = if args.normalize_embeddings {
normalize_l2(&embeddings)?
} else {
embeddings
};
println!("pooled embeddings {:?}", embeddings.shape());
all_embeddings.push(embeddings)
}
let mut similarities = vec![];
for (i, e_i) in all_embeddings.iter().enumerate() {
for (j, e_j) in all_embeddings
.iter()
.enumerate()
.take(n_sentences)
.skip(i + 1)
{
let sum_ij = (e_i * e_j)?.sum_all()?.to_scalar::<f32>()?;
let sum_i2 = (e_i * e_i)?.sum_all()?.to_scalar::<f32>()?;
let sum_j2 = (e_j * e_j)?.sum_all()?.to_scalar::<f32>()?;
let cosine_similarity = sum_ij / (sum_i2 * sum_j2).sqrt();
similarities.push((cosine_similarity, i, j))
}
}
similarities.sort_by(|u, v| v.0.total_cmp(&u.0));
for &(score, i, j) in similarities[..5].iter() {
println!("score: {score:.2} '{}' '{}'", sentences[i], sentences[j])
}
}
}
Ok(())
}
pub fn normalize_l2(v: &Tensor) -> Result<Tensor> {
Ok(v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)?)
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/t5/README.md | # candle-t5
## Encoder-decoder example:
```bash
$ cargo run --example t5 --release -- --model-id "t5-small" --prompt "translate to German: A beautiful candle." --decode
...
Eine schöne Kerze.
9 tokens generated (2.42 token/s)
```
Variants such as [flan-t5](https://huggingface.co/google/flan-t5-small), [flan-ul2](https://huggingface.co/google/flan-ul2) (with `--revision "refs/pr/25"`), and [Co-EdIT](https://huggingface.co/grammarly/coedit-large) are also supported.
## Translation with [MADLAD-400](https://arxiv.org/abs/2309.04662)
MADLAD-400 is a series of multilingual machine translation T5 models trained on 250 billion tokens covering over 450 languages using publicly available data. These models are competitive with significantly larger models.
```bash
cargo run --example t5 --release -- \
--model-id "jbochi/madlad400-3b-mt" \
--prompt "<2de> How are you, my friend?" \
--decode --temperature 0
...
Wie geht es dir, mein Freund?
```
## Sentence embedding example
```bash
$ cargo run --example t5 --release -- --model-id "t5-small" --prompt "A beautiful candle."
...
[[[ 0.0515, -0.0541, -0.0761, ..., -0.0392, 0.1511, -0.0265],
[-0.0974, 0.0998, -0.1659, ..., -0.2450, 0.1738, -0.0164],
[ 0.0624, -0.1024, 0.0430, ..., -0.1388, 0.0564, -0.2962],
[-0.0389, -0.1173, 0.0026, ..., 0.1064, -0.1065, 0.0990],
[ 0.1300, 0.0027, -0.0326, ..., 0.0026, -0.0317, 0.0851]]]
Tensor[[1, 5, 512], f32]
Took 303.766583ms
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mistral/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::mistral::{Config, Model as Mistral};
use candle_transformers::models::quantized_mistral::Model as QMistral;
use candle::{DType, Device, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
enum Model {
Mistral(Mistral),
Quantized(QMistral),
}
struct TextGeneration {
model: Model,
device: Device,
tokenizer: TokenOutputStream,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
}
impl TextGeneration {
#[allow(clippy::too_many_arguments)]
fn new(
model: Model,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
top_p: Option<f64>,
repeat_penalty: f32,
repeat_last_n: usize,
device: &Device,
) -> Self {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer: TokenOutputStream::new(tokenizer),
logits_processor,
repeat_penalty,
repeat_last_n,
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
self.tokenizer.clear();
let mut tokens = self
.tokenizer
.tokenizer()
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
for &t in tokens.iter() {
if let Some(t) = self.tokenizer.next_token(t)? {
print!("{t}")
}
}
std::io::stdout().flush()?;
let mut generated_tokens = 0usize;
let eos_token = match self.tokenizer.get_token("</s>") {
Some(token) => token,
None => anyhow::bail!("cannot find the </s> token"),
};
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let context_size = if index > 0 { 1 } else { tokens.len() };
let start_pos = tokens.len().saturating_sub(context_size);
let ctxt = &tokens[start_pos..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = match &mut self.model {
Model::Mistral(m) => m.forward(&input, start_pos)?,
Model::Quantized(m) => m.forward(&input, start_pos)?,
};
let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(self.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
generated_tokens += 1;
if next_token == eos_token {
break;
}
if let Some(t) = self.tokenizer.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
}
let dt = start_gen.elapsed();
if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
std::io::stdout().flush()?;
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
generated_tokens as f64 / dt.as_secs_f64(),
);
Ok(())
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
use_flash_attn: bool,
#[arg(long)]
prompt: String,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, short = 'n', default_value_t = 100)]
sample_len: usize,
#[arg(long)]
model_id: Option<String>,
#[arg(long, default_value = "main")]
revision: String,
#[arg(long)]
tokenizer_file: Option<String>,
#[arg(long)]
weight_files: Option<String>,
#[arg(long)]
quantized: bool,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
println!(
"avx: {}, neon: {}, simd128: {}, f16c: {}",
candle::utils::with_avx(),
candle::utils::with_neon(),
candle::utils::with_simd128(),
candle::utils::with_f16c()
);
println!(
"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
args.temperature.unwrap_or(0.),
args.repeat_penalty,
args.repeat_last_n
);
let start = std::time::Instant::now();
let api = Api::new()?;
let model_id = match args.model_id {
Some(model_id) => model_id,
None => {
if args.quantized {
"lmz/candle-mistral".to_string()
} else {
"mistralai/Mistral-7B-v0.1".to_string()
}
}
};
let repo = api.repo(Repo::with_revision(
model_id,
RepoType::Model,
args.revision,
));
let tokenizer_filename = match args.tokenizer_file {
Some(file) => std::path::PathBuf::from(file),
None => repo.get("tokenizer.json")?,
};
let filenames = match args.weight_files {
Some(files) => files
.split(',')
.map(std::path::PathBuf::from)
.collect::<Vec<_>>(),
None => {
if args.quantized {
vec![repo.get("model-q4k.gguf")?]
} else {
candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?
}
}
};
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let config = Config::config_7b_v0_1(args.use_flash_attn);
let device = candle_examples::device(args.cpu)?;
let (model, device) = if args.quantized {
let filename = &filenames[0];
let vb =
candle_transformers::quantized_var_builder::VarBuilder::from_gguf(filename, &device)?;
let model = QMistral::new(&config, vb)?;
(Model::Quantized(model), device)
} else {
let dtype = if device.is_cuda() {
DType::BF16
} else {
DType::F32
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
let model = Mistral::new(&config, vb)?;
(Model::Mistral(model), device)
};
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(
model,
tokenizer,
args.seed,
args.temperature,
args.top_p,
args.repeat_penalty,
args.repeat_last_n,
&device,
);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mistral/README.md | # candle-mistral: 7b LLM with Apache 2.0 licensed weights
Mistral-7B-v0.1 is a pretrained generative LLM with 7 billion parameters. It outperforms all the publicly available 13b models
as of 2023-09-28. Weights (and the original Python model code) are released under the permissive Apache 2.0 license.
- [Blog post](https://mistral.ai/news/announcing-mistral-7b/) from Mistral announcing the model release.
- [Model card](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the
HuggingFace Hub.
This example supports the initial model as well as a quantized variant.
## Running the example
```bash
$ cargo run --example mistral --release --features cuda -- --prompt 'Write helloworld code in Rust' --sample-len 150
Generated text:
Write helloworld code in Rust
=============================
This is a simple example of how to write "Hello, world!" program in Rust.
## Compile and run
``bash
$ cargo build --release
Compiling hello-world v0.1.0 (/home/user/rust/hello-world)
Finished release [optimized] target(s) in 0.26s
$ ./target/release/hello-world
Hello, world!
``
## Source code
``rust
fn main() {
println!("Hello, world!");
}
``
## License
This example is released under the terms
```
## Running the quantized version of the model
```bash
$ cargo run --example mistral --features accelerate --release -- \
$ --prompt "Here is a sample quick sort implementation in rust " --quantized -n 400
avx: false, neon: true, simd128: false, f16c: false
temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
retrieved the files in 562.292µs
loaded the model in 1.100323667s
Here is a sample quick sort implementation in rust
``rust
fn quick_sort(arr: &mut [i32]) {
if arr.len() <= 1 {
return;
}
let pivot = arr[0];
let mut left = vec![];
let mut right = vec![];
for i in 1..arr.len() {
if arr[i] < pivot {
left.push(arr[i]);
} else {
right.push(arr[i]);
}
}
quick_sort(&mut left);
quick_sort(&mut right);
let mut i = 0;
for _ in &left {
arr[i] = left.pop().unwrap();
i += 1;
}
for _ in &right {
arr[i] = right.pop().unwrap();
i += 1;
}
}
``
226 tokens generated (10.91 token/s)
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/bert/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::bert::{BertModel, Config, HiddenAct, DTYPE};
use anyhow::{Error as E, Result};
use candle::Tensor;
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::{PaddingParams, Tokenizer};
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
/// The model to use, check out available models: https://huggingface.co/models?library=sentence-transformers&sort=trending
#[arg(long)]
model_id: Option<String>,
#[arg(long)]
revision: Option<String>,
/// When set, compute embeddings for this prompt.
#[arg(long)]
prompt: Option<String>,
/// Use the pytorch weights rather than the safetensors ones
#[arg(long)]
use_pth: bool,
/// The number of times to run the prompt.
#[arg(long, default_value = "1")]
n: usize,
/// L2 normalization for embeddings.
#[arg(long, default_value = "true")]
normalize_embeddings: bool,
/// Use tanh based approximation for Gelu instead of erf implementation.
#[arg(long, default_value = "false")]
approximate_gelu: bool,
}
impl Args {
fn build_model_and_tokenizer(&self) -> Result<(BertModel, Tokenizer)> {
let device = candle_examples::device(self.cpu)?;
let default_model = "sentence-transformers/all-MiniLM-L6-v2".to_string();
let default_revision = "refs/pr/21".to_string();
let (model_id, revision) = match (self.model_id.to_owned(), self.revision.to_owned()) {
(Some(model_id), Some(revision)) => (model_id, revision),
(Some(model_id), None) => (model_id, "main".to_string()),
(None, Some(revision)) => (default_model, revision),
(None, None) => (default_model, default_revision),
};
let repo = Repo::with_revision(model_id, RepoType::Model, revision);
let (config_filename, tokenizer_filename, weights_filename) = {
let api = Api::new()?;
let api = api.repo(repo);
let config = api.get("config.json")?;
let tokenizer = api.get("tokenizer.json")?;
let weights = if self.use_pth {
api.get("pytorch_model.bin")?
} else {
api.get("model.safetensors")?
};
(config, tokenizer, weights)
};
let config = std::fs::read_to_string(config_filename)?;
let mut config: Config = serde_json::from_str(&config)?;
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let vb = if self.use_pth {
VarBuilder::from_pth(&weights_filename, DTYPE, &device)?
} else {
unsafe { VarBuilder::from_mmaped_safetensors(&[weights_filename], DTYPE, &device)? }
};
if self.approximate_gelu {
config.hidden_act = HiddenAct::GeluApproximate;
}
let model = BertModel::load(vb, &config)?;
Ok((model, tokenizer))
}
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
println!("tracing...");
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
let start = std::time::Instant::now();
let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
let device = &model.device;
if let Some(prompt) = args.prompt {
let tokenizer = tokenizer
.with_padding(None)
.with_truncation(None)
.map_err(E::msg)?;
let tokens = tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
let token_type_ids = token_ids.zeros_like()?;
println!("Loaded and encoded {:?}", start.elapsed());
for idx in 0..args.n {
let start = std::time::Instant::now();
let ys = model.forward(&token_ids, &token_type_ids)?;
if idx == 0 {
println!("{ys}");
}
println!("Took {:?}", start.elapsed());
}
} else {
let sentences = [
"The cat sits outside",
"A man is playing guitar",
"I love pasta",
"The new movie is awesome",
"The cat plays in the garden",
"A woman watches TV",
"The new movie is so great",
"Do you like pizza?",
];
let n_sentences = sentences.len();
if let Some(pp) = tokenizer.get_padding_mut() {
pp.strategy = tokenizers::PaddingStrategy::BatchLongest
} else {
let pp = PaddingParams {
strategy: tokenizers::PaddingStrategy::BatchLongest,
..Default::default()
};
tokenizer.with_padding(Some(pp));
}
let tokens = tokenizer
.encode_batch(sentences.to_vec(), true)
.map_err(E::msg)?;
let token_ids = tokens
.iter()
.map(|tokens| {
let tokens = tokens.get_ids().to_vec();
Ok(Tensor::new(tokens.as_slice(), device)?)
})
.collect::<Result<Vec<_>>>()?;
let token_ids = Tensor::stack(&token_ids, 0)?;
let token_type_ids = token_ids.zeros_like()?;
println!("running inference on batch {:?}", token_ids.shape());
let embeddings = model.forward(&token_ids, &token_type_ids)?;
println!("generated embeddings {:?}", embeddings.shape());
// Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
let embeddings = if args.normalize_embeddings {
normalize_l2(&embeddings)?
} else {
embeddings
};
println!("pooled embeddings {:?}", embeddings.shape());
let mut similarities = vec![];
for i in 0..n_sentences {
let e_i = embeddings.get(i)?;
for j in (i + 1)..n_sentences {
let e_j = embeddings.get(j)?;
let sum_ij = (&e_i * &e_j)?.sum_all()?.to_scalar::<f32>()?;
let sum_i2 = (&e_i * &e_i)?.sum_all()?.to_scalar::<f32>()?;
let sum_j2 = (&e_j * &e_j)?.sum_all()?.to_scalar::<f32>()?;
let cosine_similarity = sum_ij / (sum_i2 * sum_j2).sqrt();
similarities.push((cosine_similarity, i, j))
}
}
similarities.sort_by(|u, v| v.0.total_cmp(&u.0));
for &(score, i, j) in similarities[..5].iter() {
println!("score: {score:.2} '{}' '{}'", sentences[i], sentences[j])
}
}
Ok(())
}
pub fn normalize_l2(v: &Tensor) -> Result<Tensor> {
Ok(v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)?)
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/bert/README.md | # candle-bert
Bert is a general large language model. In this example it can be used for two
different tasks:
- Compute sentence embeddings for a prompt.
- Compute similarities between a set of sentences.
## Sentence embeddings
Bert is used to compute the sentence embeddings for a prompt. The model weights
are downloaded from the hub on the first run.
```bash
cargo run --example bert --release -- --prompt "Here is a test sentence"
> [[[ 0.0798, -0.0665, -0.0247, ..., -0.1082, -0.1000, -0.2751],
> [ 0.4218, 0.2690, 0.2740, ..., 0.3889, 1.3503, 0.9908],
> [ 0.0466, 0.3041, -0.1143, ..., 0.4427, 0.6926, -0.1515],
> ...
> [ 0.3396, 0.4320, -0.4408, ..., 0.9212, 0.2331, -0.6777],
> [ 0.2789, 0.7539, 0.4306, ..., -0.0095, 0.3375, -1.7529],
> [ 0.6737, 0.7882, 0.0548, ..., 0.1836, 0.7299, -0.6617]]]
> Tensor[[1, 7, 384], f32]
```
### Custom models
You can specify different models, such as BGE, with the `--model-id` flag:
```bash
cargo run --example bert --release -- \
--model-id BAAI/bge-large-zh-v1.5 \
--prompt "Here is a test sentence"
Loaded and encoded 435.70775ms
[[[ 3.0944e-1, -7.8455e-5, -1.2768e0, ..., 1.3755e-2, -3.2371e-1, 2.3819e-1],
[-2.8506e-1, 1.9953e-1, -1.3076e0, ..., 6.9819e-2, 1.0833e-2, -1.1512e0],
[ 3.9892e-1, 2.0000e-1, -9.3178e-1, ..., -4.1393e-1, -4.9644e-2, -3.3786e-1],
...
[ 6.0345e-1, 3.5744e-1, -1.2672e0, ..., -6.9165e-1, -3.4973e-3, -8.4214e-1],
[ 3.9218e-1, -3.2735e-1, -1.3123e0, ..., -4.9318e-1, -5.1334e-1, -3.6391e-1],
[ 3.0978e-1, 2.5662e-4, -1.2773e0, ..., 1.3357e-2, -3.2390e-1, 2.3858e-1]]]
Tensor[[1, 9, 1024], f32]
Took 176.744667ms
```
### Gelu approximation
You can get a speedup by using an approximation of the gelu activation, with a
small loss of precision, by passing the `--approximate-gelu` flag:
```bash
$ cargo run --example bert --release -- \
--model-id BAAI/bge-large-zh-v1.5 \
--prompt "Here is a test sentence" \
--approximate-gelu
Loaded and encoded 244.388042ms
[[[ 3.1048e-1, -6.0339e-4, -1.2758e0, ..., 1.3718e-2, -3.2362e-1, 2.3775e-1],
[-2.8354e-1, 1.9984e-1, -1.3077e0, ..., 6.9390e-2, 9.9681e-3, -1.1531e0],
[ 3.9947e-1, 1.9917e-1, -9.3178e-1, ..., -4.1301e-1, -5.0719e-2, -3.3955e-1],
...
[ 6.0499e-1, 3.5664e-1, -1.2642e0, ..., -6.9134e-1, -3.4581e-3, -8.4471e-1],
[ 3.9311e-1, -3.2812e-1, -1.3105e0, ..., -4.9291e-1, -5.1270e-1, -3.6543e-1],
[ 3.1082e-1, -2.6737e-4, -1.2762e0, ..., 1.3319e-2, -3.2381e-1, 2.3815e-1]]]
Tensor[[1, 9, 1024], f32]
Took 116.840791ms
```
## Similarities
In this example, Bert is used to compute the sentence embeddings for a set of
sentences (hardcoded in the examples). Then cosine similarities are computed for
each sentence pair and they are reported by decreasing values, hence the first
reported pair contains the two sentences that have the highest similarity score.
The sentence embeddings are computed using average pooling through all the
sentence tokens, including some potential padding.
```bash
cargo run --example bert --release
> score: 0.85 'The new movie is awesome' 'The new movie is so great'
> score: 0.61 'The cat sits outside' 'The cat plays in the garden'
> score: 0.52 'I love pasta' 'Do you like pizza?'
> score: 0.23 'The new movie is awesome' 'Do you like pizza?'
> score: 0.22 'I love pasta' 'The new movie is awesome'
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/stable-diffusion/main.rs | #[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use candle_transformers::models::stable_diffusion;
use anyhow::{Error as E, Result};
use candle::{DType, Device, IndexOp, Module, Tensor, D};
use clap::Parser;
use tokenizers::Tokenizer;
#[derive(Parser)]
#[command(author, version, about, long_about = None)]
struct Args {
/// The prompt to be used for image generation.
#[arg(
long,
default_value = "A very realistic photo of a rusty robot walking on a sandy beach"
)]
prompt: String,
#[arg(long, default_value = "")]
uncond_prompt: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
/// The height in pixels of the generated image.
#[arg(long)]
height: Option<usize>,
/// The width in pixels of the generated image.
#[arg(long)]
width: Option<usize>,
/// The UNet weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
unet_weights: Option<String>,
/// The CLIP weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
clip_weights: Option<String>,
/// The VAE weight file, in .safetensors format.
#[arg(long, value_name = "FILE")]
vae_weights: Option<String>,
#[arg(long, value_name = "FILE")]
/// The file specifying the tokenizer to used for tokenization.
tokenizer: Option<String>,
/// The size of the sliced attention or 0 for automatic slicing (disabled by default)
#[arg(long)]
sliced_attention_size: Option<usize>,
/// The number of steps to run the diffusion for.
#[arg(long)]
n_steps: Option<usize>,
/// The number of samples to generate.
#[arg(long, default_value_t = 1)]
num_samples: i64,
/// The name of the final image to generate.
#[arg(long, value_name = "FILE", default_value = "sd_final.png")]
final_image: String,
#[arg(long, value_enum, default_value = "v2-1")]
sd_version: StableDiffusionVersion,
/// Generate intermediary images at each step.
#[arg(long, action)]
intermediary_images: bool,
#[arg(long)]
use_flash_attn: bool,
#[arg(long)]
use_f16: bool,
#[arg(long)]
guidance_scale: Option<f64>,
#[arg(long, value_name = "FILE")]
img2img: Option<String>,
/// The strength, indicates how much to transform the initial image. The
/// value must be between 0 and 1, a value of 1 discards the initial image
/// information.
#[arg(long, default_value_t = 0.8)]
img2img_strength: f64,
}
#[derive(Debug, Clone, Copy, clap::ValueEnum, PartialEq, Eq)]
enum StableDiffusionVersion {
V1_5,
V2_1,
Xl,
Turbo,
}
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
enum ModelFile {
Tokenizer,
Tokenizer2,
Clip,
Clip2,
Unet,
Vae,
}
impl StableDiffusionVersion {
fn repo(&self) -> &'static str {
match self {
Self::Xl => "stabilityai/stable-diffusion-xl-base-1.0",
Self::V2_1 => "stabilityai/stable-diffusion-2-1",
Self::V1_5 => "runwayml/stable-diffusion-v1-5",
Self::Turbo => "stabilityai/sdxl-turbo",
}
}
fn unet_file(&self, use_f16: bool) -> &'static str {
match self {
Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
if use_f16 {
"unet/diffusion_pytorch_model.fp16.safetensors"
} else {
"unet/diffusion_pytorch_model.safetensors"
}
}
}
}
fn vae_file(&self, use_f16: bool) -> &'static str {
match self {
Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
if use_f16 {
"vae/diffusion_pytorch_model.fp16.safetensors"
} else {
"vae/diffusion_pytorch_model.safetensors"
}
}
}
}
fn clip_file(&self, use_f16: bool) -> &'static str {
match self {
Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
if use_f16 {
"text_encoder/model.fp16.safetensors"
} else {
"text_encoder/model.safetensors"
}
}
}
}
fn clip2_file(&self, use_f16: bool) -> &'static str {
match self {
Self::V1_5 | Self::V2_1 | Self::Xl | Self::Turbo => {
if use_f16 {
"text_encoder_2/model.fp16.safetensors"
} else {
"text_encoder_2/model.safetensors"
}
}
}
}
}
impl ModelFile {
fn get(
&self,
filename: Option<String>,
version: StableDiffusionVersion,
use_f16: bool,
) -> Result<std::path::PathBuf> {
use hf_hub::api::sync::Api;
match filename {
Some(filename) => Ok(std::path::PathBuf::from(filename)),
None => {
let (repo, path) = match self {
Self::Tokenizer => {
let tokenizer_repo = match version {
StableDiffusionVersion::V1_5 | StableDiffusionVersion::V2_1 => {
"openai/clip-vit-base-patch32"
}
StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo => {
// This seems similar to the patch32 version except some very small
// difference in the split regex.
"openai/clip-vit-large-patch14"
}
};
(tokenizer_repo, "tokenizer.json")
}
Self::Tokenizer2 => {
("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", "tokenizer.json")
}
Self::Clip => (version.repo(), version.clip_file(use_f16)),
Self::Clip2 => (version.repo(), version.clip2_file(use_f16)),
Self::Unet => (version.repo(), version.unet_file(use_f16)),
Self::Vae => {
// Override for SDXL when using f16 weights.
// See https://github.com/huggingface/candle/issues/1060
if matches!(
version,
StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo,
) && use_f16
{
(
"madebyollin/sdxl-vae-fp16-fix",
"diffusion_pytorch_model.safetensors",
)
} else {
(version.repo(), version.vae_file(use_f16))
}
}
};
let filename = Api::new()?.model(repo.to_string()).get(path)?;
Ok(filename)
}
}
}
}
fn output_filename(
basename: &str,
sample_idx: i64,
num_samples: i64,
timestep_idx: Option<usize>,
) -> String {
let filename = if num_samples > 1 {
match basename.rsplit_once('.') {
None => format!("{basename}.{sample_idx}.png"),
Some((filename_no_extension, extension)) => {
format!("{filename_no_extension}.{sample_idx}.{extension}")
}
}
} else {
basename.to_string()
};
match timestep_idx {
None => filename,
Some(timestep_idx) => match filename.rsplit_once('.') {
None => format!("{filename}-{timestep_idx}.png"),
Some((filename_no_extension, extension)) => {
format!("{filename_no_extension}-{timestep_idx}.{extension}")
}
},
}
}
#[allow(clippy::too_many_arguments)]
fn text_embeddings(
prompt: &str,
uncond_prompt: &str,
tokenizer: Option<String>,
clip_weights: Option<String>,
sd_version: StableDiffusionVersion,
sd_config: &stable_diffusion::StableDiffusionConfig,
use_f16: bool,
device: &Device,
dtype: DType,
use_guide_scale: bool,
first: bool,
) -> Result<Tensor> {
let tokenizer_file = if first {
ModelFile::Tokenizer
} else {
ModelFile::Tokenizer2
};
let tokenizer = tokenizer_file.get(tokenizer, sd_version, use_f16)?;
let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
let pad_id = match &sd_config.clip.pad_with {
Some(padding) => *tokenizer.get_vocab(true).get(padding.as_str()).unwrap(),
None => *tokenizer.get_vocab(true).get("<|endoftext|>").unwrap(),
};
println!("Running with prompt \"{prompt}\".");
let mut tokens = tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
while tokens.len() < sd_config.clip.max_position_embeddings {
tokens.push(pad_id)
}
let tokens = Tensor::new(tokens.as_slice(), device)?.unsqueeze(0)?;
println!("Building the Clip transformer.");
let clip_weights_file = if first {
ModelFile::Clip
} else {
ModelFile::Clip2
};
let clip_weights = clip_weights_file.get(clip_weights, sd_version, false)?;
let clip_config = if first {
&sd_config.clip
} else {
sd_config.clip2.as_ref().unwrap()
};
let text_model =
stable_diffusion::build_clip_transformer(clip_config, clip_weights, device, DType::F32)?;
let text_embeddings = text_model.forward(&tokens)?;
let text_embeddings = if use_guide_scale {
let mut uncond_tokens = tokenizer
.encode(uncond_prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
while uncond_tokens.len() < sd_config.clip.max_position_embeddings {
uncond_tokens.push(pad_id)
}
let uncond_tokens = Tensor::new(uncond_tokens.as_slice(), device)?.unsqueeze(0)?;
let uncond_embeddings = text_model.forward(&uncond_tokens)?;
Tensor::cat(&[uncond_embeddings, text_embeddings], 0)?.to_dtype(dtype)?
} else {
text_embeddings.to_dtype(dtype)?
};
Ok(text_embeddings)
}
fn image_preprocess<T: AsRef<std::path::Path>>(path: T) -> anyhow::Result<Tensor> {
let img = image::io::Reader::open(path)?.decode()?;
let (height, width) = (img.height() as usize, img.width() as usize);
let height = height - height % 32;
let width = width - width % 32;
let img = img.resize_to_fill(
width as u32,
height as u32,
image::imageops::FilterType::CatmullRom,
);
let img = img.to_rgb8();
let img = img.into_raw();
let img = Tensor::from_vec(img, (height, width, 3), &Device::Cpu)?
.permute((2, 0, 1))?
.to_dtype(DType::F32)?
.affine(2. / 255., -1.)?
.unsqueeze(0)?;
Ok(img)
}
fn run(args: Args) -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let Args {
prompt,
uncond_prompt,
cpu,
height,
width,
n_steps,
tokenizer,
final_image,
sliced_attention_size,
num_samples,
sd_version,
clip_weights,
vae_weights,
unet_weights,
tracing,
use_f16,
guidance_scale,
use_flash_attn,
img2img,
img2img_strength,
..
} = args;
if !(0. ..=1.).contains(&img2img_strength) {
anyhow::bail!("img2img-strength should be between 0 and 1, got {img2img_strength}")
}
let _guard = if tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
let guidance_scale = match guidance_scale {
Some(guidance_scale) => guidance_scale,
None => match sd_version {
StableDiffusionVersion::V1_5
| StableDiffusionVersion::V2_1
| StableDiffusionVersion::Xl => 7.5,
StableDiffusionVersion::Turbo => 0.,
},
};
let n_steps = match n_steps {
Some(n_steps) => n_steps,
None => match sd_version {
StableDiffusionVersion::V1_5
| StableDiffusionVersion::V2_1
| StableDiffusionVersion::Xl => 30,
StableDiffusionVersion::Turbo => 1,
},
};
let dtype = if use_f16 { DType::F16 } else { DType::F32 };
let sd_config = match sd_version {
StableDiffusionVersion::V1_5 => {
stable_diffusion::StableDiffusionConfig::v1_5(sliced_attention_size, height, width)
}
StableDiffusionVersion::V2_1 => {
stable_diffusion::StableDiffusionConfig::v2_1(sliced_attention_size, height, width)
}
StableDiffusionVersion::Xl => {
stable_diffusion::StableDiffusionConfig::sdxl(sliced_attention_size, height, width)
}
StableDiffusionVersion::Turbo => stable_diffusion::StableDiffusionConfig::sdxl_turbo(
sliced_attention_size,
height,
width,
),
};
let scheduler = sd_config.build_scheduler(n_steps)?;
let device = candle_examples::device(cpu)?;
let use_guide_scale = guidance_scale > 1.0;
let which = match sd_version {
StableDiffusionVersion::Xl | StableDiffusionVersion::Turbo => vec![true, false],
_ => vec![true],
};
let text_embeddings = which
.iter()
.map(|first| {
text_embeddings(
&prompt,
&uncond_prompt,
tokenizer.clone(),
clip_weights.clone(),
sd_version,
&sd_config,
use_f16,
&device,
dtype,
use_guide_scale,
*first,
)
})
.collect::<Result<Vec<_>>>()?;
let text_embeddings = Tensor::cat(&text_embeddings, D::Minus1)?;
println!("{text_embeddings:?}");
println!("Building the autoencoder.");
let vae_weights = ModelFile::Vae.get(vae_weights, sd_version, use_f16)?;
let vae = sd_config.build_vae(vae_weights, &device, dtype)?;
let init_latent_dist = match &img2img {
None => None,
Some(image) => {
let image = image_preprocess(image)?.to_device(&device)?;
Some(vae.encode(&image)?)
}
};
println!("Building the unet.");
let unet_weights = ModelFile::Unet.get(unet_weights, sd_version, use_f16)?;
let unet = sd_config.build_unet(unet_weights, &device, 4, use_flash_attn, dtype)?;
let t_start = if img2img.is_some() {
n_steps - (n_steps as f64 * img2img_strength) as usize
} else {
0
};
let bsize = 1;
let vae_scale = match sd_version {
StableDiffusionVersion::V1_5
| StableDiffusionVersion::V2_1
| StableDiffusionVersion::Xl => 0.18215,
StableDiffusionVersion::Turbo => 0.13025,
};
for idx in 0..num_samples {
let timesteps = scheduler.timesteps();
let latents = match &init_latent_dist {
Some(init_latent_dist) => {
let latents = (init_latent_dist.sample()? * vae_scale)?.to_device(&device)?;
if t_start < timesteps.len() {
let noise = latents.randn_like(0f64, 1f64)?;
scheduler.add_noise(&latents, noise, timesteps[t_start])?
} else {
latents
}
}
None => {
let latents = Tensor::randn(
0f32,
1f32,
(bsize, 4, sd_config.height / 8, sd_config.width / 8),
&device,
)?;
// scale the initial noise by the standard deviation required by the scheduler
(latents * scheduler.init_noise_sigma())?
}
};
let mut latents = latents.to_dtype(dtype)?;
println!("starting sampling");
for (timestep_index, ×tep) in timesteps.iter().enumerate() {
if timestep_index < t_start {
continue;
}
let start_time = std::time::Instant::now();
let latent_model_input = if use_guide_scale {
Tensor::cat(&[&latents, &latents], 0)?
} else {
latents.clone()
};
let latent_model_input = scheduler.scale_model_input(latent_model_input, timestep)?;
let noise_pred =
unet.forward(&latent_model_input, timestep as f64, &text_embeddings)?;
let noise_pred = if use_guide_scale {
let noise_pred = noise_pred.chunk(2, 0)?;
let (noise_pred_uncond, noise_pred_text) = (&noise_pred[0], &noise_pred[1]);
(noise_pred_uncond + ((noise_pred_text - noise_pred_uncond)? * guidance_scale)?)?
} else {
noise_pred
};
latents = scheduler.step(&noise_pred, timestep, &latents)?;
let dt = start_time.elapsed().as_secs_f32();
println!("step {}/{n_steps} done, {:.2}s", timestep_index + 1, dt);
if args.intermediary_images {
let image = vae.decode(&(&latents / vae_scale)?)?;
let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
let image = (image * 255.)?.to_dtype(DType::U8)?.i(0)?;
let image_filename =
output_filename(&final_image, idx + 1, num_samples, Some(timestep_index + 1));
candle_examples::save_image(&image, image_filename)?
}
}
println!(
"Generating the final image for sample {}/{}.",
idx + 1,
num_samples
);
let image = vae.decode(&(&latents / vae_scale)?)?;
let image = ((image / 2.)? + 0.5)?.to_device(&Device::Cpu)?;
let image = (image.clamp(0f32, 1.)? * 255.)?.to_dtype(DType::U8)?.i(0)?;
let image_filename = output_filename(&final_image, idx + 1, num_samples, None);
candle_examples::save_image(&image, image_filename)?
}
Ok(())
}
fn main() -> Result<()> {
let args = Args::parse();
run(args)
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/stable-diffusion/README.md | # candle-stable-diffusion: A Diffusers API in Rust/Candle
_A rusty robot holding a fire torch in its hand_, generated by Stable Diffusion
XL using Rust and [candle](https://github.com/huggingface/candle).
The `stable-diffusion` example is a conversion of
[diffusers-rs](https://github.com/LaurentMazare/diffusers-rs) using candle
rather than libtorch. This implementation supports Stable Diffusion v1.5, v2.1,
as well as Stable Diffusion XL 1.0, and Turbo.
## Getting the weights
The weights are automatically downloaded for you from the [HuggingFace
Hub](https://huggingface.co/) on the first run. There are various command line
flags to use local files instead, run with `--help` to learn about them.
## Running some example.
```bash
cargo run --example stable-diffusion --release --features=cuda,cudnn \
-- --prompt "a cosmonaut on a horse (hd, realistic, high-def)"
```
The final image is named `sd_final.png` by default. The Turbo version is much
faster than previous versions, to give it a try add a `--sd-version turbo` flag,
e.g.:
```bash
cargo run --example stable-diffusion --release --features=cuda,cudnn \
-- --prompt "a cosmonaut on a horse (hd, realistic, high-def)" --sd-version turbo
```
The default scheduler for the v1.5, v2.1 and XL 1.0 version is the Denoising
Diffusion Implicit Model scheduler (DDIM). The original paper and some code can
be found in the [associated repo](https://github.com/ermongroup/ddim).
The default scheduler for the XL Turbo version is the Euler Ancestral scheduler.
### Command-line flags
- `--prompt`: the prompt to be used to generate the image.
- `--uncond-prompt`: the optional unconditional prompt.
- `--sd-version`: the Stable Diffusion version to use, can be `v1-5`, `v2-1`,
`xl`, or `turbo`.
- `--cpu`: use the cpu rather than the gpu (much slower).
- `--height`, `--width`: set the height and width for the generated image.
- `--n-steps`: the number of steps to be used in the diffusion process.
- `--num-samples`: the number of samples to generate.
- `--final-image`: the filename for the generated image(s).
### Using flash-attention
Using flash attention makes image generation a lot faster and uses less memory.
The downside is some long compilation time. You can set the
`CANDLE_FLASH_ATTN_BUILD_DIR` environment variable to something like
`/home/user/.candle` to ensures that the compilation artifacts are properly
cached.
Enabling flash-attention requires both a feature flag, `--feature flash-attn`
and using the command line flag `--use-flash-attn`.
Note that flash-attention-v2 is only compatible with Ampere, Ada, or Hopper GPUs
(e.g., A100/H100, RTX 3090/4090).
## Image to Image Pipeline
...
## FAQ
### Memory Issues
This requires a GPU with more than 8GB of memory, as a fallback the CPU version can be used
with the `--cpu` flag but is much slower.
Alternatively, reducing the height and width with the `--height` and `--width`
flag is likely to reduce memory usage significantly.
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/falcon/main.rs | // TODO: Add an offline mode.
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{Error as E, Result};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use clap::Parser;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
use candle_transformers::models::falcon::{Config, Falcon};
struct TextGeneration {
model: Falcon,
device: Device,
tokenizer: Tokenizer,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
}
struct GenerationOptions {
temp: Option<f64>,
top_p: Option<f64>,
repeat_penalty: f32,
repeat_last_n: usize,
}
impl TextGeneration {
fn new(
model: Falcon,
tokenizer: Tokenizer,
generation_options: GenerationOptions,
seed: u64,
device: &Device,
) -> Self {
let logits_processor =
LogitsProcessor::new(seed, generation_options.temp, generation_options.top_p);
let repeat_penalty = generation_options.repeat_penalty;
let repeat_last_n = generation_options.repeat_last_n;
Self {
model,
tokenizer,
logits_processor,
device: device.clone(),
repeat_penalty,
repeat_last_n,
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
println!("starting the inference loop");
let mut tokens = self
.tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let mut new_tokens = vec![];
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let start_gen = std::time::Instant::now();
let context_size = if self.model.config().use_cache && index > 0 {
1
} else {
tokens.len()
};
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = self.model.forward(&input)?;
let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(self.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
new_tokens.push(next_token);
println!("> {:?}", start_gen.elapsed());
println!(
"{} token: {} '{}'",
index + 1,
next_token,
self.tokenizer.decode(&[next_token], true).map_err(E::msg)?
);
}
let dt = start_gen.elapsed();
println!(
"{sample_len} tokens generated ({} token/s)\n----\n{}\n----",
sample_len as f64 / dt.as_secs_f64(),
self.tokenizer.decode(&new_tokens, true).map_err(E::msg)?
);
Ok(())
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
#[arg(long)]
prompt: String,
/// Use f32 computations rather than bf16.
#[arg(long)]
use_f32: bool,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, default_value_t = 100)]
sample_len: usize,
#[arg(long, default_value = "tiiuae/falcon-7b")]
model_id: String,
#[arg(long, default_value = "refs/pr/43")]
revision: String,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.0)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let start = std::time::Instant::now();
let api = Api::new()?;
let repo = api.repo(Repo::with_revision(
args.model_id,
RepoType::Model,
args.revision,
));
let tokenizer_filename = repo.get("tokenizer.json")?;
let filenames = candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?;
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let dtype = if args.use_f32 {
DType::F32
} else {
DType::BF16
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
let config = Config::falcon7b();
config.validate()?;
let model = Falcon::load(vb, config)?;
println!("loaded the model in {:?}", start.elapsed());
let generation_options = GenerationOptions {
temp: args.temperature,
top_p: args.top_p,
repeat_penalty: args.repeat_penalty,
repeat_last_n: args.repeat_last_n,
};
let mut pipeline =
TextGeneration::new(model, tokenizer, generation_options, args.seed, &device);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/falcon/README.md | # candle-falcon
Falcon is a general large language model.
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mnist-training/main.rs | // This should reach 91.5% accuracy.
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use rand::prelude::*;
use candle::{DType, Result, Tensor, D};
use candle_nn::{loss, ops, Conv2d, Linear, Module, ModuleT, Optimizer, VarBuilder, VarMap};
const IMAGE_DIM: usize = 784;
const LABELS: usize = 10;
fn linear_z(in_dim: usize, out_dim: usize, vs: VarBuilder) -> Result<Linear> {
let ws = vs.get_with_hints((out_dim, in_dim), "weight", candle_nn::init::ZERO)?;
let bs = vs.get_with_hints(out_dim, "bias", candle_nn::init::ZERO)?;
Ok(Linear::new(ws, Some(bs)))
}
trait Model: Sized {
fn new(vs: VarBuilder) -> Result<Self>;
fn forward(&self, xs: &Tensor) -> Result<Tensor>;
}
struct LinearModel {
linear: Linear,
}
impl Model for LinearModel {
fn new(vs: VarBuilder) -> Result<Self> {
let linear = linear_z(IMAGE_DIM, LABELS, vs)?;
Ok(Self { linear })
}
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
self.linear.forward(xs)
}
}
struct Mlp {
ln1: Linear,
ln2: Linear,
}
impl Model for Mlp {
fn new(vs: VarBuilder) -> Result<Self> {
let ln1 = candle_nn::linear(IMAGE_DIM, 100, vs.pp("ln1"))?;
let ln2 = candle_nn::linear(100, LABELS, vs.pp("ln2"))?;
Ok(Self { ln1, ln2 })
}
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let xs = self.ln1.forward(xs)?;
let xs = xs.relu()?;
self.ln2.forward(&xs)
}
}
#[derive(Debug)]
struct ConvNet {
conv1: Conv2d,
conv2: Conv2d,
fc1: Linear,
fc2: Linear,
dropout: candle_nn::Dropout,
}
impl ConvNet {
fn new(vs: VarBuilder) -> Result<Self> {
let conv1 = candle_nn::conv2d(1, 32, 5, Default::default(), vs.pp("c1"))?;
let conv2 = candle_nn::conv2d(32, 64, 5, Default::default(), vs.pp("c2"))?;
let fc1 = candle_nn::linear(1024, 1024, vs.pp("fc1"))?;
let fc2 = candle_nn::linear(1024, LABELS, vs.pp("fc2"))?;
let dropout = candle_nn::Dropout::new(0.5);
Ok(Self {
conv1,
conv2,
fc1,
fc2,
dropout,
})
}
fn forward(&self, xs: &Tensor, train: bool) -> Result<Tensor> {
let (b_sz, _img_dim) = xs.dims2()?;
let xs = xs
.reshape((b_sz, 1, 28, 28))?
.apply(&self.conv1)?
.max_pool2d(2)?
.apply(&self.conv2)?
.max_pool2d(2)?
.flatten_from(1)?
.apply(&self.fc1)?
.relu()?;
self.dropout.forward_t(&xs, train)?.apply(&self.fc2)
}
}
struct TrainingArgs {
learning_rate: f64,
load: Option<String>,
save: Option<String>,
epochs: usize,
}
fn training_loop_cnn(
m: candle_datasets::vision::Dataset,
args: &TrainingArgs,
) -> anyhow::Result<()> {
const BSIZE: usize = 64;
let dev = candle::Device::cuda_if_available(0)?;
let train_labels = m.train_labels;
let train_images = m.train_images.to_device(&dev)?;
let train_labels = train_labels.to_dtype(DType::U32)?.to_device(&dev)?;
let mut varmap = VarMap::new();
let vs = VarBuilder::from_varmap(&varmap, DType::F32, &dev);
let model = ConvNet::new(vs.clone())?;
if let Some(load) = &args.load {
println!("loading weights from {load}");
varmap.load(load)?
}
let adamw_params = candle_nn::ParamsAdamW {
lr: args.learning_rate,
..Default::default()
};
let mut opt = candle_nn::AdamW::new(varmap.all_vars(), adamw_params)?;
let test_images = m.test_images.to_device(&dev)?;
let test_labels = m.test_labels.to_dtype(DType::U32)?.to_device(&dev)?;
let n_batches = train_images.dim(0)? / BSIZE;
let mut batch_idxs = (0..n_batches).collect::<Vec<usize>>();
for epoch in 1..args.epochs {
let mut sum_loss = 0f32;
batch_idxs.shuffle(&mut thread_rng());
for batch_idx in batch_idxs.iter() {
let train_images = train_images.narrow(0, batch_idx * BSIZE, BSIZE)?;
let train_labels = train_labels.narrow(0, batch_idx * BSIZE, BSIZE)?;
let logits = model.forward(&train_images, true)?;
let log_sm = ops::log_softmax(&logits, D::Minus1)?;
let loss = loss::nll(&log_sm, &train_labels)?;
opt.backward_step(&loss)?;
sum_loss += loss.to_vec0::<f32>()?;
}
let avg_loss = sum_loss / n_batches as f32;
let test_logits = model.forward(&test_images, false)?;
let sum_ok = test_logits
.argmax(D::Minus1)?
.eq(&test_labels)?
.to_dtype(DType::F32)?
.sum_all()?
.to_scalar::<f32>()?;
let test_accuracy = sum_ok / test_labels.dims1()? as f32;
println!(
"{epoch:4} train loss {:8.5} test acc: {:5.2}%",
avg_loss,
100. * test_accuracy
);
}
if let Some(save) = &args.save {
println!("saving trained weights in {save}");
varmap.save(save)?
}
Ok(())
}
fn training_loop<M: Model>(
m: candle_datasets::vision::Dataset,
args: &TrainingArgs,
) -> anyhow::Result<()> {
let dev = candle::Device::cuda_if_available(0)?;
let train_labels = m.train_labels;
let train_images = m.train_images.to_device(&dev)?;
let train_labels = train_labels.to_dtype(DType::U32)?.to_device(&dev)?;
let mut varmap = VarMap::new();
let vs = VarBuilder::from_varmap(&varmap, DType::F32, &dev);
let model = M::new(vs.clone())?;
if let Some(load) = &args.load {
println!("loading weights from {load}");
varmap.load(load)?
}
let mut sgd = candle_nn::SGD::new(varmap.all_vars(), args.learning_rate)?;
let test_images = m.test_images.to_device(&dev)?;
let test_labels = m.test_labels.to_dtype(DType::U32)?.to_device(&dev)?;
for epoch in 1..args.epochs {
let logits = model.forward(&train_images)?;
let log_sm = ops::log_softmax(&logits, D::Minus1)?;
let loss = loss::nll(&log_sm, &train_labels)?;
sgd.backward_step(&loss)?;
let test_logits = model.forward(&test_images)?;
let sum_ok = test_logits
.argmax(D::Minus1)?
.eq(&test_labels)?
.to_dtype(DType::F32)?
.sum_all()?
.to_scalar::<f32>()?;
let test_accuracy = sum_ok / test_labels.dims1()? as f32;
println!(
"{epoch:4} train loss: {:8.5} test acc: {:5.2}%",
loss.to_scalar::<f32>()?,
100. * test_accuracy
);
}
if let Some(save) = &args.save {
println!("saving trained weights in {save}");
varmap.save(save)?
}
Ok(())
}
#[derive(ValueEnum, Clone)]
enum WhichModel {
Linear,
Mlp,
Cnn,
}
#[derive(Parser)]
struct Args {
#[clap(value_enum, default_value_t = WhichModel::Linear)]
model: WhichModel,
#[arg(long)]
learning_rate: Option<f64>,
#[arg(long, default_value_t = 200)]
epochs: usize,
/// The file where to save the trained weights, in safetensors format.
#[arg(long)]
save: Option<String>,
/// The file where to load the trained weights from, in safetensors format.
#[arg(long)]
load: Option<String>,
/// The directory where to load the dataset from, in ubyte format.
#[arg(long)]
local_mnist: Option<String>,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
// Load the dataset
let m = if let Some(directory) = args.local_mnist {
candle_datasets::vision::mnist::load_dir(directory)?
} else {
candle_datasets::vision::mnist::load()?
};
println!("train-images: {:?}", m.train_images.shape());
println!("train-labels: {:?}", m.train_labels.shape());
println!("test-images: {:?}", m.test_images.shape());
println!("test-labels: {:?}", m.test_labels.shape());
let default_learning_rate = match args.model {
WhichModel::Linear => 1.,
WhichModel::Mlp => 0.05,
WhichModel::Cnn => 0.001,
};
let training_args = TrainingArgs {
epochs: args.epochs,
learning_rate: args.learning_rate.unwrap_or(default_learning_rate),
load: args.load,
save: args.save,
};
match args.model {
WhichModel::Linear => training_loop::<LinearModel>(m, &training_args),
WhichModel::Mlp => training_loop::<Mlp>(m, &training_args),
WhichModel::Cnn => training_loop_cnn(m, &training_args),
}
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/blip/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Error as E;
use clap::Parser;
use candle::{DType, Device, Result, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::models::blip;
use candle_transformers::models::quantized_blip;
use tokenizers::Tokenizer;
enum Model {
M(blip::BlipForConditionalGeneration),
Q(quantized_blip::BlipForConditionalGeneration),
}
impl Model {
fn text_decoder_forward(&mut self, xs: &Tensor, img_xs: &Tensor) -> Result<Tensor> {
match self {
Self::M(m) => m.text_decoder().forward(xs, img_xs),
Self::Q(m) => m.text_decoder().forward(xs, img_xs),
}
}
}
// TODO: Maybe add support for the conditional prompt.
#[derive(Parser)]
struct Args {
#[arg(long)]
model: Option<String>,
#[arg(long)]
tokenizer: Option<String>,
#[arg(long)]
image: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Use the quantized version of the model.
#[arg(long)]
quantized: bool,
}
const SEP_TOKEN_ID: u32 = 102;
/// Loads an image from disk using the image crate, this returns a tensor with shape
/// (3, 384, 384). OpenAI normalization is applied.
pub fn load_image<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
let img = image::io::Reader::open(p)?
.decode()
.map_err(candle::Error::wrap)?
.resize_to_fill(384, 384, image::imageops::FilterType::Triangle);
let img = img.to_rgb8();
let data = img.into_raw();
let data = Tensor::from_vec(data, (384, 384, 3), &Device::Cpu)?.permute((2, 0, 1))?;
let mean =
Tensor::new(&[0.48145466f32, 0.4578275, 0.40821073], &Device::Cpu)?.reshape((3, 1, 1))?;
let std = Tensor::new(&[0.26862954f32, 0.261_302_6, 0.275_777_1], &Device::Cpu)?
.reshape((3, 1, 1))?;
(data.to_dtype(candle::DType::F32)? / 255.)?
.broadcast_sub(&mean)?
.broadcast_div(&std)
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let model_file = match args.model {
None => {
let api = hf_hub::api::sync::Api::new()?;
if args.quantized {
let api = api.model("lmz/candle-blip".to_string());
api.get("blip-image-captioning-large-q4k.gguf")?
} else {
let api = api.repo(hf_hub::Repo::with_revision(
"Salesforce/blip-image-captioning-large".to_string(),
hf_hub::RepoType::Model,
"refs/pr/18".to_string(),
));
api.get("model.safetensors")?
}
}
Some(model) => model.into(),
};
let tokenizer = match args.tokenizer {
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("Salesforce/blip-image-captioning-large".to_string());
api.get("tokenizer.json")?
}
Some(file) => file.into(),
};
let tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
let mut tokenizer = TokenOutputStream::new(tokenizer);
let mut logits_processor =
candle_transformers::generation::LogitsProcessor::new(1337, None, None);
let config = blip::Config::image_captioning_large();
let device = candle_examples::device(args.cpu)?;
let (image_embeds, device, mut model) = if args.quantized {
let device = Device::Cpu;
let image = load_image(args.image)?.to_device(&device)?;
println!("loaded image {image:?}");
let vb = quantized_blip::VarBuilder::from_gguf(model_file, &device)?;
let model = quantized_blip::BlipForConditionalGeneration::new(&config, vb)?;
let image_embeds = image.unsqueeze(0)?.apply(model.vision_model())?;
(image_embeds, device, Model::Q(model))
} else {
let image = load_image(args.image)?.to_device(&device)?;
println!("loaded image {image:?}");
let vb =
unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let model = blip::BlipForConditionalGeneration::new(&config, vb)?;
let image_embeds = image.unsqueeze(0)?.apply(model.vision_model())?;
(image_embeds, device, Model::M(model))
};
let mut token_ids = vec![30522u32];
for index in 0..1000 {
let context_size = if index > 0 { 1 } else { token_ids.len() };
let start_pos = token_ids.len().saturating_sub(context_size);
let input_ids = Tensor::new(&token_ids[start_pos..], &device)?.unsqueeze(0)?;
let logits = model.text_decoder_forward(&input_ids, &image_embeds)?;
let logits = logits.squeeze(0)?;
let logits = logits.get(logits.dim(0)? - 1)?;
let token = logits_processor.sample(&logits)?;
if token == SEP_TOKEN_ID {
break;
}
token_ids.push(token);
if let Some(t) = tokenizer.next_token(token)? {
use std::io::Write;
print!("{t}");
std::io::stdout().flush()?;
}
}
if let Some(rest) = tokenizer.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
println!();
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/blip/README.md | # candle-blip
The
[blip-image-captioning](https://huggingface.co/Salesforce/blip-image-captioning-base)
model can generate captions for an input image.
## Running on an example
```bash
cargo run --example blip --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg
```
```
Running on CPU, to run on GPU, build this example with `--features cuda`
loaded image Tensor[dims 3, 384, 384; f32]
model built
several cyclists are riding down a road with cars behind them%
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/quantized/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use std::io::Write;
use tokenizers::Tokenizer;
use candle::quantized::{ggml_file, gguf_file};
use candle::Tensor;
use candle_transformers::generation::LogitsProcessor;
use candle_examples::token_output_stream::TokenOutputStream;
use candle_transformers::models::quantized_llama as model;
use model::ModelWeights;
const DEFAULT_PROMPT: &str = "My favorite theorem is ";
#[derive(Debug)]
enum Prompt {
Interactive,
Chat,
One(String),
}
#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
enum Which {
#[value(name = "7b")]
L7b,
#[value(name = "13b")]
L13b,
#[value(name = "70b")]
L70b,
#[value(name = "7b-chat")]
L7bChat,
#[value(name = "13b-chat")]
L13bChat,
#[value(name = "70b-chat")]
L70bChat,
#[value(name = "7b-code")]
L7bCode,
#[value(name = "13b-code")]
L13bCode,
#[value(name = "32b-code")]
L34bCode,
#[value(name = "7b-leo")]
Leo7b,
#[value(name = "13b-leo")]
Leo13b,
#[value(name = "7b-mistral")]
Mistral7b,
#[value(name = "7b-mistral-instruct")]
Mistral7bInstruct,
#[value(name = "7b-mistral-instruct-v0.2")]
Mistral7bInstructV02,
#[value(name = "7b-zephyr-a")]
Zephyr7bAlpha,
#[value(name = "7b-zephyr-b")]
Zephyr7bBeta,
#[value(name = "7b-open-chat-3.5")]
OpenChat35,
#[value(name = "7b-starling-a")]
Starling7bAlpha,
#[value(name = "mixtral")]
Mixtral,
#[value(name = "mixtral-instruct")]
MixtralInstruct,
}
impl Which {
fn is_mistral(&self) -> bool {
match self {
Self::L7b
| Self::L13b
| Self::L70b
| Self::L7bChat
| Self::L13bChat
| Self::L70bChat
| Self::L7bCode
| Self::L13bCode
| Self::L34bCode
| Self::Leo7b
| Self::Leo13b => false,
// Zephyr and OpenChat are fine tuned versions of mistral and should be treated in the
// same way. Starling is a fine tuned version of OpenChat.
Self::OpenChat35
| Self::Starling7bAlpha
| Self::Zephyr7bAlpha
| Self::Zephyr7bBeta
| Self::Mixtral
| Self::MixtralInstruct
| Self::Mistral7b
| Self::Mistral7bInstruct
| Self::Mistral7bInstructV02 => true,
}
}
fn is_zephyr(&self) -> bool {
match self {
Self::L7b
| Self::L13b
| Self::L70b
| Self::L7bChat
| Self::L13bChat
| Self::L70bChat
| Self::L7bCode
| Self::L13bCode
| Self::L34bCode
| Self::Leo7b
| Self::Leo13b
| Self::Mixtral
| Self::MixtralInstruct
| Self::Mistral7b
| Self::Mistral7bInstruct
| Self::Mistral7bInstructV02
| Self::OpenChat35
| Self::Starling7bAlpha => false,
Self::Zephyr7bAlpha | Self::Zephyr7bBeta => true,
}
}
fn is_open_chat(&self) -> bool {
match self {
Self::L7b
| Self::L13b
| Self::L70b
| Self::L7bChat
| Self::L13bChat
| Self::L70bChat
| Self::L7bCode
| Self::L13bCode
| Self::L34bCode
| Self::Leo7b
| Self::Leo13b
| Self::Mixtral
| Self::MixtralInstruct
| Self::Mistral7b
| Self::Mistral7bInstruct
| Self::Mistral7bInstructV02
| Self::Zephyr7bAlpha
| Self::Zephyr7bBeta => false,
Self::OpenChat35 | Self::Starling7bAlpha => true,
}
}
fn tokenizer_repo(&self) -> &'static str {
match self {
Which::L7b
| Which::L13b
| Which::L70b
| Which::L7bChat
| Which::L13bChat
| Which::L70bChat
| Which::L7bCode
| Which::L13bCode
| Which::L34bCode => "hf-internal-testing/llama-tokenizer",
Which::Leo7b => "LeoLM/leo-hessianai-7b",
Which::Leo13b => "LeoLM/leo-hessianai-13b",
Which::Mixtral => "mistralai/Mixtral-8x7B-v0.1",
Which::MixtralInstruct => "mistralai/Mixtral-8x7B-Instruct-v0.1",
Which::Mistral7b
| Which::Mistral7bInstruct
| Which::Mistral7bInstructV02
| Which::Zephyr7bAlpha
| Which::Zephyr7bBeta => "mistralai/Mistral-7B-v0.1",
Which::OpenChat35 => "openchat/openchat_3.5",
Which::Starling7bAlpha => "berkeley-nest/Starling-LM-7B-alpha",
}
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// GGML/GGUF file to load, typically a .bin/.gguf file generated by the quantize command from llama.cpp
#[arg(long)]
model: Option<String>,
/// The initial prompt, use 'interactive' for entering multiple prompts in an interactive way
/// and 'chat' for an interactive model where history of previous prompts and generated tokens
/// is preserved.
#[arg(long)]
prompt: Option<String>,
/// The length of the sample to generate (in tokens).
#[arg(short = 'n', long, default_value_t = 1000)]
sample_len: usize,
/// The tokenizer config in json format.
#[arg(long)]
tokenizer: Option<String>,
/// The temperature used to generate samples, use 0 for greedy sampling.
#[arg(long, default_value_t = 0.8)]
temperature: f64,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
/// Display the token for the specified prompt.
#[arg(long)]
verbose_prompt: bool,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
/// The model size to use.
#[arg(long, default_value = "7b")]
which: Which,
/// Group-Query Attention, use 8 for the 70B version of LLaMAv2.
#[arg(long)]
gqa: Option<usize>,
}
impl Args {
fn tokenizer(&self) -> anyhow::Result<Tokenizer> {
let tokenizer_path = match &self.tokenizer {
Some(config) => std::path::PathBuf::from(config),
None => {
let api = hf_hub::api::sync::Api::new()?;
let repo = self.which.tokenizer_repo();
let api = api.model(repo.to_string());
api.get("tokenizer.json")?
}
};
Tokenizer::from_file(tokenizer_path).map_err(anyhow::Error::msg)
}
fn model(&self) -> anyhow::Result<std::path::PathBuf> {
let model_path = match &self.model {
Some(config) => std::path::PathBuf::from(config),
None => {
let (repo, filename) = match self.which {
Which::L7b => ("TheBloke/Llama-2-7B-GGML", "llama-2-7b.ggmlv3.q4_0.bin"),
Which::L13b => ("TheBloke/Llama-2-13B-GGML", "llama-2-13b.ggmlv3.q4_0.bin"),
Which::L70b => ("TheBloke/Llama-2-70B-GGML", "llama-2-70b.ggmlv3.q4_0.bin"),
Which::L7bChat => (
"TheBloke/Llama-2-7B-Chat-GGML",
"llama-2-7b-chat.ggmlv3.q4_0.bin",
),
Which::L13bChat => (
"TheBloke/Llama-2-13B-Chat-GGML",
"llama-2-13b-chat.ggmlv3.q4_0.bin",
),
Which::L70bChat => (
"TheBloke/Llama-2-70B-Chat-GGML",
"llama-2-70b-chat.ggmlv3.q4_0.bin",
),
Which::L7bCode => ("TheBloke/CodeLlama-7B-GGUF", "codellama-7b.Q8_0.gguf"),
Which::L13bCode => ("TheBloke/CodeLlama-13B-GGUF", "codellama-13b.Q8_0.gguf"),
Which::L34bCode => ("TheBloke/CodeLlama-34B-GGUF", "codellama-34b.Q8_0.gguf"),
Which::Leo7b => (
"TheBloke/leo-hessianai-7B-GGUF",
"leo-hessianai-7b.Q4_K_M.gguf",
),
Which::Leo13b => (
"TheBloke/leo-hessianai-13B-GGUF",
"leo-hessianai-13b.Q4_K_M.gguf",
),
Which::Mixtral => (
"TheBloke/Mixtral-8x7B-v0.1-GGUF",
"mixtral-8x7b-v0.1.Q4_K_M.gguf",
),
Which::MixtralInstruct => (
"TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF",
"mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf",
),
Which::Mistral7b => (
"TheBloke/Mistral-7B-v0.1-GGUF",
"mistral-7b-v0.1.Q4_K_S.gguf",
),
Which::Mistral7bInstruct => (
"TheBloke/Mistral-7B-Instruct-v0.1-GGUF",
"mistral-7b-instruct-v0.1.Q4_K_S.gguf",
),
Which::Mistral7bInstructV02 => (
"TheBloke/Mistral-7B-Instruct-v0.2-GGUF",
"mistral-7b-instruct-v0.2.Q4_K_S.gguf",
),
Which::Zephyr7bAlpha => (
"TheBloke/zephyr-7B-alpha-GGUF",
"zephyr-7b-alpha.Q4_K_M.gguf",
),
Which::Zephyr7bBeta => {
("TheBloke/zephyr-7B-beta-GGUF", "zephyr-7b-beta.Q4_K_M.gguf")
}
Which::OpenChat35 => ("TheBloke/openchat_3.5-GGUF", "openchat_3.5.Q4_K_M.gguf"),
Which::Starling7bAlpha => (
"TheBloke/Starling-LM-7B-alpha-GGUF",
"starling-lm-7b-alpha.Q4_K_M.gguf",
),
};
let api = hf_hub::api::sync::Api::new()?;
let api = api.model(repo.to_string());
api.get(filename)?
}
};
Ok(model_path)
}
}
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)
}
}
fn main() -> anyhow::Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let temperature = if args.temperature == 0. {
None
} else {
Some(args.temperature)
};
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
println!(
"avx: {}, neon: {}, simd128: {}, f16c: {}",
candle::utils::with_avx(),
candle::utils::with_neon(),
candle::utils::with_simd128(),
candle::utils::with_f16c()
);
println!(
"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
args.temperature, args.repeat_penalty, args.repeat_last_n
);
let model_path = args.model()?;
let mut file = std::fs::File::open(&model_path)?;
let start = std::time::Instant::now();
let device = candle_examples::device(false)?;
let mut model = match model_path.extension().and_then(|v| v.to_str()) {
Some("gguf") => {
let model = gguf_file::Content::read(&mut file).map_err(|e| e.with_path(model_path))?;
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.block_size();
}
println!(
"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, &device)?
}
Some("ggml" | "bin") | Some(_) | None => {
let model = ggml_file::Content::read(&mut file, &device)
.map_err(|e| e.with_path(model_path))?;
let mut total_size_in_bytes = 0;
for (_, tensor) in model.tensors.iter() {
let elem_count = tensor.shape().elem_count();
total_size_in_bytes +=
elem_count * tensor.dtype().type_size() / tensor.dtype().block_size();
}
println!(
"loaded {:?} tensors ({}) in {:.2}s",
model.tensors.len(),
&format_size(total_size_in_bytes),
start.elapsed().as_secs_f32(),
);
println!("params: {:?}", model.hparams);
let default_gqa = match args.which {
Which::L7b
| Which::L13b
| Which::L7bChat
| Which::L13bChat
| Which::L7bCode
| Which::L13bCode
| Which::L34bCode
| Which::Leo7b
| Which::Leo13b => 1,
Which::Mixtral
| Which::MixtralInstruct
| Which::Mistral7b
| Which::Mistral7bInstruct
| Which::Mistral7bInstructV02
| Which::Zephyr7bAlpha
| Which::Zephyr7bBeta
| Which::L70b
| Which::L70bChat
| Which::OpenChat35
| Which::Starling7bAlpha => 8,
};
ModelWeights::from_ggml(model, args.gqa.unwrap_or(default_gqa))?
}
};
println!("model built");
let tokenizer = args.tokenizer()?;
let mut tos = TokenOutputStream::new(tokenizer);
let prompt = match args.prompt.as_deref() {
Some("chat") => Prompt::Chat,
Some("interactive") => Prompt::Interactive,
Some(s) => Prompt::One(s.to_string()),
None => Prompt::One(DEFAULT_PROMPT.to_string()),
};
let mut pre_prompt_tokens = vec![];
for prompt_index in 0.. {
let prompt_str = match &prompt {
Prompt::One(prompt) => prompt.clone(),
Prompt::Interactive | Prompt::Chat => {
let is_interactive = matches!(prompt, Prompt::Interactive);
print!("> ");
std::io::stdout().flush()?;
let mut prompt = String::new();
std::io::stdin().read_line(&mut prompt)?;
if prompt.ends_with('\n') {
prompt.pop();
if prompt.ends_with('\r') {
prompt.pop();
}
}
if args.which.is_open_chat() {
format!("GPT4 Correct User: {prompt}<|end_of_turn|>GPT4 Correct Assistant:")
} else if args.which.is_zephyr() {
if prompt_index == 0 || is_interactive {
format!("<|system|>\n</s>\n<|user|>\n{prompt}</s>\n<|assistant|>",)
} else {
format!("<|user|>\n{prompt}</s>\n<|assistant|>")
}
} else if args.which.is_mistral() {
format!("[INST] {prompt} [/INST]")
} else {
prompt
}
}
};
print!("{}", &prompt_str);
let tokens = tos
.tokenizer()
.encode(prompt_str, true)
.map_err(anyhow::Error::msg)?;
if args.verbose_prompt {
for (token, id) in tokens.get_tokens().iter().zip(tokens.get_ids().iter()) {
let token = token.replace('▁', " ").replace("<0x0A>", "\n");
println!("{id:7} -> '{token}'");
}
}
let prompt_tokens = [&pre_prompt_tokens, tokens.get_ids()].concat();
let to_sample = args.sample_len.saturating_sub(1);
let prompt_tokens = if prompt_tokens.len() + to_sample > model::MAX_SEQ_LEN - 10 {
let to_remove = prompt_tokens.len() + to_sample + 10 - model::MAX_SEQ_LEN;
prompt_tokens[prompt_tokens.len().saturating_sub(to_remove)..].to_vec()
} else {
prompt_tokens
};
let mut all_tokens = vec![];
let mut logits_processor = LogitsProcessor::new(args.seed, temperature, args.top_p);
let start_prompt_processing = std::time::Instant::now();
let mut next_token = {
let input = Tensor::new(prompt_tokens.as_slice(), &device)?.unsqueeze(0)?;
let logits = model.forward(&input, 0)?;
let logits = logits.squeeze(0)?;
logits_processor.sample(&logits)?
};
let prompt_dt = start_prompt_processing.elapsed();
all_tokens.push(next_token);
if let Some(t) = tos.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
let eos_token = if args.which.is_open_chat() {
"<|end_of_turn|>"
} else {
"</s>"
};
let eos_token = *tos.tokenizer().get_vocab(true).get(eos_token).unwrap();
let start_post_prompt = std::time::Instant::now();
let mut sampled = 0;
for index in 0..to_sample {
let input = Tensor::new(&[next_token], &device)?.unsqueeze(0)?;
let logits = model.forward(&input, prompt_tokens.len() + index)?;
let logits = logits.squeeze(0)?;
let logits = if args.repeat_penalty == 1. {
logits
} else {
let start_at = all_tokens.len().saturating_sub(args.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
args.repeat_penalty,
&all_tokens[start_at..],
)?
};
next_token = logits_processor.sample(&logits)?;
all_tokens.push(next_token);
if let Some(t) = tos.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
sampled += 1;
if next_token == eos_token {
break;
};
}
if let Some(rest) = tos.decode_rest().map_err(candle::Error::msg)? {
print!("{rest}");
}
std::io::stdout().flush()?;
let dt = start_post_prompt.elapsed();
println!(
"\n\n{:4} prompt tokens processed: {:.2} token/s",
prompt_tokens.len(),
prompt_tokens.len() as f64 / prompt_dt.as_secs_f64(),
);
println!(
"{sampled:4} tokens generated: {:.2} token/s",
sampled as f64 / dt.as_secs_f64(),
);
match prompt {
Prompt::One(_) => break,
Prompt::Interactive => {}
Prompt::Chat => {
pre_prompt_tokens = [prompt_tokens.as_slice(), all_tokens.as_slice()].concat()
}
}
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/quantized/README.md | # candle-quantized-llama: Fast Inference of quantized LLaMA models
This example provides a quantized LLaMA model similar to
[llama.cpp](https://github.com/ggerganov/llama.cpp). This is based on candle
built-in quantization methods. Supported features include:
- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support.
- SIMD optimizations on Apple Silicon and x86.
- Support using the `gguf` and `ggml` file formats.
The weights are automatically downloaded for you from the [HuggingFace
Hub](https://huggingface.co/) on the first run. There are various command line
flags to use local files instead, run with `--help` to learn about them.
## Running some example.
```bash
cargo run --example quantized --release -- --prompt "The best thing about coding in rust is "
> avx: true, neon: false, simd128: false, f16c: true
> temp: 0.80 repeat-penalty: 1.10 repeat-last-n: 64
> loaded 291 tensors (3.79GB) in 2.17s
> params: HParams { n_vocab: 32000, n_embd: 4096, n_mult: 256, n_head: 32, n_layer: 32, n_rot: 128, ftype: 2 }
> The best thing about coding in rust is 1.) that I don’t need to worry about memory leaks, 2.) speed and 3.) my program will compile even on old machines.
```
Using the mixtral sparse mixture of expert model:
```bash
$ cargo run --example quantized --release -- --which mixtral --prompt "Lebesgue's integral is superior to Riemann's because "
> avx: true, neon: false, simd128: false, f16c: true
> temp: 0.80 repeat-penalty: 1.10 repeat-last-n: 64
> loaded 995 tensors (26.44GB) in 0.03s
Lebesgue's integral is superior to Riemann's because 1. it is defined for a wider class of functions, those which are absolutely integrable; 2. the definition does not involve limits in two variables---one being computed before the other (which makes some computations more difficult); and 3. interchange of order of integration is easier to establish than with Riemann's integral. On the other hand, Lebesgue's integral applies only for bounded functions defined on finite intervals; it does not provide numerical values for improper integrals. The latter are best evaluated using Cauchy's limit definition.
The reason $f(x) = x^2$ is discontinuous at the ends of its interval of definition, and Riemann's integral requires continuity on the whole of an open interval containing it (see our earlier post), sine no such function exists with this property, is that the endpoints are infinite in measure for Lebesgue's integral.
```
## Command-line flags
Run with `--help` to see all options.
- `--which`: specify the model to use, e.g. `7b`, `13-chat`, `7b-code`.
- `--prompt interactive`: interactive mode where multiple prompts can be
entered.
- `--model mymodelfile.gguf`: use a local model file rather than getting one
from the hub.
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/custom-ops/main.rs | // This example illustrates how to implement custom operations. These operations can provide their
// own forward pass (CPU and GPU versions) as well as their backward pass.
//
// In this example we add the RMS normalization operation and implement it for f32.
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[rustfmt::skip]
#[cfg(feature = "cuda")]
mod cuda_kernels;
use clap::Parser;
use candle::{CpuStorage, CustomOp1, Layout, Result, Shape, Tensor};
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
}
struct LayerNorm {
eps: f32,
}
impl CustomOp1 for LayerNorm {
fn name(&self) -> &'static str {
"layer-norm"
}
fn cpu_fwd(&self, storage: &CpuStorage, layout: &Layout) -> Result<(CpuStorage, Shape)> {
let (dim1, dim2) = layout.shape().dims2()?;
let slice = storage.as_slice::<f32>()?;
let src = match layout.contiguous_offsets() {
None => candle::bail!("input has to be contiguous"),
Some((o1, o2)) => &slice[o1..o2],
};
let mut dst = Vec::with_capacity(dim1 * dim2);
for idx1 in 0..dim1 {
let src = &src[idx1 * dim2..(idx1 + 1) * dim2];
let variance = src.iter().map(|x| x * x).sum::<f32>();
let s_variance = 1f32 / (variance / dim2 as f32 + self.eps).sqrt();
dst.extend(src.iter().map(|x| x * s_variance))
}
let storage = candle::WithDType::to_cpu_storage_owned(dst);
Ok((storage, layout.shape().clone()))
}
#[cfg(feature = "cuda")]
fn cuda_fwd(
&self,
storage: &candle::CudaStorage,
layout: &Layout,
) -> Result<(candle::CudaStorage, Shape)> {
use candle::backend::BackendStorage;
use candle::cuda_backend::cudarc::driver::{LaunchAsync, LaunchConfig};
use candle::cuda_backend::WrapErr;
let (d1, d2) = layout.shape().dims2()?;
let d1 = d1 as u32;
let d2 = d2 as u32;
let dev = storage.device().clone();
let slice = storage.as_cuda_slice::<f32>()?;
let slice = match layout.contiguous_offsets() {
None => candle::bail!("input has to be contiguous"),
Some((o1, o2)) => slice.slice(o1..o2),
};
let elem_count = layout.shape().elem_count();
let dst = unsafe { dev.alloc::<f32>(elem_count) }.w()?;
let func = dev.get_or_load_func("rms_f32", cuda_kernels::LAYERNORM_KERNELS)?;
let params = (&dst, &slice, self.eps, d1, d2);
let cfg = LaunchConfig {
grid_dim: (d1, 1, 1),
block_dim: (d2, 1, 1),
shared_mem_bytes: 0,
};
unsafe { func.launch(cfg, params) }.w()?;
let dst = candle::CudaStorage::wrap_cuda_slice(dst, dev);
Ok((dst, layout.shape().clone()))
}
}
fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let t = Tensor::arange(0f32, 14f32, &device)?.reshape((2, 7))?;
println!("{t}");
let t = t.apply_op1(LayerNorm { eps: 1e-5 })?;
println!("{t}");
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples/custom-ops | hf_public_repos/candle/candle-examples/examples/custom-ops/kernels/layernorm_kernels.cu | #include <stdint.h>
#include "reduction_utils.cuh"
template <typename scalar_t>
__device__ void
rms_norm_kernel(scalar_t *__restrict__ out, // [num_tokens, hidden_size]
const scalar_t *__restrict__ input, // [num_tokens, hidden_size]
const float epsilon, const uint32_t num_tokens,
const uint32_t hidden_size) {
__shared__ float s_variance;
float variance = 0.0f;
for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
const float x = (float)input[blockIdx.x * hidden_size + idx];
variance += x * x;
}
variance = blockReduceSum<float>(variance);
if (threadIdx.x == 0) {
s_variance = rsqrtf(variance / hidden_size + epsilon);
}
__syncthreads();
for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
float x = (float)input[blockIdx.x * hidden_size + idx];
out[blockIdx.x * hidden_size + idx] = ((scalar_t)(x * s_variance));
}
}
extern "C" __global__ void rms_f32(
float *__restrict__ out, // [num_tokens, hidden_size]
const float *__restrict__ input, // [num_tokens, hidden_size]
const float epsilon, const uint32_t num_tokens,
const uint32_t hidden_size) {
rms_norm_kernel(out, input, epsilon, num_tokens, hidden_size);
}
| 0 |
hf_public_repos/candle/candle-examples/examples/custom-ops | hf_public_repos/candle/candle-examples/examples/custom-ops/kernels/reduction_utils.cuh | /*
* Adapted from
* https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/reduce_kernel_utils.cuh
* Copyright (c) 2023, The vLLM team.
* Copyright (c) 2020-2023, NVIDIA CORPORATION. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#pragma once
template <typename T> __inline__ __device__ T warpReduceSum(T val) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1)
val += __shfl_xor_sync(0xffffffff, val, mask, 32);
return val;
}
/* Calculate the sum of all elements in a block */
template <typename T> __inline__ __device__ T blockReduceSum(T val) {
static __shared__ T shared[32];
int lane = threadIdx.x & 0x1f;
int wid = threadIdx.x >> 5;
val = warpReduceSum<T>(val);
if (lane == 0)
shared[wid] = val;
__syncthreads();
// Modify from blockDim.x << 5 to blockDim.x / 32. to prevent
// blockDim.x is not divided by 32
val = (threadIdx.x < (blockDim.x / 32.f)) ? shared[lane] : (T)(0.0f);
val = warpReduceSum<T>(val);
return val;
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mobileone/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::{Parser, ValueEnum};
use candle::{DType, IndexOp, D};
use candle_nn::{Module, VarBuilder};
use candle_transformers::models::mobileone;
#[derive(Clone, Copy, Debug, ValueEnum)]
enum Which {
S0,
S1,
S2,
S3,
S4,
}
impl Which {
fn model_filename(&self) -> String {
let name = match self {
Self::S0 => "s0",
Self::S1 => "s1",
Self::S2 => "s2",
Self::S3 => "s3",
Self::S4 => "s4",
};
format!("timm/mobileone_{}.apple_in1k", name)
}
fn config(&self) -> mobileone::Config {
match self {
Self::S0 => mobileone::Config::s0(),
Self::S1 => mobileone::Config::s1(),
Self::S2 => mobileone::Config::s2(),
Self::S3 => mobileone::Config::s3(),
Self::S4 => mobileone::Config::s4(),
}
}
}
#[derive(Parser)]
struct Args {
#[arg(long)]
model: Option<String>,
#[arg(long)]
image: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
#[arg(value_enum, long, default_value_t=Which::S0)]
which: Which,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let image = candle_examples::imagenet::load_image224(args.image)?;
println!("loaded image {image:?}");
let model_file = match args.model {
None => {
let model_name = args.which.model_filename();
let api = hf_hub::api::sync::Api::new()?;
let api = api.model(model_name);
api.get("model.safetensors")?
}
Some(model) => model.into(),
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let model = mobileone::mobileone(&args.which.config(), 1000, vb)?;
println!("model built");
let logits = model.forward(&image.unsqueeze(0)?)?;
let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
.i(0)?
.to_vec1::<f32>()?;
let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
for &(category_idx, pr) in prs.iter().take(5) {
println!(
"{:24}: {:.2}%",
candle_examples::imagenet::CLASSES[category_idx],
100. * pr
);
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mobileone/README.md | # candle-mobileone
[MobileOne: An Improved One millisecond Mobile Backbone](https://arxiv.org/abs/2206.04040).
This candle implementation uses a pre-trained MobileOne network for inference. The
classification head has been trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Running an example
```
$ cargo run --example mobileone --release -- --image candle-examples/examples/yolo-v8/assets/bike.jpg --which s2
loaded image Tensor[dims 3, 224, 224; f32]
model built
mountain bike, all-terrain bike, off-roader: 79.33%
bicycle-built-for-two, tandem bicycle, tandem: 15.32%
crash helmet : 2.58%
unicycle, monocycle : 1.70%
alp : 0.21%
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/llama/main.rs | // An implementation of LLaMA https://github.com/facebookresearch/llama
//
// This is based on nanoGPT in a similar way to:
// https://github.com/Lightning-AI/lit-llama/blob/main/lit_llama/model.py
//
// The tokenizer config can be retrieved from:
// https://huggingface.co/hf-internal-testing/llama-tokenizer/raw/main/tokenizer.json
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
use anyhow::{bail, Error as E, Result};
use clap::{Parser, ValueEnum};
use candle::{DType, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use std::io::Write;
use candle_transformers::models::llama as model;
use model::{Llama, LlamaConfig};
const EOS_TOKEN: &str = "</s>";
const DEFAULT_PROMPT: &str = "My favorite theorem is ";
#[derive(Clone, Debug, Copy, PartialEq, Eq, ValueEnum)]
enum Which {
V1,
V2,
#[value(name = "solar-10.7b")]
Solar10_7B,
#[value(name = "tiny-llama-1.1b-chat")]
TinyLlama1_1BChat,
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, default_value_t = 100)]
sample_len: usize,
/// Disable the key-value cache.
#[arg(long)]
no_kv_cache: bool,
/// The initial prompt.
#[arg(long)]
prompt: Option<String>,
/// Use different dtype than f16
#[arg(long)]
dtype: Option<String>,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
model_id: Option<String>,
#[arg(long)]
revision: Option<String>,
/// The model size to use.
#[arg(long, default_value = "v2")]
which: Which,
#[arg(long)]
use_flash_attn: bool,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.0)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
use tokenizers::Tokenizer;
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
let device = candle_examples::device(args.cpu)?;
let dtype = match args.dtype.as_deref() {
Some("f16") => DType::F16,
Some("bf16") => DType::BF16,
Some("f32") => DType::F32,
Some(dtype) => bail!("Unsupported dtype {dtype}"),
None => DType::F16,
};
let (llama, tokenizer_filename, cache) = {
let api = Api::new()?;
let model_id = args.model_id.unwrap_or_else(|| match args.which {
Which::V1 => "Narsil/amall-7b".to_string(),
Which::V2 => "meta-llama/Llama-2-7b-hf".to_string(),
Which::Solar10_7B => "upstage/SOLAR-10.7B-v1.0".to_string(),
Which::TinyLlama1_1BChat => "TinyLlama/TinyLlama-1.1B-Chat-v1.0".to_string(),
});
println!("loading the model weights from {model_id}");
let revision = args.revision.unwrap_or("main".to_string());
let api = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
let tokenizer_filename = api.get("tokenizer.json")?;
let config_filename = api.get("config.json")?;
let config: LlamaConfig = serde_json::from_slice(&std::fs::read(config_filename)?)?;
let config = config.into_config(args.use_flash_attn);
let filenames = match args.which {
Which::V1 | Which::V2 | Which::Solar10_7B => {
candle_examples::hub_load_safetensors(&api, "model.safetensors.index.json")?
}
Which::TinyLlama1_1BChat => vec![api.get("model.safetensors")?],
};
println!("building the model");
let cache = model::Cache::new(!args.no_kv_cache, dtype, &config, &device)?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device)? };
(Llama::load(vb, &cache, &config)?, tokenizer_filename, cache)
};
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let eos_token_id = tokenizer.token_to_id(EOS_TOKEN);
let prompt = args.prompt.as_ref().map_or(DEFAULT_PROMPT, |p| p.as_str());
let mut tokens = tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
println!("starting the inference loop");
print!("{prompt}");
let mut logits_processor = LogitsProcessor::new(args.seed, args.temperature, args.top_p);
let start_gen = std::time::Instant::now();
let mut index_pos = 0;
let mut token_generated = 0;
for index in 0..args.sample_len {
let (context_size, context_index) = if cache.use_kv_cache && index > 0 {
(1, index_pos)
} else {
(tokens.len(), 0)
};
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
let input = Tensor::new(ctxt, &device)?.unsqueeze(0)?;
let logits = llama.forward(&input, context_index)?;
let logits = logits.squeeze(0)?;
let logits = if args.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(args.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
args.repeat_penalty,
&tokens[start_at..],
)?
};
index_pos += ctxt.len();
let next_token = logits_processor.sample(&logits)?;
token_generated += 1;
tokens.push(next_token);
// Extracting the last token as a string is complicated, here we just apply some simple
// heuristics as it seems to work well enough for this example. See the following for more
// details:
// https://github.com/huggingface/tokenizers/issues/1141#issuecomment-1562644141
if let Some(text) = tokenizer.id_to_token(next_token) {
let text = text.replace('▁', " ").replace("<0x0A>", "\n");
print!("{text}");
std::io::stdout().flush()?;
}
if Some(next_token) == eos_token_id {
break;
}
}
let dt = start_gen.elapsed();
println!(
"\n\n{} tokens generated ({} token/s)\n",
token_generated,
token_generated as f64 / dt.as_secs_f64(),
);
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/quantized-t5/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use std::io::Write;
use std::path::PathBuf;
use candle_transformers::models::quantized_t5 as t5;
use anyhow::{Error as E, Result};
use candle::{Device, Tensor};
use candle_transformers::generation::LogitsProcessor;
use clap::{Parser, ValueEnum};
use hf_hub::{api::sync::Api, api::sync::ApiRepo, Repo, RepoType};
use tokenizers::Tokenizer;
#[derive(Clone, Debug, Copy, ValueEnum)]
enum Which {
T5Small,
FlanT5Small,
FlanT5Base,
FlanT5Large,
FlanT5Xl,
FlanT5Xxl,
}
#[derive(Parser, Debug, Clone)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
/// The model repository to use on the HuggingFace hub.
#[arg(long)]
model_id: Option<String>,
#[arg(long)]
revision: Option<String>,
#[arg(long)]
weight_file: Option<String>,
#[arg(long)]
config_file: Option<String>,
// Enable/disable decoding.
#[arg(long, default_value = "false")]
disable_cache: bool,
/// Use this prompt, otherwise compute sentence similarities.
#[arg(long)]
prompt: String,
/// The temperature used to generate samples.
#[arg(long, default_value_t = 0.8)]
temperature: f64,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
/// The model size to use.
#[arg(long, default_value = "t5-small")]
which: Which,
}
struct T5ModelBuilder {
device: Device,
config: t5::Config,
weights_filename: PathBuf,
}
impl T5ModelBuilder {
pub fn load(args: &Args) -> Result<(Self, Tokenizer)> {
let device = Device::Cpu;
let default_model = "lmz/candle-quantized-t5".to_string();
let (model_id, revision) = match (args.model_id.to_owned(), args.revision.to_owned()) {
(Some(model_id), Some(revision)) => (model_id, revision),
(Some(model_id), None) => (model_id, "main".to_string()),
(None, Some(revision)) => (default_model, revision),
(None, None) => (default_model, "main".to_string()),
};
let repo = Repo::with_revision(model_id, RepoType::Model, revision);
let api = Api::new()?;
let api = api.repo(repo);
let config_filename = match &args.config_file {
Some(filename) => Self::get_local_or_remote_file(filename, &api)?,
None => match args.which {
Which::T5Small => api.get("config.json")?,
Which::FlanT5Small => api.get("config-flan-t5-small.json")?,
Which::FlanT5Base => api.get("config-flan-t5-base.json")?,
Which::FlanT5Large => api.get("config-flan-t5-large.json")?,
Which::FlanT5Xl => api.get("config-flan-t5-xl.json")?,
Which::FlanT5Xxl => api.get("config-flan-t5-xxl.json")?,
},
};
let tokenizer_filename = api.get("tokenizer.json")?;
let weights_filename = match &args.weight_file {
Some(filename) => Self::get_local_or_remote_file(filename, &api)?,
None => match args.which {
Which::T5Small => api.get("model.gguf")?,
Which::FlanT5Small => api.get("model-flan-t5-small.gguf")?,
Which::FlanT5Base => api.get("model-flan-t5-base.gguf")?,
Which::FlanT5Large => api.get("model-flan-t5-large.gguf")?,
Which::FlanT5Xl => api.get("model-flan-t5-xl.gguf")?,
Which::FlanT5Xxl => api.get("model-flan-t5-xxl.gguf")?,
},
};
let config = std::fs::read_to_string(config_filename)?;
let mut config: t5::Config = serde_json::from_str(&config)?;
config.use_cache = !args.disable_cache;
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
Ok((
Self {
device,
config,
weights_filename,
},
tokenizer,
))
}
pub fn build_model(&self) -> Result<t5::T5ForConditionalGeneration> {
let device = Device::Cpu;
let vb = t5::VarBuilder::from_gguf(&self.weights_filename, &device)?;
Ok(t5::T5ForConditionalGeneration::load(vb, &self.config)?)
}
fn get_local_or_remote_file(filename: &str, api: &ApiRepo) -> Result<PathBuf> {
let local_filename = std::path::PathBuf::from(filename);
if local_filename.exists() {
Ok(local_filename)
} else {
Ok(api.get(filename)?)
}
}
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
let (builder, mut tokenizer) = T5ModelBuilder::load(&args)?;
let device = &builder.device;
let tokenizer = tokenizer
.with_padding(None)
.with_truncation(None)
.map_err(E::msg)?;
let tokens = tokenizer
.encode(args.prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let input_token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
let mut model = builder.build_model()?;
let mut output_token_ids = [builder
.config
.decoder_start_token_id
.unwrap_or(builder.config.pad_token_id) as u32]
.to_vec();
let temperature = if args.temperature <= 0. {
None
} else {
Some(args.temperature)
};
let mut logits_processor = LogitsProcessor::new(299792458, temperature, args.top_p);
let encoder_output = model.encode(&input_token_ids)?;
let start = std::time::Instant::now();
for index in 0.. {
if output_token_ids.len() > 512 {
break;
}
let decoder_token_ids = if index == 0 || !builder.config.use_cache {
Tensor::new(output_token_ids.as_slice(), device)?.unsqueeze(0)?
} else {
let last_token = *output_token_ids.last().unwrap();
Tensor::new(&[last_token], device)?.unsqueeze(0)?
};
let logits = model
.decode(&decoder_token_ids, &encoder_output)?
.squeeze(0)?;
let logits = if args.repeat_penalty == 1. {
logits
} else {
let start_at = output_token_ids.len().saturating_sub(args.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
args.repeat_penalty,
&output_token_ids[start_at..],
)?
};
let next_token_id = logits_processor.sample(&logits)?;
if next_token_id as usize == builder.config.eos_token_id {
break;
}
output_token_ids.push(next_token_id);
if let Some(text) = tokenizer.id_to_token(next_token_id) {
let text = text.replace('▁', " ").replace("<0x0A>", "\n");
print!("{text}");
std::io::stdout().flush()?;
}
}
let dt = start.elapsed();
println!(
"\n{} tokens generated ({:.2} token/s)\n",
output_token_ids.len(),
output_token_ids.len() as f64 / dt.as_secs_f64(),
);
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/quantized-t5/README.md | # candle-quantized-t5
## Seq2Seq example
This example uses a quantized version of the t5 model.
```bash
$ cargo run --example quantized-t5 --release -- --prompt "translate to German: A beautiful candle."
...
Eine schöne Kerze.
```
## Generating Quantized weight files
The weight file is automatically retrieved from the hub. It is also possible to
generate quantized weight files from the original safetensors file by using the
`tensor-tools` command line utility via:
```bash
$ cargo run --example tensor-tools --release -- quantize --quantization q6k PATH/TO/T5/model.safetensors /tmp/model.gguf
```
## Using custom models
To use a different model, specify the `model-id`.
For example, for text editing, you can use quantized [CoEdit models](https://huggingface.co/jbochi/candle-coedit-quantized).
```bash
$ cargo run --example quantized-t5 --release -- \
--model-id "jbochi/candle-coedit-quantized" \
--prompt "Make this text coherent: Their flight is weak. They run quickly through the tree canopy." \
--temperature 0
...
Although their flight is weak, they run quickly through the tree canopy.
```
By default, it will look for `model.gguf` and `config.json`, but you can specify
custom local or remote `weight-file` and `config-file`s:
```bash
cargo run --example quantized-t5 --release -- \
--model-id "jbochi/candle-coedit-quantized" \
--weight-file "model-xl.gguf" \
--config-file "config-xl.json" \
--prompt "Rewrite to make this easier to understand: Note that a storm surge is what forecasters consider a hurricane's most treacherous aspect." \
--temperature 0
...
Note that a storm surge is what forecasters consider a hurricane's most dangerous part.
```
### [MADLAD-400](https://arxiv.org/abs/2309.04662)
MADLAD-400 is a series of multilingual machine translation T5 models trained on 250 billion tokens covering over 450 languages using publicly available data. These models are competitive with significantly larger models.
```bash
cargo run --example quantized-t5 --release -- \
--model-id "jbochi/madlad400-3b-mt" --weight-file "model-q4k.gguf" \
--prompt "<2de> How are you, my friend?" \
--temperature 0
...
Wie geht es dir, mein Freund?
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/jina-bert/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle_transformers::models::jina_bert::{BertModel, Config};
use anyhow::Error as E;
use candle::{DType, Module, Tensor};
use candle_nn::VarBuilder;
use clap::Parser;
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
/// When set, compute embeddings for this prompt.
#[arg(long)]
prompt: Option<String>,
/// The number of times to run the prompt.
#[arg(long, default_value = "1")]
n: usize,
/// L2 normalization for embeddings.
#[arg(long, default_value = "true")]
normalize_embeddings: bool,
#[arg(long)]
tokenizer: Option<String>,
#[arg(long)]
model: Option<String>,
}
impl Args {
fn build_model_and_tokenizer(&self) -> anyhow::Result<(BertModel, tokenizers::Tokenizer)> {
use hf_hub::{api::sync::Api, Repo, RepoType};
let model = match &self.model {
Some(model_file) => std::path::PathBuf::from(model_file),
None => Api::new()?
.repo(Repo::new(
"jinaai/jina-embeddings-v2-base-en".to_string(),
RepoType::Model,
))
.get("model.safetensors")?,
};
let tokenizer = match &self.tokenizer {
Some(file) => std::path::PathBuf::from(file),
None => Api::new()?
.repo(Repo::new(
"sentence-transformers/all-MiniLM-L6-v2".to_string(),
RepoType::Model,
))
.get("tokenizer.json")?,
};
let device = candle_examples::device(self.cpu)?;
let config = Config::v2_base();
let tokenizer = tokenizers::Tokenizer::from_file(tokenizer).map_err(E::msg)?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model], DType::F32, &device)? };
let model = BertModel::new(vb, &config)?;
Ok((model, tokenizer))
}
}
fn main() -> anyhow::Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
println!("tracing...");
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
let start = std::time::Instant::now();
let (model, mut tokenizer) = args.build_model_and_tokenizer()?;
let device = &model.device;
if let Some(prompt) = args.prompt {
let tokenizer = tokenizer
.with_padding(None)
.with_truncation(None)
.map_err(E::msg)?;
let tokens = tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let token_ids = Tensor::new(&tokens[..], device)?.unsqueeze(0)?;
println!("Loaded and encoded {:?}", start.elapsed());
for idx in 0..args.n {
let start = std::time::Instant::now();
let ys = model.forward(&token_ids)?;
if idx == 0 {
println!("{ys}");
}
println!("Took {:?}", start.elapsed());
}
} else {
let sentences = [
"The cat sits outside",
"A man is playing guitar",
"I love pasta",
"The new movie is awesome",
"The cat plays in the garden",
"A woman watches TV",
"The new movie is so great",
"Do you like pizza?",
];
let n_sentences = sentences.len();
if let Some(pp) = tokenizer.get_padding_mut() {
pp.strategy = tokenizers::PaddingStrategy::BatchLongest
} else {
let pp = tokenizers::PaddingParams {
strategy: tokenizers::PaddingStrategy::BatchLongest,
..Default::default()
};
tokenizer.with_padding(Some(pp));
}
let tokens = tokenizer
.encode_batch(sentences.to_vec(), true)
.map_err(E::msg)?;
let token_ids = tokens
.iter()
.map(|tokens| {
let tokens = tokens.get_ids().to_vec();
Tensor::new(tokens.as_slice(), device)
})
.collect::<candle::Result<Vec<_>>>()?;
let token_ids = Tensor::stack(&token_ids, 0)?;
println!("running inference on batch {:?}", token_ids.shape());
let embeddings = model.forward(&token_ids)?;
println!("generated embeddings {:?}", embeddings.shape());
// Apply some avg-pooling by taking the mean embedding value for all tokens (including padding)
let (_n_sentence, n_tokens, _hidden_size) = embeddings.dims3()?;
let embeddings = (embeddings.sum(1)? / (n_tokens as f64))?;
let embeddings = if args.normalize_embeddings {
normalize_l2(&embeddings)?
} else {
embeddings
};
println!("pooled embeddings {:?}", embeddings.shape());
let mut similarities = vec![];
for i in 0..n_sentences {
let e_i = embeddings.get(i)?;
for j in (i + 1)..n_sentences {
let e_j = embeddings.get(j)?;
let sum_ij = (&e_i * &e_j)?.sum_all()?.to_scalar::<f32>()?;
let sum_i2 = (&e_i * &e_i)?.sum_all()?.to_scalar::<f32>()?;
let sum_j2 = (&e_j * &e_j)?.sum_all()?.to_scalar::<f32>()?;
let cosine_similarity = sum_ij / (sum_i2 * sum_j2).sqrt();
similarities.push((cosine_similarity, i, j))
}
}
similarities.sort_by(|u, v| v.0.total_cmp(&u.0));
for &(score, i, j) in similarities[..5].iter() {
println!("score: {score:.2} '{}' '{}'", sentences[i], sentences[j])
}
}
Ok(())
}
pub fn normalize_l2(v: &Tensor) -> candle::Result<Tensor> {
v.broadcast_div(&v.sqr()?.sum_keepdim(1)?.sqrt()?)
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/jina-bert/README.md | # candle-jina-bert
Jina-Bert is a general large language model with a context size of 8192, [model
card](https://huggingface.co/jinaai/jina-embeddings-v2-base-en). In this example
it can be used for two different tasks:
- Compute sentence embeddings for a prompt.
- Compute similarities between a set of sentences.
## Sentence embeddings
Jina-Bert is used to compute the sentence embeddings for a prompt. The model weights
are downloaded from the hub on the first run.
```bash
cargo run --example jina-bert --release -- --prompt "Here is a test sentence"
> [[[ 0.1595, -0.9885, 0.6494, ..., 0.3003, -0.6901, -1.2355],
> [ 0.0374, -0.1798, 1.3359, ..., 0.6731, 0.2133, -1.6807],
> [ 0.1700, -0.8534, 0.8924, ..., -0.1785, -0.0727, -1.5087],
> ...
> [-0.3113, -1.3665, 0.2027, ..., -0.2519, 0.1711, -1.5811],
> [ 0.0907, -1.0492, 0.5382, ..., 0.0242, -0.7077, -1.0830],
> [ 0.0369, -0.6343, 0.6105, ..., 0.0671, 0.3778, -1.1505]]]
> Tensor[[1, 7, 768], f32]
```
## Similarities
In this example, Jina-Bert is used to compute the sentence embeddings for a set of
sentences (hardcoded in the examples). Then cosine similarities are computed for
each sentence pair and they are reported by decreasing values, hence the first
reported pair contains the two sentences that have the highest similarity score.
The sentence embeddings are computed using average pooling through all the
sentence tokens, including some potential padding.
```bash
cargo run --example jina-bert --release
> score: 0.94 'The new movie is awesome' 'The new movie is so great'
> score: 0.81 'The cat sits outside' 'The cat plays in the garden'
> score: 0.78 'I love pasta' 'Do you like pizza?'
> score: 0.68 'I love pasta' 'The new movie is awesome'
> score: 0.67 'A man is playing guitar' 'A woman watches TV'
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/bigcode/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::bigcode::{Config, GPTBigCode};
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
struct TextGeneration {
model: GPTBigCode,
device: Device,
tokenizer: Tokenizer,
logits_processor: LogitsProcessor,
}
impl TextGeneration {
fn new(
model: GPTBigCode,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
top_p: Option<f64>,
device: &Device,
) -> Self {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer,
logits_processor,
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
println!("starting the inference loop");
print!("{prompt}");
std::io::stdout().flush()?;
let mut tokens = self
.tokenizer
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
let mut new_tokens = vec![];
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let (context_size, past_len) = if self.model.config().use_cache && index > 0 {
(1, tokens.len().saturating_sub(1))
} else {
(tokens.len(), 0)
};
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = self.model.forward(&input, past_len)?;
let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
new_tokens.push(next_token);
let token = self.tokenizer.decode(&[next_token], true).map_err(E::msg)?;
print!("{token}");
std::io::stdout().flush()?;
}
let dt = start_gen.elapsed();
println!(
"{sample_len} tokens generated ({:.3} token/s)",
sample_len as f64 / dt.as_secs_f64(),
);
Ok(())
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
#[arg(long)]
prompt: String,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, default_value_t = 100)]
sample_len: usize,
#[arg(long, default_value = "bigcode/starcoderbase-1b")]
model_id: String,
#[arg(long, default_value = "main")]
revision: String,
#[arg(long)]
weight_file: Option<String>,
}
fn main() -> Result<()> {
let args = Args::parse();
let start = std::time::Instant::now();
let api = Api::new()?;
let repo = api.repo(Repo::with_revision(
args.model_id,
RepoType::Model,
args.revision,
));
let tokenizer_filename = repo.get("tokenizer.json")?;
let filenames = match args.weight_file {
Some(weight_file) => vec![std::path::PathBuf::from(weight_file)],
None => ["model.safetensors"]
.iter()
.map(|f| repo.get(f))
.collect::<std::result::Result<Vec<_>, _>>()?,
};
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let device = candle_examples::device(args.cpu)?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
let config = Config::starcoder_1b();
let model = GPTBigCode::load(vb, config)?;
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(
model,
tokenizer,
args.seed,
args.temperature,
args.top_p,
&device,
);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/bigcode/README.md | # candle-starcoder: code generation model
[StarCoder/BigCode](https://huggingface.co/bigcode/starcoderbase-1b) is a LLM
model specialized to code generation. The initial model was trained on 80
programming languages.
## Running some example
```bash
cargo run --example bigcode --release -- --prompt "fn fact(n: u64) -> u64 "
> fn fact(n: u64) -> u64 {
> if n == 0 {
> 1
> } else {
> n * fact(n - 1)
> }
> }
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/replit-code/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::Parser;
use candle_transformers::models::mpt::{Config, Model as M};
use candle_transformers::models::quantized_mpt::Model as Q;
use candle::{DType, Device, Tensor};
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
enum Model {
M(M),
Q(Q),
}
impl Model {
fn forward(&mut self, xs: &Tensor) -> candle::Result<Tensor> {
match self {
Self::M(model) => model.forward(xs),
Self::Q(model) => model.forward(xs),
}
}
}
struct TextGeneration {
model: Model,
device: Device,
tokenizer: Tokenizer,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
verbose_prompt: bool,
}
impl TextGeneration {
#[allow(clippy::too_many_arguments)]
fn new(
model: Model,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
top_p: Option<f64>,
repeat_penalty: f32,
repeat_last_n: usize,
verbose_prompt: bool,
device: &Device,
) -> Self {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer,
logits_processor,
repeat_penalty,
repeat_last_n,
verbose_prompt,
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
println!("starting the inference loop");
let tokens = self.tokenizer.encode(prompt, true).map_err(E::msg)?;
if tokens.is_empty() {
anyhow::bail!("Empty prompts are not supported in the phi model.")
}
if self.verbose_prompt {
for (token, id) in tokens.get_tokens().iter().zip(tokens.get_ids().iter()) {
let token = token.replace('▁', " ").replace("<0x0A>", "\n");
println!("{id:7} -> '{token}'");
}
}
let mut tokens = tokens.get_ids().to_vec();
let mut generated_tokens = 0usize;
let eos_token = match self.tokenizer.get_vocab(true).get("<|endoftext|>") {
Some(token) => *token,
None => anyhow::bail!("cannot find the endoftext token"),
};
print!("{prompt}");
std::io::stdout().flush()?;
let start_gen = std::time::Instant::now();
for index in 0..sample_len {
let context_size = if index > 0 { 1 } else { tokens.len() };
let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
let input = Tensor::new(ctxt, &self.device)?.unsqueeze(0)?;
let logits = self.model.forward(&input)?;
let logits = logits.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(self.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
generated_tokens += 1;
if next_token == eos_token {
break;
}
let token = self.tokenizer.decode(&[next_token], true).map_err(E::msg)?;
print!("{token}");
std::io::stdout().flush()?;
}
let dt = start_gen.elapsed();
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
generated_tokens as f64 / dt.as_secs_f64(),
);
Ok(())
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
/// Display the token for the specified prompt.
#[arg(long)]
verbose_prompt: bool,
#[arg(long)]
prompt: String,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, short = 'n', default_value_t = 1000)]
sample_len: usize,
#[arg(long)]
model_id: Option<String>,
#[arg(long)]
revision: Option<String>,
#[arg(long)]
quantized: bool,
#[arg(long)]
weight_file: Option<String>,
#[arg(long)]
tokenizer: Option<String>,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
println!(
"avx: {}, neon: {}, simd128: {}, f16c: {}",
candle::utils::with_avx(),
candle::utils::with_neon(),
candle::utils::with_simd128(),
candle::utils::with_f16c()
);
println!(
"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
args.temperature.unwrap_or(0.),
args.repeat_penalty,
args.repeat_last_n
);
let start = std::time::Instant::now();
let api = Api::new()?;
let model_id = match args.model_id {
Some(model_id) => model_id.to_string(),
None => "lmz/candle-replit-code".to_string(),
};
let revision = match args.revision {
Some(rev) => rev.to_string(),
None => "main".to_string(),
};
let repo = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
let tokenizer_filename = match args.tokenizer {
Some(file) => std::path::PathBuf::from(file),
None => repo.get("tokenizer.json")?,
};
let filename = match args.weight_file {
Some(weight_file) => std::path::PathBuf::from(weight_file),
None => {
if args.quantized {
repo.get("model-replit-code-v1_5-q4k.gguf")?
} else {
repo.get("model.safetensors")?
}
}
};
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let device = candle_examples::device(args.cpu)?;
let config = Config::replit_code_v1_5_3b();
let model = if args.quantized {
let vb =
candle_transformers::quantized_var_builder::VarBuilder::from_gguf(&filename, &device)?;
Model::Q(Q::new(&config, vb.pp("transformer"))?)
} else {
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[filename], DType::F32, &device)? };
Model::M(M::new(&config, vb.pp("transformer"))?)
};
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(
model,
tokenizer,
args.seed,
args.temperature,
args.top_p,
args.repeat_penalty,
args.repeat_last_n,
args.verbose_prompt,
&device,
);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/replit-code/README.md | # candle-replit-code: code completion specialized model.
[replit-code-v1_5-3b](https://huggingface.co/replit/replit-code-v1_5-3b) is a
language model specialized for code completion. This model uses 3.3B parameters
in `bfloat16` (so the GPU version will only work on recent nvidia cards).
## Running some example
```bash
cargo run --example replit-code --release -- --prompt 'def fibonacci(n): '
```
This produces the following output.
```
def fibonacci(n): # write Fibonacci series up to n
"""Print a Fibonacci series up to n."""
a, b = 0, 1
while a < n:
print(a, end=' ')
a, b = b, a+b
print()
def fibonacci_loop(n): # write Fibonacci series up to n
"""Print a Fibonacci series up to n."""
result = []
a, b = 0, 1
while a < n:
result.append(a)
a, b = b, a+b
return result
def fibonacci_generator(n): # write Fibonacci series up to n
"""Print a Fibonacci series up to n."""
a, b = 0, 1
while a < n:
yield a
a, b = b, a+b
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mamba-minimal/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use clap::{Parser, ValueEnum};
mod model;
use model::{Config, Model};
use candle::{DType, Device, Module, Tensor};
use candle_examples::token_output_stream::TokenOutputStream;
use candle_nn::VarBuilder;
use candle_transformers::generation::LogitsProcessor;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::Tokenizer;
struct TextGeneration {
model: Model,
device: Device,
tokenizer: TokenOutputStream,
logits_processor: LogitsProcessor,
repeat_penalty: f32,
repeat_last_n: usize,
}
impl TextGeneration {
#[allow(clippy::too_many_arguments)]
fn new(
model: Model,
tokenizer: Tokenizer,
seed: u64,
temp: Option<f64>,
top_p: Option<f64>,
repeat_penalty: f32,
repeat_last_n: usize,
device: &Device,
) -> Self {
let logits_processor = LogitsProcessor::new(seed, temp, top_p);
Self {
model,
tokenizer: TokenOutputStream::new(tokenizer),
logits_processor,
repeat_penalty,
repeat_last_n,
device: device.clone(),
}
}
fn run(&mut self, prompt: &str, sample_len: usize) -> Result<()> {
use std::io::Write;
self.tokenizer.clear();
let mut tokens = self
.tokenizer
.tokenizer()
.encode(prompt, true)
.map_err(E::msg)?
.get_ids()
.to_vec();
for &t in tokens.iter() {
if let Some(t) = self.tokenizer.next_token(t)? {
print!("{t}")
}
}
std::io::stdout().flush()?;
let mut generated_tokens = 0usize;
let eos_token = match self.tokenizer.get_token("<|endoftext|>") {
Some(token) => token,
None => anyhow::bail!("cannot find the </s> token"),
};
let start_gen = std::time::Instant::now();
for _ in 0..sample_len {
let input = Tensor::new(tokens.as_slice(), &self.device)?.unsqueeze(0)?;
let logits = self.model.forward(&input)?;
let logits = logits.squeeze(0)?.squeeze(0)?.to_dtype(DType::F32)?;
let logits = if self.repeat_penalty == 1. {
logits
} else {
let start_at = tokens.len().saturating_sub(self.repeat_last_n);
candle_transformers::utils::apply_repeat_penalty(
&logits,
self.repeat_penalty,
&tokens[start_at..],
)?
};
let next_token = self.logits_processor.sample(&logits)?;
tokens.push(next_token);
generated_tokens += 1;
if next_token == eos_token {
break;
}
if let Some(t) = self.tokenizer.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
}
let dt = start_gen.elapsed();
if let Some(rest) = self.tokenizer.decode_rest().map_err(E::msg)? {
print!("{rest}");
}
std::io::stdout().flush()?;
println!(
"\n{generated_tokens} tokens generated ({:.2} token/s)",
generated_tokens as f64 / dt.as_secs_f64(),
);
Ok(())
}
}
#[derive(Parser, ValueEnum, Clone, Copy, PartialEq, Eq, Debug)]
enum Which {
Mamba130m,
Mamba370m,
Mamba790m,
Mamba1_4b,
Mamba2_8b,
Mamba2_8bSlimPj,
}
impl std::fmt::Display for Which {
fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
write!(f, "{:?}", self)
}
}
impl Which {
fn model_id(&self) -> &'static str {
match self {
Self::Mamba130m => "state-spaces/mamba-130m",
Self::Mamba370m => "state-spaces/mamba-370m",
Self::Mamba790m => "state-spaces/mamba-790m",
Self::Mamba1_4b => "state-spaces/mamba-1.4b",
Self::Mamba2_8b => "state-spaces/mamba-2.8b",
Self::Mamba2_8bSlimPj => "state-spaces/mamba-2.8b-slimpj'",
}
}
fn revision(&self) -> &'static str {
match self {
Self::Mamba130m
| Self::Mamba370m
| Self::Mamba790m
| Self::Mamba1_4b
| Self::Mamba2_8bSlimPj => "refs/pr/1",
Self::Mamba2_8b => "refs/pr/4",
}
}
}
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// Enable tracing (generates a trace-timestamp.json file).
#[arg(long)]
tracing: bool,
#[arg(long)]
prompt: String,
/// The temperature used to generate samples.
#[arg(long)]
temperature: Option<f64>,
/// Nucleus sampling probability cutoff.
#[arg(long)]
top_p: Option<f64>,
/// The seed to use when generating random samples.
#[arg(long, default_value_t = 299792458)]
seed: u64,
/// The length of the sample to generate (in tokens).
#[arg(long, short = 'n', default_value_t = 5000)]
sample_len: usize,
#[arg(long, default_value = "mamba130m")]
which: Which,
#[arg(long)]
model_id: Option<String>,
#[arg(long)]
revision: Option<String>,
#[arg(long)]
tokenizer_file: Option<String>,
#[arg(long)]
weight_files: Option<String>,
#[arg(long)]
config_file: Option<String>,
/// Penalty to be applied for repeating tokens, 1. means no penalty.
#[arg(long, default_value_t = 1.1)]
repeat_penalty: f32,
/// The context size to consider for the repeat penalty.
#[arg(long, default_value_t = 64)]
repeat_last_n: usize,
}
fn main() -> Result<()> {
use tracing_chrome::ChromeLayerBuilder;
use tracing_subscriber::prelude::*;
let args = Args::parse();
let _guard = if args.tracing {
let (chrome_layer, guard) = ChromeLayerBuilder::new().build();
tracing_subscriber::registry().with(chrome_layer).init();
Some(guard)
} else {
None
};
println!(
"avx: {}, neon: {}, simd128: {}, f16c: {}",
candle::utils::with_avx(),
candle::utils::with_neon(),
candle::utils::with_simd128(),
candle::utils::with_f16c()
);
println!(
"temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
args.temperature.unwrap_or(0.),
args.repeat_penalty,
args.repeat_last_n
);
let start = std::time::Instant::now();
let api = Api::new()?;
let repo = api.repo(Repo::with_revision(
args.model_id
.unwrap_or_else(|| args.which.model_id().to_string()),
RepoType::Model,
args.revision
.unwrap_or_else(|| args.which.revision().to_string()),
));
let tokenizer_filename = match args.tokenizer_file {
Some(file) => std::path::PathBuf::from(file),
None => api
.model("EleutherAI/gpt-neox-20b".to_string())
.get("tokenizer.json")?,
};
let config_filename = match args.config_file {
Some(file) => std::path::PathBuf::from(file),
None => repo.get("config.json")?,
};
let filenames = match args.weight_files {
Some(files) => files
.split(',')
.map(std::path::PathBuf::from)
.collect::<Vec<_>>(),
None => {
vec![repo.get("model.safetensors")?]
}
};
println!("retrieved the files in {:?}", start.elapsed());
let tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(E::msg)?;
let start = std::time::Instant::now();
let config: Config = serde_json::from_slice(&std::fs::read(config_filename)?)?;
let device = candle_examples::device(args.cpu)?;
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, DType::F32, &device)? };
let model = Model::new(&config, vb.pp("backbone"))?;
println!("loaded the model in {:?}", start.elapsed());
let mut pipeline = TextGeneration::new(
model,
tokenizer,
args.seed,
args.temperature,
args.top_p,
args.repeat_penalty,
args.repeat_last_n,
&device,
);
pipeline.run(&args.prompt, args.sample_len)?;
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mamba-minimal/README.md | # candle-mamba-minimal: minimal implementation of Mamba
This is based on [mamba-minimal](https://github.com/johnma2006/mamba-minimal).
## Running the example
```bash
$ cargo run --example mamba-minimal --release -- --prompt "Mamba is the"
Mamba is the most popular and best-selling game in the world. It has been downloaded more than 1,000 times by over 1 million people worldwide since its release on March 18th 2016.
The Mamba series of games are a collection that combines elements from all genres including action, adventure, strategy & puzzle games with some unique gameplay features such as stealth and survival. The game is also known for its innovative graphics and the ability to play in a variety of different modes like single player or multiplayer.
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/mamba-minimal/model.rs | /// This follows the lines of:
/// https://github.com/johnma2006/mamba-minimal/blob/master/model.py
/// Simple, minimal implementation of Mamba in one file of PyTorch.
use candle::{IndexOp, Module, Result, Tensor, D};
use candle_nn::{RmsNorm, VarBuilder};
use candle_transformers::models::with_tracing::{linear, linear_no_bias, Linear};
#[derive(Debug, Clone, serde::Deserialize)]
pub struct Config {
d_model: usize,
n_layer: usize,
vocab_size: usize,
pad_vocab_size_multiple: usize,
}
impl Config {
fn vocab_size(&self) -> usize {
let pad = self.pad_vocab_size_multiple;
(self.vocab_size + pad - 1) / pad * pad
}
fn dt_rank(&self) -> usize {
(self.d_model + 15) / 16
}
fn d_conv(&self) -> usize {
4
}
fn d_state(&self) -> usize {
16
}
fn d_inner(&self) -> usize {
self.d_model * 2
}
}
// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L177
#[derive(Clone, Debug)]
pub struct MambaBlock {
in_proj: Linear,
conv1d: candle_nn::Conv1d,
x_proj: Linear,
dt_proj: Linear,
a_log: Tensor,
d: Tensor,
out_proj: Linear,
dt_rank: usize,
}
impl MambaBlock {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let d_inner = cfg.d_inner();
let d_conv = cfg.d_conv();
let d_state = cfg.d_state();
let dt_rank = cfg.dt_rank();
let in_proj = linear_no_bias(cfg.d_model, d_inner * 2, vb.pp("in_proj"))?;
let conv_cfg = candle_nn::Conv1dConfig {
groups: d_inner,
padding: d_conv - 1,
..Default::default()
};
let conv1d = candle_nn::conv1d(d_inner, d_inner, d_conv, conv_cfg, vb.pp("conv1d"))?;
let x_proj = linear_no_bias(d_inner, dt_rank + d_state * 2, vb.pp("x_proj"))?;
let dt_proj = linear(dt_rank, d_inner, vb.pp("dt_proj"))?;
let a_log = vb.get((d_inner, d_state), "A_log")?;
let d = vb.get(d_inner, "D")?;
let out_proj = linear_no_bias(d_inner, cfg.d_model, vb.pp("out_proj"))?;
Ok(Self {
in_proj,
conv1d,
x_proj,
dt_proj,
a_log,
d,
out_proj,
dt_rank,
})
}
fn ssm(&self, xs: &Tensor) -> Result<Tensor> {
let (_d_in, n) = self.a_log.dims2()?;
let a = self.a_log.to_dtype(candle::DType::F32)?.exp()?.neg()?;
let d = self.d.to_dtype(candle::DType::F32)?;
let x_dbl = xs.apply(&self.x_proj)?;
let delta = x_dbl.narrow(D::Minus1, 0, self.dt_rank)?;
let b = x_dbl.narrow(D::Minus1, self.dt_rank, n)?;
let c = x_dbl.narrow(D::Minus1, self.dt_rank + n, n)?;
let delta = delta.contiguous()?.apply(&self.dt_proj)?;
// softplus without threshold
let delta = (delta.exp()? + 1.)?.log()?;
let ss = selective_scan(xs, &delta, &a, &b, &c, &d)?;
Ok(ss)
}
}
// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L275
fn selective_scan(
u: &Tensor,
delta: &Tensor,
a: &Tensor,
b: &Tensor,
c: &Tensor,
d: &Tensor,
) -> Result<Tensor> {
let (b_sz, l, d_in) = u.dims3()?;
let n = a.dim(1)?;
let delta = delta.t()?.reshape((b_sz, d_in, l, 1))?; // b d_in l 1
let delta_a = delta.broadcast_mul(&a.reshape((1, d_in, 1, n))?)?.exp()?;
let delta_b_u = delta
.broadcast_mul(&b.reshape((b_sz, 1, l, n))?)?
.broadcast_mul(&u.t()?.reshape((b_sz, d_in, l, 1))?)?;
let mut xs = Tensor::zeros((b_sz, d_in, n), delta_a.dtype(), delta_a.device())?;
let mut ys = Vec::with_capacity(l);
for i in 0..l {
xs = ((delta_a.i((.., .., i))? * xs)? + delta_b_u.i((.., .., i))?)?;
let y = xs.matmul(&c.i((.., i, ..))?.unsqueeze(2)?)?.squeeze(2)?;
ys.push(y)
}
let ys = Tensor::stack(ys.as_slice(), 1)?;
ys + u.broadcast_mul(d)
}
impl Module for MambaBlock {
// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L206
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let (_b_sz, seq_len, _dim) = xs.dims3()?;
let xs_and_res = xs.apply(&self.in_proj)?.chunk(2, D::Minus1)?;
let (xs, res) = (&xs_and_res[0], &xs_and_res[1]);
let xs = xs
.t()?
.apply(&self.conv1d)?
.narrow(D::Minus1, 0, seq_len)?
.t()?;
let xs = candle_nn::ops::silu(&xs)?;
let ys = (self.ssm(&xs)? * candle_nn::ops::silu(res))?;
ys.apply(&self.out_proj)
}
}
// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L143
#[derive(Clone, Debug)]
pub struct ResidualBlock {
mixer: MambaBlock,
norm: RmsNorm,
}
impl ResidualBlock {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let norm = candle_nn::rms_norm(cfg.d_model, 1e-5, vb.pp("norm"))?;
let mixer = MambaBlock::new(cfg, vb.pp("mixer"))?;
Ok(Self { mixer, norm })
}
}
impl Module for ResidualBlock {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
xs.apply(&self.norm)?.apply(&self.mixer)? + xs
}
}
// https://github.com/johnma2006/mamba-minimal/blob/61f01953ca153f8c4a850d7111beecbf4be9cee1/model.py#L56
#[derive(Clone, Debug)]
pub struct Model {
embedding: candle_nn::Embedding,
layers: Vec<ResidualBlock>,
norm_f: RmsNorm,
lm_head: Linear,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let embedding = candle_nn::embedding(cfg.vocab_size(), cfg.d_model, vb.pp("embedding"))?;
let mut layers = Vec::with_capacity(cfg.n_layer);
let vb_l = vb.pp("layers");
for layer_idx in 0..cfg.n_layer {
let layer = ResidualBlock::new(cfg, vb_l.pp(layer_idx))?;
layers.push(layer)
}
let norm_f = candle_nn::rms_norm(cfg.d_model, 1e-5, vb.pp("norm_f"))?;
let lm_head = Linear::from_weights(embedding.embeddings().clone(), None);
Ok(Self {
embedding,
layers,
norm_f,
lm_head,
})
}
}
impl Module for Model {
fn forward(&self, input_ids: &Tensor) -> Result<Tensor> {
let (_b_size, seq_len) = input_ids.dims2()?;
let mut xs = self.embedding.forward(input_ids)?;
for layer in self.layers.iter() {
xs = layer.forward(&xs)?
}
xs.narrow(1, seq_len - 1, 1)?
.apply(&self.norm_f)?
.apply(&self.lm_head)
}
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/vit/main.rs | #[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use clap::Parser;
use candle::{DType, IndexOp, D};
use candle_nn::VarBuilder;
use candle_transformers::models::vit;
#[derive(Parser)]
struct Args {
#[arg(long)]
model: Option<String>,
#[arg(long)]
image: String,
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
}
pub fn main() -> anyhow::Result<()> {
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let image = candle_examples::imagenet::load_image224(args.image)?;
println!("loaded image {image:?}");
let model_file = match args.model {
None => {
let api = hf_hub::api::sync::Api::new()?;
let api = api.model("google/vit-base-patch16-224".into());
api.get("model.safetensors")?
}
Some(model) => model.into(),
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model_file], DType::F32, &device)? };
let model = vit::Model::new(&vit::Config::vit_base_patch16_224(), 1000, vb)?;
println!("model built");
let logits = model.forward(&image.unsqueeze(0)?)?;
let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
.i(0)?
.to_vec1::<f32>()?;
let mut prs = prs.iter().enumerate().collect::<Vec<_>>();
prs.sort_by(|(_, p1), (_, p2)| p2.total_cmp(p1));
for &(category_idx, pr) in prs.iter().take(5) {
println!(
"{:24}: {:.2}%",
candle_examples::imagenet::CLASSES[category_idx],
100. * pr
);
}
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/vit/README.md | # candle-vit
Vision Transformer (ViT) model implementation following the lines of
[vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224)
This uses a classification head trained on the ImageNet dataset and returns the
probabilities for the top-5 classes.
## Running an example
```
$ cargo run --example vit --release -- --image tiger.jpg
loaded image Tensor[dims 3, 224, 224; f32]
model built
tiger, Panthera tigris : 100.00%
tiger cat : 0.00%
jaguar, panther, Panthera onca, Felis onca: 0.00%
leopard, Panthera pardus: 0.00%
lion, king of beasts, Panthera leo: 0.00%
```
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/musicgen/main.rs | #![allow(dead_code)]
// https://huggingface.co/facebook/musicgen-small/tree/main
// https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/models/musicgen/modeling_musicgen.py
// TODO: Add an offline mode.
// TODO: Add a KV cache.
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
mod encodec_model;
mod musicgen_model;
mod nn;
use musicgen_model::{GenConfig, MusicgenForConditionalGeneration};
use anyhow::{Error as E, Result};
use candle::{DType, Tensor};
use candle_nn::VarBuilder;
use clap::Parser;
use hf_hub::{api::sync::Api, Repo, RepoType};
const DTYPE: DType = DType::F32;
#[derive(Parser, Debug)]
#[command(author, version, about, long_about = None)]
struct Args {
/// Run on CPU rather than on GPU.
#[arg(long)]
cpu: bool,
/// The model weight file, in safetensor format.
#[arg(long)]
model: Option<String>,
/// The tokenizer config.
#[arg(long)]
tokenizer: Option<String>,
#[arg(
long,
default_value = "90s rock song with loud guitars and heavy drums"
)]
prompt: String,
}
fn main() -> Result<()> {
use tokenizers::Tokenizer;
let args = Args::parse();
let device = candle_examples::device(args.cpu)?;
let tokenizer = match args.tokenizer {
Some(tokenizer) => std::path::PathBuf::from(tokenizer),
None => Api::new()?
.model("facebook/musicgen-small".to_string())
.get("tokenizer.json")?,
};
let mut tokenizer = Tokenizer::from_file(tokenizer).map_err(E::msg)?;
let tokenizer = tokenizer
.with_padding(None)
.with_truncation(None)
.map_err(E::msg)?;
let model = match args.model {
Some(model) => std::path::PathBuf::from(model),
None => Api::new()?
.repo(Repo::with_revision(
"facebook/musicgen-small".to_string(),
RepoType::Model,
"refs/pr/13".to_string(),
))
.get("model.safetensors")?,
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&[model], DTYPE, &device)? };
let config = GenConfig::small();
let mut model = MusicgenForConditionalGeneration::load(vb, config)?;
let tokens = tokenizer
.encode(args.prompt.as_str(), true)
.map_err(E::msg)?
.get_ids()
.to_vec();
println!("tokens: {tokens:?}");
let tokens = Tensor::new(tokens.as_slice(), &device)?.unsqueeze(0)?;
println!("{tokens:?}");
let embeds = model.text_encoder.forward(&tokens)?;
println!("{embeds}");
Ok(())
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/musicgen/musicgen_model.rs | use crate::encodec_model;
use candle::{DType, Device, Result, Tensor, D};
use candle_nn::{
embedding, layer_norm, linear_no_bias, Activation, Embedding, LayerNorm, Linear, Module,
VarBuilder,
};
use candle_transformers::models::t5;
// https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/models/musicgen/configuration_musicgen.py#L83
#[derive(Debug, Clone, PartialEq)]
pub struct Config {
vocab_size: usize,
max_position_embeddings: usize,
num_hidden_layers: usize,
ffn_dim: usize,
num_attention_heads: usize,
layerdrop: f64,
use_cache: bool,
activation_function: Activation,
hidden_size: usize,
dropout: f64,
attention_dropout: f64,
activation_dropout: f64,
initializer_factor: f64,
scale_embedding: bool,
num_codebooks: usize,
pad_token_id: usize,
bos_token_id: usize,
eos_token_id: Option<usize>,
tie_word_embeddings: bool,
}
impl Default for Config {
fn default() -> Self {
Self {
vocab_size: 2048,
max_position_embeddings: 2048,
num_hidden_layers: 24,
ffn_dim: 4096,
num_attention_heads: 16,
layerdrop: 0.0,
use_cache: true,
activation_function: Activation::Gelu,
hidden_size: 1024,
dropout: 0.1,
attention_dropout: 0.0,
activation_dropout: 0.0,
initializer_factor: 0.02,
scale_embedding: false,
num_codebooks: 4,
pad_token_id: 2048,
bos_token_id: 2048,
eos_token_id: None,
tie_word_embeddings: false,
}
}
}
impl Config {
fn musicgen_small() -> Self {
Self {
vocab_size: 2048,
max_position_embeddings: 2048,
num_hidden_layers: 24,
ffn_dim: 4096,
num_attention_heads: 16,
layerdrop: 0.0,
use_cache: true,
activation_function: Activation::Gelu,
hidden_size: 1024,
dropout: 0.1,
attention_dropout: 0.0,
activation_dropout: 0.0,
initializer_factor: 0.02,
scale_embedding: false,
num_codebooks: 4,
pad_token_id: 2048,
bos_token_id: 2048,
eos_token_id: None,
tie_word_embeddings: false,
}
}
}
fn get_embedding(num_embeddings: usize, embedding_dim: usize) -> Result<Tensor> {
let half_dim = embedding_dim / 2;
let emb = f64::ln(10000.) / (half_dim - 1) as f64;
let xs: Vec<_> = (0..num_embeddings).map(|v| v as f32).collect();
let xs = Tensor::from_vec(xs, (num_embeddings, 1), &Device::Cpu)?;
let ys: Vec<_> = (0..half_dim)
.map(|v| f64::exp(v as f64 * -emb) as f32)
.collect();
let ys = Tensor::from_vec(ys, (1, half_dim), &Device::Cpu)?;
let shape = (num_embeddings, half_dim);
let emb = (xs.broadcast_as(shape)? * ys.broadcast_as(shape)?)?;
let emb =
Tensor::cat(&[&emb.cos()?, &emb.sin()?], 1)?.reshape((num_embeddings, 2 * half_dim))?;
let emb = if embedding_dim % 2 == 1 {
let zeros = Tensor::zeros((num_embeddings, 1), DType::F32, &Device::Cpu)?;
Tensor::cat(&[&emb, &zeros], 1)?
} else {
emb
};
Ok(emb)
}
#[derive(Debug)]
struct MusicgenSinusoidalPositionalEmbedding {
num_positions: usize,
embedding_dim: usize,
weights: Tensor,
}
impl MusicgenSinusoidalPositionalEmbedding {
fn load(_vb: VarBuilder, cfg: &Config) -> Result<Self> {
let num_positions = cfg.max_position_embeddings;
let embedding_dim = cfg.hidden_size;
let weights = get_embedding(num_positions, embedding_dim)?;
Ok(Self {
num_positions,
embedding_dim,
weights,
})
}
fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let (_b_sz, _codebooks, seq_len) = input_ids.dims3()?;
if seq_len > self.weights.dim(0)? {
self.weights = get_embedding(seq_len, self.embedding_dim)?
}
self.weights.narrow(0, 0, seq_len)
}
}
#[derive(Debug)]
struct MusicgenAttention {
scaling: f64,
is_decoder: bool,
num_heads: usize,
head_dim: usize,
k_proj: Linear,
v_proj: Linear,
q_proj: Linear,
out_proj: Linear,
}
impl MusicgenAttention {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let h = cfg.hidden_size;
let num_heads = cfg.num_attention_heads;
let head_dim = h / num_heads;
let k_proj = linear_no_bias(h, h, vb.pp("k_proj"))?;
let v_proj = linear_no_bias(h, h, vb.pp("v_proj"))?;
let q_proj = linear_no_bias(h, h, vb.pp("q_proj"))?;
let out_proj = linear_no_bias(h, h, vb.pp("out_proj"))?;
Ok(Self {
scaling: 1. / (head_dim as f64).sqrt(),
is_decoder: true,
num_heads,
head_dim,
k_proj,
v_proj,
q_proj,
out_proj,
})
}
fn forward(
&mut self,
xs: &Tensor,
kv_states: Option<&Tensor>,
attention_mask: &Tensor,
) -> Result<Tensor> {
let (b_sz, tgt_len, _) = xs.dims3()?;
let query_states = (self.q_proj.forward(xs)? * self.scaling)?;
let kv_states = kv_states.unwrap_or(xs);
let key_states = self.k_proj.forward(kv_states)?;
let value_states = self.v_proj.forward(kv_states)?;
let tgt = (b_sz, tgt_len, self.num_heads, self.head_dim);
let query_states = query_states.reshape(tgt)?.transpose(1, 2)?.contiguous()?;
let key_states = key_states.reshape(tgt)?.transpose(1, 2)?.contiguous()?;
let value_states = value_states.reshape(tgt)?.transpose(1, 2)?.contiguous()?;
let src_len = key_states.dim(1)?;
let attn_weights = query_states.matmul(&key_states.transpose(1, 2)?)?;
let attn_weights = attn_weights
.reshape((b_sz, self.num_heads, tgt_len, src_len))?
.broadcast_add(attention_mask)?;
let attn_weights = candle_nn::ops::softmax(&attn_weights, D::Minus1)?;
// TODO: layer_head_mask?
let attn_output = attn_weights
.matmul(&value_states)?
.reshape((b_sz, self.num_heads, tgt_len, self.head_dim))?
.transpose(1, 2)?
.reshape((b_sz, tgt_len, self.num_heads * self.head_dim))?;
let attn_output = self.out_proj.forward(&attn_output)?;
Ok(attn_output)
}
}
#[derive(Debug)]
struct MusicgenDecoderLayer {
self_attn: MusicgenAttention,
self_attn_layer_norm: LayerNorm,
encoder_attn: MusicgenAttention,
encoder_attn_layer_norm: LayerNorm,
fc1: Linear,
fc2: Linear,
final_layer_norm: LayerNorm,
activation_fn: Activation,
}
impl MusicgenDecoderLayer {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let h = cfg.hidden_size;
let self_attn = MusicgenAttention::load(vb.pp("self_attn"), cfg)?;
let self_attn_layer_norm = layer_norm(h, 1e-5, vb.pp("self_attn_layer_norm"))?;
let encoder_attn = MusicgenAttention::load(vb.pp("encoder_attn"), cfg)?;
let encoder_attn_layer_norm = layer_norm(h, 1e-5, vb.pp("encoder_attn_layer_norm"))?;
let fc1 = linear_no_bias(h, cfg.ffn_dim, vb.pp("fc1"))?;
let fc2 = linear_no_bias(cfg.ffn_dim, h, vb.pp("fc2"))?;
let final_layer_norm = layer_norm(h, 1e-5, vb.pp("final_layer_norm"))?;
Ok(Self {
self_attn,
self_attn_layer_norm,
encoder_attn,
encoder_attn_layer_norm,
fc1,
fc2,
final_layer_norm,
activation_fn: cfg.activation_function,
})
}
fn forward(
&mut self,
xs: &Tensor,
attention_mask: &Tensor,
encoder_hidden_states: Option<&Tensor>,
) -> Result<Tensor> {
let residual = xs.clone();
let xs = self.self_attn_layer_norm.forward(xs)?;
let xs = self.self_attn.forward(&xs, None, attention_mask)?;
let mut xs = (xs + residual)?;
if let Some(encoder_hidden_states) = &encoder_hidden_states {
let residual = xs.clone();
let encoder_attention_mask = attention_mask.clone(); // TODO
xs = self.encoder_attn.forward(
&xs,
Some(encoder_hidden_states),
&encoder_attention_mask,
)?;
xs = (xs + residual)?
}
let residual = xs.clone();
let xs = self.final_layer_norm.forward(&xs)?;
let xs = self.fc1.forward(&xs)?;
let xs = self.activation_fn.forward(&xs)?;
let xs = self.fc2.forward(&xs)?;
let xs = (xs + residual)?;
Ok(xs)
}
}
#[derive(Debug)]
struct MusicgenDecoder {
embed_tokens: Vec<Embedding>,
embed_positions: MusicgenSinusoidalPositionalEmbedding,
layers: Vec<MusicgenDecoderLayer>,
layer_norm: LayerNorm,
embed_scale: f64,
num_codebooks: usize,
d_model: usize,
}
impl MusicgenDecoder {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let h = cfg.hidden_size;
let embed_scale = if cfg.scale_embedding {
(h as f64).sqrt()
} else {
1.
};
let embed_dim = cfg.vocab_size + 1;
let embed_tokens = (0..cfg.num_codebooks)
.map(|i| embedding(embed_dim, h, vb.pp(&format!("embed_tokens.{i}"))))
.collect::<Result<Vec<_>>>()?;
let embed_positions = MusicgenSinusoidalPositionalEmbedding::load(vb.clone(), cfg)?;
let layers = (0..cfg.num_hidden_layers)
.map(|i| MusicgenDecoderLayer::load(vb.pp(&format!("layers.{i}")), cfg))
.collect::<Result<Vec<_>>>()?;
let layer_norm = layer_norm(h, 1e-5, vb.pp("layer_norm"))?;
Ok(Self {
embed_tokens,
embed_positions,
layers,
layer_norm,
embed_scale,
num_codebooks: cfg.num_codebooks,
d_model: cfg.hidden_size,
})
}
fn prepare_decoder_attention_mask(&self, _b_sz: usize, _seq_len: usize) -> Result<Tensor> {
todo!()
}
fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let dev = input_ids.device();
let (b_sz_times_codebooks, seq_len) = input_ids.dims2()?;
let b_sz = b_sz_times_codebooks / self.num_codebooks;
let input = input_ids.reshape((b_sz, self.num_codebooks, seq_len))?;
let mut inputs_embeds = Tensor::zeros((b_sz, seq_len, self.d_model), DType::F32, dev)?;
for (idx, codebook) in self.embed_tokens.iter().enumerate() {
let inp = input.narrow(1, idx, 1)?.squeeze(1)?;
inputs_embeds = (inputs_embeds + codebook.forward(&inp)?)?
}
let inputs_embeds = inputs_embeds;
let positions = self.embed_positions.forward(&input)?.to_device(dev)?;
let mut xs = inputs_embeds.broadcast_add(&positions)?;
let attention_mask = self.prepare_decoder_attention_mask(b_sz, seq_len)?;
for decoder_layer in self.layers.iter_mut() {
xs = decoder_layer.forward(&xs, &attention_mask, None)?;
}
let xs = self.layer_norm.forward(&xs)?;
Ok(xs)
}
}
#[derive(Debug)]
pub struct MusicgenForCausalLM {
decoder: MusicgenDecoder,
lm_heads: Vec<Linear>,
num_codebooks: usize,
vocab_size: usize,
}
impl MusicgenForCausalLM {
pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let h = cfg.hidden_size;
let decoder = MusicgenDecoder::load(vb.pp("model.decoder"), cfg)?;
let lm_heads = (0..cfg.num_codebooks)
.map(|i| linear_no_bias(h, cfg.vocab_size, vb.pp(&format!("lm_heads.{i}"))))
.collect::<Result<Vec<_>>>()?;
Ok(Self {
decoder,
lm_heads,
num_codebooks: cfg.num_codebooks,
vocab_size: cfg.vocab_size,
})
}
pub fn forward(&mut self, input_ids: &Tensor) -> Result<Tensor> {
let (b_sz, seq_len) = input_ids.dims2()?;
let hidden_states = self.decoder.forward(input_ids)?;
let lm_logits = self
.lm_heads
.iter()
.map(|h| h.forward(&hidden_states))
.collect::<Result<Vec<_>>>()?;
let lm_logits = Tensor::stack(&lm_logits, 1)?.reshape((
b_sz * self.num_codebooks,
seq_len,
self.vocab_size,
))?;
Ok(lm_logits)
}
}
#[derive(Debug)]
pub struct MusicgenForConditionalGeneration {
pub text_encoder: t5::T5EncoderModel,
pub audio_encoder: crate::encodec_model::EncodecModel,
pub decoder: MusicgenForCausalLM,
cfg: GenConfig,
}
#[derive(Debug, Clone, PartialEq)]
pub struct GenConfig {
musicgen: Config,
t5: t5::Config,
encodec: crate::encodec_model::Config,
}
impl GenConfig {
pub fn small() -> Self {
Self {
musicgen: Config::musicgen_small(),
t5: t5::Config::musicgen_small(),
encodec: encodec_model::Config::musicgen_small(),
}
}
}
impl MusicgenForConditionalGeneration {
pub fn config(&self) -> &GenConfig {
&self.cfg
}
pub fn load(vb: VarBuilder, cfg: GenConfig) -> Result<Self> {
let text_encoder = t5::T5EncoderModel::load(vb.pp("text_encoder"), &cfg.t5)?;
let audio_encoder =
encodec_model::EncodecModel::load(vb.pp("audio_encoder"), &cfg.encodec)?;
let decoder = MusicgenForCausalLM::load(vb.pp("decoder"), &cfg.musicgen)?;
Ok(Self {
text_encoder,
audio_encoder,
decoder,
cfg,
})
}
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/musicgen/nn.rs | use candle::Result;
use candle_nn::{Conv1d, Conv1dConfig, VarBuilder};
// Applies weight norm for inference by recomputing the weight tensor. This
// does not apply to training.
// https://pytorch.org/docs/stable/generated/torch.nn.utils.weight_norm.html
pub fn conv1d_weight_norm(
in_c: usize,
out_c: usize,
kernel_size: usize,
config: Conv1dConfig,
vb: VarBuilder,
) -> Result<Conv1d> {
let weight_g = vb.get((out_c, 1, 1), "weight_g")?;
let weight_v = vb.get((out_c, in_c, kernel_size), "weight_v")?;
let norm_v = weight_v.sqr()?.sum_keepdim((1, 2))?.sqrt()?;
let weight = weight_v.broadcast_mul(&weight_g)?.broadcast_div(&norm_v)?;
let bias = vb.get(out_c, "bias")?;
Ok(Conv1d::new(weight, Some(bias), config))
}
| 0 |
hf_public_repos/candle/candle-examples/examples | hf_public_repos/candle/candle-examples/examples/musicgen/encodec_model.rs | use crate::nn::conv1d_weight_norm;
use candle::{DType, IndexOp, Module, Result, Tensor};
use candle_nn::{conv1d, Conv1d, Conv1dConfig, VarBuilder};
// Encodec Model
// https://github.com/huggingface/transformers/blob/main/src/transformers/models/encodec/modeling_encodec.py
#[derive(Debug, Clone, PartialEq)]
enum NormType {
WeightNorm,
TimeGroupNorm,
None,
}
#[derive(Debug, Clone, PartialEq)]
pub struct Config {
target_bandwidths: Vec<f64>,
sampling_rate: usize,
audio_channels: usize,
normalize: bool,
chunk_length_s: Option<usize>,
overlap: Option<usize>,
hidden_size: usize,
num_filters: usize,
num_residual_layers: usize,
upsampling_ratios: Vec<usize>,
norm_type: NormType,
kernel_size: usize,
last_kernel_size: usize,
residual_kernel_size: usize,
dilation_growth_rate: usize,
use_causal_conv: bool,
pad_mode: &'static str,
compress: usize,
num_lstm_layers: usize,
trim_right_ratio: f64,
codebook_size: usize,
codebook_dim: Option<usize>,
use_conv_shortcut: bool,
}
impl Default for Config {
fn default() -> Self {
Self {
target_bandwidths: vec![1.5, 3.0, 6.0, 12.0, 24.0],
sampling_rate: 24_000,
audio_channels: 1,
normalize: false,
chunk_length_s: None,
overlap: None,
hidden_size: 128,
num_filters: 32,
num_residual_layers: 1,
upsampling_ratios: vec![8, 5, 4, 2],
norm_type: NormType::WeightNorm,
kernel_size: 7,
last_kernel_size: 7,
residual_kernel_size: 3,
dilation_growth_rate: 2,
use_causal_conv: true,
pad_mode: "reflect",
compress: 2,
num_lstm_layers: 2,
trim_right_ratio: 1.0,
codebook_size: 1024,
codebook_dim: None,
use_conv_shortcut: true,
}
}
}
impl Config {
// https://huggingface.co/facebook/musicgen-small/blob/495da4ad086b3416a27c6187f9239f9fd96f3962/config.json#L6
pub fn musicgen_small() -> Self {
Self {
audio_channels: 1,
chunk_length_s: None,
codebook_dim: Some(128),
codebook_size: 2048,
compress: 2,
dilation_growth_rate: 2,
hidden_size: 128,
kernel_size: 7,
last_kernel_size: 7,
norm_type: NormType::WeightNorm,
normalize: false,
num_filters: 64,
num_lstm_layers: 2,
num_residual_layers: 1,
overlap: None,
pad_mode: "reflect",
residual_kernel_size: 3,
sampling_rate: 32_000,
target_bandwidths: vec![2.2],
trim_right_ratio: 1.0,
upsampling_ratios: vec![8, 5, 4, 4],
use_causal_conv: false,
use_conv_shortcut: false,
}
}
fn codebook_dim(&self) -> usize {
self.codebook_dim.unwrap_or(self.codebook_size)
}
fn frame_rate(&self) -> usize {
let hop_length: usize = self.upsampling_ratios.iter().product();
(self.sampling_rate + hop_length - 1) / hop_length
}
fn num_quantizers(&self) -> usize {
let num = 1000f64
* self
.target_bandwidths
.last()
.expect("empty target_bandwidths");
(num as usize) / (self.frame_rate() * 10)
}
}
// https://github.com/huggingface/transformers/blob/abaca9f9432a84cfaa95531de4c72334f38a42f2/src/transformers/models/encodec/modeling_encodec.py#L340
#[derive(Debug)]
struct EncodecEuclideanCodebook {
inited: Tensor,
cluster_size: Tensor,
embed: Tensor,
embed_avg: Tensor,
}
impl EncodecEuclideanCodebook {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let inited = vb.get(1, "inited")?;
let cluster_size = vb.get(cfg.codebook_size, "cluster_size")?;
let e_shape = (cfg.codebook_size, cfg.codebook_dim());
let embed = vb.get(e_shape, "embed")?;
let embed_avg = vb.get(e_shape, "embed_avg")?;
Ok(Self {
inited,
cluster_size,
embed,
embed_avg,
})
}
fn decode(&self, embed_ind: &Tensor) -> Result<Tensor> {
let quantize = self.embed.embedding(embed_ind)?;
Ok(quantize)
}
}
#[derive(Debug)]
struct EncodecVectorQuantization {
codebook: EncodecEuclideanCodebook,
}
impl EncodecVectorQuantization {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let codebook = EncodecEuclideanCodebook::load(vb.pp("codebook"), cfg)?;
Ok(Self { codebook })
}
fn decode(&self, embed_ind: &Tensor) -> Result<Tensor> {
let quantize = self.codebook.decode(embed_ind)?;
let quantize = quantize.transpose(1, 2)?;
Ok(quantize)
}
}
#[derive(Debug)]
struct EncodecResidualVectorQuantizer {
layers: Vec<EncodecVectorQuantization>,
}
impl EncodecResidualVectorQuantizer {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let vb = &vb.pp("layers");
let layers = (0..cfg.num_quantizers())
.map(|i| EncodecVectorQuantization::load(vb.pp(&i.to_string()), cfg))
.collect::<Result<Vec<_>>>()?;
Ok(Self { layers })
}
fn decode(&self, codes: &Tensor) -> Result<Tensor> {
let mut quantized_out = Tensor::zeros((), DType::F32, codes.device())?;
if codes.dim(0)? != self.layers.len() {
candle::bail!(
"codes shape {:?} does not match the number of quantization layers {}",
codes.shape(),
self.layers.len()
)
}
for (i, layer) in self.layers.iter().enumerate() {
let quantized = layer.decode(&codes.i(i)?)?;
quantized_out = quantized.broadcast_add(&quantized_out)?;
}
Ok(quantized_out)
}
}
// https://github.com/huggingface/transformers/blob/abaca9f9432a84cfaa95531de4c72334f38a42f2/src/transformers/models/encodec/modeling_encodec.py#L226
#[derive(Debug)]
struct EncodecLSTM {
layers: Vec<candle_nn::LSTM>,
}
impl EncodecLSTM {
fn load(dim: usize, vb: VarBuilder, cfg: &Config) -> Result<Self> {
let vb = &vb.pp("lstm");
let mut layers = vec![];
for layer_idx in 0..cfg.num_lstm_layers {
let config = candle_nn::LSTMConfig {
layer_idx,
..Default::default()
};
let lstm = candle_nn::lstm(dim, dim, config, vb.clone())?;
layers.push(lstm)
}
Ok(Self { layers })
}
}
impl Module for EncodecLSTM {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
use candle_nn::RNN;
let mut xs = xs.clone();
for layer in self.layers.iter() {
let states = layer.seq(&xs)?;
xs = layer.states_to_tensor(&states)?;
}
Ok(xs)
}
}
#[derive(Debug)]
struct EncodecConvTranspose1d {
weight_g: Tensor,
weight_v: Tensor,
bias: Tensor,
}
impl EncodecConvTranspose1d {
fn load(
in_c: usize,
out_c: usize,
k: usize,
_stride: usize,
vb: VarBuilder,
_cfg: &Config,
) -> Result<Self> {
let vb = &vb.pp("conv");
let weight_g = vb.get((in_c, 1, 1), "weight_g")?;
let weight_v = vb.get((in_c, out_c, k), "weight_v")?;
let bias = vb.get(out_c, "bias")?;
Ok(Self {
weight_g,
weight_v,
bias,
})
}
}
impl Module for EncodecConvTranspose1d {
fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
todo!()
}
}
#[derive(Debug)]
struct EncodecConv1d {
causal: bool,
conv: Conv1d,
norm: Option<candle_nn::GroupNorm>,
}
impl EncodecConv1d {
fn load(
in_c: usize,
out_c: usize,
kernel_size: usize,
stride: usize,
vb: VarBuilder,
cfg: &Config,
) -> Result<Self> {
let conv = match cfg.norm_type {
NormType::WeightNorm => conv1d_weight_norm(
in_c,
out_c,
kernel_size,
Conv1dConfig {
padding: 0,
stride,
groups: 1,
dilation: 1,
},
vb.pp("conv"),
)?,
NormType::None | NormType::TimeGroupNorm => conv1d(
in_c,
out_c,
kernel_size,
Conv1dConfig {
padding: 0,
stride,
groups: 1,
dilation: 1,
},
vb.pp("conv"),
)?,
};
let norm = match cfg.norm_type {
NormType::None | NormType::WeightNorm => None,
NormType::TimeGroupNorm => {
let gn = candle_nn::group_norm(1, out_c, 1e-5, vb.pp("norm"))?;
Some(gn)
}
};
Ok(Self {
causal: cfg.use_causal_conv,
conv,
norm,
})
}
}
impl Module for EncodecConv1d {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
// TODO: padding, depending on causal.
let xs = self.conv.forward(xs)?;
match &self.norm {
None => Ok(xs),
Some(norm) => xs.apply(norm),
}
}
}
#[derive(Debug)]
struct EncodecResnetBlock {
block_conv1: EncodecConv1d,
block_conv2: EncodecConv1d,
shortcut: Option<EncodecConv1d>,
}
impl EncodecResnetBlock {
fn load(dim: usize, dilations: &[usize], vb: VarBuilder, cfg: &Config) -> Result<Self> {
let h = dim / cfg.compress;
let mut layer = Layer::new(vb.pp("block"));
if dilations.len() != 2 {
candle::bail!("expected dilations of size 2")
}
// TODO: Apply dilations!
layer.inc();
let block_conv1 =
EncodecConv1d::load(dim, h, cfg.residual_kernel_size, 1, layer.next(), cfg)?;
layer.inc();
let block_conv2 = EncodecConv1d::load(h, dim, 1, 1, layer.next(), cfg)?;
let shortcut = if cfg.use_conv_shortcut {
let conv = EncodecConv1d::load(dim, dim, 1, 1, vb.pp("shortcut"), cfg)?;
Some(conv)
} else {
None
};
Ok(Self {
block_conv1,
block_conv2,
shortcut,
})
}
}
impl Module for EncodecResnetBlock {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let residual = xs.clone();
let xs = xs.elu(1.)?;
let xs = self.block_conv1.forward(&xs)?;
let xs = xs.elu(1.)?;
let xs = self.block_conv2.forward(&xs)?;
let xs = match &self.shortcut {
None => (xs + residual)?,
Some(shortcut) => xs.add(&shortcut.forward(&residual)?)?,
};
Ok(xs)
}
}
struct Layer<'a> {
vb: VarBuilder<'a>,
cnt: usize,
}
impl<'a> Layer<'a> {
fn new(vb: VarBuilder<'a>) -> Self {
Self { vb, cnt: 0 }
}
fn inc(&mut self) {
self.cnt += 1;
}
fn next(&mut self) -> VarBuilder {
let vb = self.vb.pp(&self.cnt.to_string());
self.cnt += 1;
vb
}
}
#[derive(Debug)]
struct EncodecEncoder {
init_conv: EncodecConv1d,
sampling_layers: Vec<(Vec<EncodecResnetBlock>, EncodecConv1d)>,
final_lstm: EncodecLSTM,
final_conv: EncodecConv1d,
}
impl EncodecEncoder {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let mut layer = Layer::new(vb.pp("layers"));
let init_conv = EncodecConv1d::load(
cfg.audio_channels,
cfg.num_filters,
cfg.kernel_size,
1,
layer.next(),
cfg,
)?;
let mut sampling_layers = vec![];
let mut scaling = 1;
for &ratio in cfg.upsampling_ratios.iter().rev() {
let current_scale = scaling * cfg.num_filters;
let mut resnets = vec![];
for j in 0..(cfg.num_residual_layers as u32) {
let resnet = EncodecResnetBlock::load(
current_scale,
&[cfg.dilation_growth_rate.pow(j), 1],
layer.next(),
cfg,
)?;
resnets.push(resnet)
}
layer.inc(); // ELU
let conv1d = EncodecConv1d::load(
current_scale,
current_scale * 2,
ratio * 2,
ratio,
layer.next(),
cfg,
)?;
sampling_layers.push((resnets, conv1d));
scaling *= 2;
}
let final_lstm = EncodecLSTM::load(cfg.num_filters * scaling, layer.next(), cfg)?;
layer.inc(); // ELU
let final_conv = EncodecConv1d::load(
cfg.num_filters * scaling,
cfg.hidden_size,
cfg.last_kernel_size,
1,
layer.next(),
cfg,
)?;
Ok(Self {
init_conv,
sampling_layers,
final_conv,
final_lstm,
})
}
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let mut xs = xs.apply(&self.init_conv)?;
for (resnets, conv) in self.sampling_layers.iter() {
for resnet in resnets.iter() {
xs = xs.apply(resnet)?;
}
xs = xs.elu(1.0)?.apply(conv)?;
}
xs.apply(&self.final_lstm)?
.elu(1.0)?
.apply(&self.final_conv)
}
}
#[derive(Debug)]
struct EncodecDecoder {
init_conv: EncodecConv1d,
init_lstm: EncodecLSTM,
sampling_layers: Vec<(EncodecConvTranspose1d, Vec<EncodecResnetBlock>)>,
final_conv: EncodecConv1d,
}
impl EncodecDecoder {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let mut layer = Layer::new(vb.pp("layers"));
let mut scaling = usize::pow(2, cfg.upsampling_ratios.len() as u32);
let init_conv = EncodecConv1d::load(
cfg.hidden_size,
cfg.num_filters * scaling,
cfg.last_kernel_size,
1,
layer.next(),
cfg,
)?;
let init_lstm = EncodecLSTM::load(cfg.num_filters * scaling, layer.next(), cfg)?;
let mut sampling_layers = vec![];
for &ratio in cfg.upsampling_ratios.iter() {
let current_scale = scaling * cfg.num_filters;
layer.inc(); // ELU
let conv1d = EncodecConvTranspose1d::load(
current_scale,
current_scale / 2,
ratio * 2,
ratio,
layer.next(),
cfg,
)?;
let mut resnets = vec![];
for j in 0..(cfg.num_residual_layers as u32) {
let resnet = EncodecResnetBlock::load(
current_scale / 2,
&[cfg.dilation_growth_rate.pow(j), 1],
layer.next(),
cfg,
)?;
resnets.push(resnet)
}
sampling_layers.push((conv1d, resnets));
scaling /= 2;
}
layer.inc(); // ELU
let final_conv = EncodecConv1d::load(
cfg.num_filters,
cfg.audio_channels,
cfg.last_kernel_size,
1,
layer.next(),
cfg,
)?;
Ok(Self {
init_conv,
init_lstm,
sampling_layers,
final_conv,
})
}
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let mut xs = xs.apply(&self.init_conv)?.apply(&self.init_lstm)?;
for (conv, resnets) in self.sampling_layers.iter() {
xs = xs.elu(1.)?.apply(conv)?;
for resnet in resnets.iter() {
xs = xs.apply(resnet)?
}
}
xs.elu(1.)?.apply(&self.final_conv)
}
}
#[derive(Debug)]
pub struct EncodecModel {
encoder: EncodecEncoder,
decoder: EncodecDecoder,
quantizer: EncodecResidualVectorQuantizer,
}
impl EncodecModel {
pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let encoder = EncodecEncoder::load(vb.pp("encoder"), cfg)?;
let decoder = EncodecDecoder::load(vb.pp("decoder"), cfg)?;
let quantizer = EncodecResidualVectorQuantizer::load(vb.pp("quantizer"), cfg)?;
Ok(Self {
encoder,
decoder,
quantizer,
})
}
pub fn forward(&self, _xs: &Tensor) -> Result<Tensor> {
todo!()
}
}
| 0 |
hf_public_repos/candle/candle-examples | hf_public_repos/candle/candle-examples/src/token_output_stream.rs | use candle::Result;
/// This is a wrapper around a tokenizer to ensure that tokens can be returned to the user in a
/// streaming way rather than having to wait for the full decoding.
pub struct TokenOutputStream {
tokenizer: tokenizers::Tokenizer,
tokens: Vec<u32>,
prev_index: usize,
current_index: usize,
}
impl TokenOutputStream {
pub fn new(tokenizer: tokenizers::Tokenizer) -> Self {
Self {
tokenizer,
tokens: Vec::new(),
prev_index: 0,
current_index: 0,
}
}
pub fn into_inner(self) -> tokenizers::Tokenizer {
self.tokenizer
}
fn decode(&self, tokens: &[u32]) -> Result<String> {
match self.tokenizer.decode(tokens, true) {
Ok(str) => Ok(str),
Err(err) => candle::bail!("cannot decode: {err}"),
}
}
// https://github.com/huggingface/text-generation-inference/blob/5ba53d44a18983a4de32d122f4cb46f4a17d9ef6/server/text_generation_server/models/model.py#L68
pub fn next_token(&mut self, token: u32) -> Result<Option<String>> {
let prev_text = if self.tokens.is_empty() {
String::new()
} else {
let tokens = &self.tokens[self.prev_index..self.current_index];
self.decode(tokens)?
};
self.tokens.push(token);
let text = self.decode(&self.tokens[self.prev_index..])?;
if text.len() > prev_text.len() && text.chars().last().unwrap().is_ascii() {
let text = text.split_at(prev_text.len());
self.prev_index = self.current_index;
self.current_index = self.tokens.len();
Ok(Some(text.1.to_string()))
} else {
Ok(None)
}
}
pub fn decode_rest(&self) -> Result<Option<String>> {
let prev_text = if self.tokens.is_empty() {
String::new()
} else {
let tokens = &self.tokens[self.prev_index..self.current_index];
self.decode(tokens)?
};
let text = self.decode(&self.tokens[self.prev_index..])?;
if text.len() > prev_text.len() {
let text = text.split_at(prev_text.len());
Ok(Some(text.1.to_string()))
} else {
Ok(None)
}
}
pub fn decode_all(&self) -> Result<String> {
self.decode(&self.tokens)
}
pub fn get_token(&self, token_s: &str) -> Option<u32> {
self.tokenizer.get_vocab(true).get(token_s).copied()
}
pub fn tokenizer(&self) -> &tokenizers::Tokenizer {
&self.tokenizer
}
pub fn clear(&mut self) {
self.tokens.clear();
self.prev_index = 0;
self.current_index = 0;
}
}
| 0 |
hf_public_repos/candle/candle-examples | hf_public_repos/candle/candle-examples/src/lib.rs | pub mod coco_classes;
pub mod imagenet;
pub mod token_output_stream;
use candle::utils::{cuda_is_available, metal_is_available};
use candle::{Device, Result, Tensor};
pub fn device(cpu: bool) -> Result<Device> {
if cpu {
Ok(Device::Cpu)
} else if cuda_is_available() {
Ok(Device::new_cuda(0)?)
} else if metal_is_available() {
Ok(Device::new_metal(0)?)
} else {
#[cfg(all(target_os = "macos", target_arch = "aarch64"))]
{
println!(
"Running on CPU, to run on GPU(metal), build this example with `--features metal`"
);
}
#[cfg(not(all(target_os = "macos", target_arch = "aarch64")))]
{
println!("Running on CPU, to run on GPU, build this example with `--features cuda`");
}
Ok(Device::Cpu)
}
}
pub fn load_image<P: AsRef<std::path::Path>>(
p: P,
resize_longest: Option<usize>,
) -> Result<(Tensor, usize, usize)> {
let img = image::io::Reader::open(p)?
.decode()
.map_err(candle::Error::wrap)?;
let (initial_h, initial_w) = (img.height() as usize, img.width() as usize);
let img = match resize_longest {
None => img,
Some(resize_longest) => {
let (height, width) = (img.height(), img.width());
let resize_longest = resize_longest as u32;
let (height, width) = if height < width {
let h = (resize_longest * height) / width;
(h, resize_longest)
} else {
let w = (resize_longest * width) / height;
(resize_longest, w)
};
img.resize_exact(width, height, image::imageops::FilterType::CatmullRom)
}
};
let (height, width) = (img.height() as usize, img.width() as usize);
let img = img.to_rgb8();
let data = img.into_raw();
let data = Tensor::from_vec(data, (height, width, 3), &Device::Cpu)?.permute((2, 0, 1))?;
Ok((data, initial_h, initial_w))
}
pub fn load_image_and_resize<P: AsRef<std::path::Path>>(
p: P,
width: usize,
height: usize,
) -> Result<Tensor> {
let img = image::io::Reader::open(p)?
.decode()
.map_err(candle::Error::wrap)?
.resize_to_fill(
width as u32,
height as u32,
image::imageops::FilterType::Triangle,
);
let img = img.to_rgb8();
let data = img.into_raw();
Tensor::from_vec(data, (width, height, 3), &Device::Cpu)?.permute((2, 0, 1))
}
/// Saves an image to disk using the image crate, this expects an input with shape
/// (c, height, width).
pub fn save_image<P: AsRef<std::path::Path>>(img: &Tensor, p: P) -> Result<()> {
let p = p.as_ref();
let (channel, height, width) = img.dims3()?;
if channel != 3 {
candle::bail!("save_image expects an input of shape (3, height, width)")
}
let img = img.permute((1, 2, 0))?.flatten_all()?;
let pixels = img.to_vec1::<u8>()?;
let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) {
Some(image) => image,
None => candle::bail!("error saving image {p:?}"),
};
image.save(p).map_err(candle::Error::wrap)?;
Ok(())
}
pub fn save_image_resize<P: AsRef<std::path::Path>>(
img: &Tensor,
p: P,
h: usize,
w: usize,
) -> Result<()> {
let p = p.as_ref();
let (channel, height, width) = img.dims3()?;
if channel != 3 {
candle::bail!("save_image expects an input of shape (3, height, width)")
}
let img = img.permute((1, 2, 0))?.flatten_all()?;
let pixels = img.to_vec1::<u8>()?;
let image: image::ImageBuffer<image::Rgb<u8>, Vec<u8>> =
match image::ImageBuffer::from_raw(width as u32, height as u32, pixels) {
Some(image) => image,
None => candle::bail!("error saving image {p:?}"),
};
let image = image::DynamicImage::from(image);
let image = image.resize_to_fill(w as u32, h as u32, image::imageops::FilterType::CatmullRom);
image.save(p).map_err(candle::Error::wrap)?;
Ok(())
}
/// Loads the safetensors files for a model from the hub based on a json index file.
pub fn hub_load_safetensors(
repo: &hf_hub::api::sync::ApiRepo,
json_file: &str,
) -> Result<Vec<std::path::PathBuf>> {
let json_file = repo.get(json_file).map_err(candle::Error::wrap)?;
let json_file = std::fs::File::open(json_file)?;
let json: serde_json::Value =
serde_json::from_reader(&json_file).map_err(candle::Error::wrap)?;
let weight_map = match json.get("weight_map") {
None => candle::bail!("no weight map in {json_file:?}"),
Some(serde_json::Value::Object(map)) => map,
Some(_) => candle::bail!("weight map in {json_file:?} is not a map"),
};
let mut safetensors_files = std::collections::HashSet::new();
for value in weight_map.values() {
if let Some(file) = value.as_str() {
safetensors_files.insert(file.to_string());
}
}
let safetensors_files = safetensors_files
.iter()
.map(|v| repo.get(v).map_err(candle::Error::wrap))
.collect::<Result<Vec<_>>>()?;
Ok(safetensors_files)
}
| 0 |
hf_public_repos/candle/candle-examples | hf_public_repos/candle/candle-examples/src/coco_classes.rs | pub const NAMES: [&str; 80] = [
"person",
"bicycle",
"car",
"motorbike",
"aeroplane",
"bus",
"train",
"truck",
"boat",
"traffic light",
"fire hydrant",
"stop sign",
"parking meter",
"bench",
"bird",
"cat",
"dog",
"horse",
"sheep",
"cow",
"elephant",
"bear",
"zebra",
"giraffe",
"backpack",
"umbrella",
"handbag",
"tie",
"suitcase",
"frisbee",
"skis",
"snowboard",
"sports ball",
"kite",
"baseball bat",
"baseball glove",
"skateboard",
"surfboard",
"tennis racket",
"bottle",
"wine glass",
"cup",
"fork",
"knife",
"spoon",
"bowl",
"banana",
"apple",
"sandwich",
"orange",
"broccoli",
"carrot",
"hot dog",
"pizza",
"donut",
"cake",
"chair",
"sofa",
"pottedplant",
"bed",
"diningtable",
"toilet",
"tvmonitor",
"laptop",
"mouse",
"remote",
"keyboard",
"cell phone",
"microwave",
"oven",
"toaster",
"sink",
"refrigerator",
"book",
"clock",
"vase",
"scissors",
"teddy bear",
"hair drier",
"toothbrush",
];
| 0 |
hf_public_repos/candle/candle-examples | hf_public_repos/candle/candle-examples/src/imagenet.rs | use candle::{Device, Result, Tensor};
/// Loads an image from disk using the image crate, this returns a tensor with shape
/// (3, 224, 224). imagenet normalization is applied.
pub fn load_image224<P: AsRef<std::path::Path>>(p: P) -> Result<Tensor> {
let img = image::io::Reader::open(p)?
.decode()
.map_err(candle::Error::wrap)?
.resize_to_fill(224, 224, image::imageops::FilterType::Triangle);
let img = img.to_rgb8();
let data = img.into_raw();
let data = Tensor::from_vec(data, (224, 224, 3), &Device::Cpu)?.permute((2, 0, 1))?;
let mean = Tensor::new(&[0.485f32, 0.456, 0.406], &Device::Cpu)?.reshape((3, 1, 1))?;
let std = Tensor::new(&[0.229f32, 0.224, 0.225], &Device::Cpu)?.reshape((3, 1, 1))?;
(data.to_dtype(candle::DType::F32)? / 255.)?
.broadcast_sub(&mean)?
.broadcast_div(&std)
}
pub const CLASS_COUNT: i64 = 1000;
pub const CLASSES: [&str; 1000] = [
"tench, Tinca tinca",
"goldfish, Carassius auratus",
"great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias",
"tiger shark, Galeocerdo cuvieri",
"hammerhead, hammerhead shark",
"electric ray, crampfish, numbfish, torpedo",
"stingray",
"cock",
"hen",
"ostrich, Struthio camelus",
"brambling, Fringilla montifringilla",
"goldfinch, Carduelis carduelis",
"house finch, linnet, Carpodacus mexicanus",
"junco, snowbird",
"indigo bunting, indigo finch, indigo bird, Passerina cyanea",
"robin, American robin, Turdus migratorius",
"bulbul",
"jay",
"magpie",
"chickadee",
"water ouzel, dipper",
"kite",
"bald eagle, American eagle, Haliaeetus leucocephalus",
"vulture",
"great grey owl, great gray owl, Strix nebulosa",
"European fire salamander, Salamandra salamandra",
"common newt, Triturus vulgaris",
"eft",
"spotted salamander, Ambystoma maculatum",
"axolotl, mud puppy, Ambystoma mexicanum",
"bullfrog, Rana catesbeiana",
"tree frog, tree-frog",
"tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui",
"loggerhead, loggerhead turtle, Caretta caretta",
"leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea",
"mud turtle",
"terrapin",
"box turtle, box tortoise",
"banded gecko",
"common iguana, iguana, Iguana iguana",
"American chameleon, anole, Anolis carolinensis",
"whiptail, whiptail lizard",
"agama",
"frilled lizard, Chlamydosaurus kingi",
"alligator lizard",
"Gila monster, Heloderma suspectum",
"green lizard, Lacerta viridis",
"African chameleon, Chamaeleo chamaeleon",
"Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis",
"African crocodile, Nile crocodile, Crocodylus niloticus",
"American alligator, Alligator mississipiensis",
"triceratops",
"thunder snake, worm snake, Carphophis amoenus",
"ringneck snake, ring-necked snake, ring snake",
"hognose snake, puff adder, sand viper",
"green snake, grass snake",
"king snake, kingsnake",
"garter snake, grass snake",
"water snake",
"vine snake",
"night snake, Hypsiglena torquata",
"boa constrictor, Constrictor constrictor",
"rock python, rock snake, Python sebae",
"Indian cobra, Naja naja",
"green mamba",
"sea snake",
"horned viper, cerastes, sand viper, horned asp, Cerastes cornutus",
"diamondback, diamondback rattlesnake, Crotalus adamanteus",
"sidewinder, horned rattlesnake, Crotalus cerastes",
"trilobite",
"harvestman, daddy longlegs, Phalangium opilio",
"scorpion",
"black and gold garden spider, Argiope aurantia",
"barn spider, Araneus cavaticus",
"garden spider, Aranea diademata",
"black widow, Latrodectus mactans",
"tarantula",
"wolf spider, hunting spider",
"tick",
"centipede",
"black grouse",
"ptarmigan",
"ruffed grouse, partridge, Bonasa umbellus",
"prairie chicken, prairie grouse, prairie fowl",
"peacock",
"quail",
"partridge",
"African grey, African gray, Psittacus erithacus",
"macaw",
"sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita",
"lorikeet",
"coucal",
"bee eater",
"hornbill",
"hummingbird",
"jacamar",
"toucan",
"drake",
"red-breasted merganser, Mergus serrator",
"goose",
"black swan, Cygnus atratus",
"tusker",
"echidna, spiny anteater, anteater",
"platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus",
"wallaby, brush kangaroo",
"koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus",
"wombat",
"jellyfish",
"sea anemone, anemone",
"brain coral",
"flatworm, platyhelminth",
"nematode, nematode worm, roundworm",
"conch",
"snail",
"slug",
"sea slug, nudibranch",
"chiton, coat-of-mail shell, sea cradle, polyplacophore",
"chambered nautilus, pearly nautilus, nautilus",
"Dungeness crab, Cancer magister",
"rock crab, Cancer irroratus",
"fiddler crab",
"king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica",
"American lobster, Northern lobster, Maine lobster, Homarus americanus",
"spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish",
"crayfish, crawfish, crawdad, crawdaddy",
"hermit crab",
"isopod",
"white stork, Ciconia ciconia",
"black stork, Ciconia nigra",
"spoonbill",
"flamingo",
"little blue heron, Egretta caerulea",
"American egret, great white heron, Egretta albus",
"bittern",
"crane",
"limpkin, Aramus pictus",
"European gallinule, Porphyrio porphyrio",
"American coot, marsh hen, mud hen, water hen, Fulica americana",
"bustard",
"ruddy turnstone, Arenaria interpres",
"red-backed sandpiper, dunlin, Erolia alpina",
"redshank, Tringa totanus",
"dowitcher",
"oystercatcher, oyster catcher",
"pelican",
"king penguin, Aptenodytes patagonica",
"albatross, mollymawk",
"grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus",
"killer whale, killer, orca, grampus, sea wolf, Orcinus orca",
"dugong, Dugong dugon",
"sea lion",
"Chihuahua",
"Japanese spaniel",
"Maltese dog, Maltese terrier, Maltese",
"Pekinese, Pekingese, Peke",
"Shih-Tzu",
"Blenheim spaniel",
"papillon",
"toy terrier",
"Rhodesian ridgeback",
"Afghan hound, Afghan",
"basset, basset hound",
"beagle",
"bloodhound, sleuthhound",
"bluetick",
"black-and-tan coonhound",
"Walker hound, Walker foxhound",
"English foxhound",
"redbone",
"borzoi, Russian wolfhound",
"Irish wolfhound",
"Italian greyhound",
"whippet",
"Ibizan hound, Ibizan Podenco",
"Norwegian elkhound, elkhound",
"otterhound, otter hound",
"Saluki, gazelle hound",
"Scottish deerhound, deerhound",
"Weimaraner",
"Staffordshire bullterrier, Staffordshire bull terrier",
"American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier",
"Bedlington terrier",
"Border terrier",
"Kerry blue terrier",
"Irish terrier",
"Norfolk terrier",
"Norwich terrier",
"Yorkshire terrier",
"wire-haired fox terrier",
"Lakeland terrier",
"Sealyham terrier, Sealyham",
"Airedale, Airedale terrier",
"cairn, cairn terrier",
"Australian terrier",
"Dandie Dinmont, Dandie Dinmont terrier",
"Boston bull, Boston terrier",
"miniature schnauzer",
"giant schnauzer",
"standard schnauzer",
"Scotch terrier, Scottish terrier, Scottie",
"Tibetan terrier, chrysanthemum dog",
"silky terrier, Sydney silky",
"soft-coated wheaten terrier",
"West Highland white terrier",
"Lhasa, Lhasa apso",
"flat-coated retriever",
"curly-coated retriever",
"golden retriever",
"Labrador retriever",
"Chesapeake Bay retriever",
"German short-haired pointer",
"vizsla, Hungarian pointer",
"English setter",
"Irish setter, red setter",
"Gordon setter",
"Brittany spaniel",
"clumber, clumber spaniel",
"English springer, English springer spaniel",
"Welsh springer spaniel",
"cocker spaniel, English cocker spaniel, cocker",
"Sussex spaniel",
"Irish water spaniel",
"kuvasz",
"schipperke",
"groenendael",
"malinois",
"briard",
"kelpie",
"komondor",
"Old English sheepdog, bobtail",
"Shetland sheepdog, Shetland sheep dog, Shetland",
"collie",
"Border collie",
"Bouvier des Flandres, Bouviers des Flandres",
"Rottweiler",
"German shepherd, German shepherd dog, German police dog, alsatian",
"Doberman, Doberman pinscher",
"miniature pinscher",
"Greater Swiss Mountain dog",
"Bernese mountain dog",
"Appenzeller",
"EntleBucher",
"boxer",
"bull mastiff",
"Tibetan mastiff",
"French bulldog",
"Great Dane",
"Saint Bernard, St Bernard",
"Eskimo dog, husky",
"malamute, malemute, Alaskan malamute",
"Siberian husky",
"dalmatian, coach dog, carriage dog",
"affenpinscher, monkey pinscher, monkey dog",
"basenji",
"pug, pug-dog",
"Leonberg",
"Newfoundland, Newfoundland dog",
"Great Pyrenees",
"Samoyed, Samoyede",
"Pomeranian",
"chow, chow chow",
"keeshond",
"Brabancon griffon",
"Pembroke, Pembroke Welsh corgi",
"Cardigan, Cardigan Welsh corgi",
"toy poodle",
"miniature poodle",
"standard poodle",
"Mexican hairless",
"timber wolf, grey wolf, gray wolf, Canis lupus",
"white wolf, Arctic wolf, Canis lupus tundrarum",
"red wolf, maned wolf, Canis rufus, Canis niger",
"coyote, prairie wolf, brush wolf, Canis latrans",
"dingo, warrigal, warragal, Canis dingo",
"dhole, Cuon alpinus",
"African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus",
"hyena, hyaena",
"red fox, Vulpes vulpes",
"kit fox, Vulpes macrotis",
"Arctic fox, white fox, Alopex lagopus",
"grey fox, gray fox, Urocyon cinereoargenteus",
"tabby, tabby cat",
"tiger cat",
"Persian cat",
"Siamese cat, Siamese",
"Egyptian cat",
"cougar, puma, catamount, mountain lion, painter, panther, Felis concolor",
"lynx, catamount",
"leopard, Panthera pardus",
"snow leopard, ounce, Panthera uncia",
"jaguar, panther, Panthera onca, Felis onca",
"lion, king of beasts, Panthera leo",
"tiger, Panthera tigris",
"cheetah, chetah, Acinonyx jubatus",
"brown bear, bruin, Ursus arctos",
"American black bear, black bear, Ursus americanus, Euarctos americanus",
"ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus",
"sloth bear, Melursus ursinus, Ursus ursinus",
"mongoose",
"meerkat, mierkat",
"tiger beetle",
"ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle",
"ground beetle, carabid beetle",
"long-horned beetle, longicorn, longicorn beetle",
"leaf beetle, chrysomelid",
"dung beetle",
"rhinoceros beetle",
"weevil",
"fly",
"bee",
"ant, emmet, pismire",
"grasshopper, hopper",
"cricket",
"walking stick, walkingstick, stick insect",
"cockroach, roach",
"mantis, mantid",
"cicada, cicala",
"leafhopper",
"lacewing, lacewing fly",
"dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
"damselfly",
"admiral",
"ringlet, ringlet butterfly",
"monarch, monarch butterfly, milkweed butterfly, Danaus plexippus",
"cabbage butterfly",
"sulphur butterfly, sulfur butterfly",
"lycaenid, lycaenid butterfly",
"starfish, sea star",
"sea urchin",
"sea cucumber, holothurian",
"wood rabbit, cottontail, cottontail rabbit",
"hare",
"Angora, Angora rabbit",
"hamster",
"porcupine, hedgehog",
"fox squirrel, eastern fox squirrel, Sciurus niger",
"marmot",
"beaver",
"guinea pig, Cavia cobaya",
"sorrel",
"zebra",
"hog, pig, grunter, squealer, Sus scrofa",
"wild boar, boar, Sus scrofa",
"warthog",
"hippopotamus, hippo, river horse, Hippopotamus amphibius",
"ox",
"water buffalo, water ox, Asiatic buffalo, Bubalus bubalis",
"bison",
"ram, tup",
"bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis",
"ibex, Capra ibex",
"hartebeest",
"impala, Aepyceros melampus",
"gazelle",
"Arabian camel, dromedary, Camelus dromedarius",
"llama",
"weasel",
"mink",
"polecat, fitch, foulmart, foumart, Mustela putorius",
"black-footed ferret, ferret, Mustela nigripes",
"otter",
"skunk, polecat, wood pussy",
"badger",
"armadillo",
"three-toed sloth, ai, Bradypus tridactylus",
"orangutan, orang, orangutang, Pongo pygmaeus",
"gorilla, Gorilla gorilla",
"chimpanzee, chimp, Pan troglodytes",
"gibbon, Hylobates lar",
"siamang, Hylobates syndactylus, Symphalangus syndactylus",
"guenon, guenon monkey",
"patas, hussar monkey, Erythrocebus patas",
"baboon",
"macaque",
"langur",
"colobus, colobus monkey",
"proboscis monkey, Nasalis larvatus",
"marmoset",
"capuchin, ringtail, Cebus capucinus",
"howler monkey, howler",
"titi, titi monkey",
"spider monkey, Ateles geoffroyi",
"squirrel monkey, Saimiri sciureus",
"Madagascar cat, ring-tailed lemur, Lemur catta",
"indri, indris, Indri indri, Indri brevicaudatus",
"Indian elephant, Elephas maximus",
"African elephant, Loxodonta africana",
"lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens",
"giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca",
"barracouta, snoek",
"eel",
"coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch",
"rock beauty, Holocanthus tricolor",
"anemone fish",
"sturgeon",
"gar, garfish, garpike, billfish, Lepisosteus osseus",
"lionfish",
"puffer, pufferfish, blowfish, globefish",
"abacus",
"abaya",
"academic gown, academic robe, judge's robe",
"accordion, piano accordion, squeeze box",
"acoustic guitar",
"aircraft carrier, carrier, flattop, attack aircraft carrier",
"airliner",
"airship, dirigible",
"altar",
"ambulance",
"amphibian, amphibious vehicle",
"analog clock",
"apiary, bee house",
"apron",
"ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin",
"assault rifle, assault gun",
"backpack, back pack, knapsack, packsack, rucksack, haversack",
"bakery, bakeshop, bakehouse",
"balance beam, beam",
"balloon",
"ballpoint, ballpoint pen, ballpen, Biro",
"Band Aid",
"banjo",
"bannister, banister, balustrade, balusters, handrail",
"barbell",
"barber chair",
"barbershop",
"barn",
"barometer",
"barrel, cask",
"barrow, garden cart, lawn cart, wheelbarrow",
"baseball",
"basketball",
"bassinet",
"bassoon",
"bathing cap, swimming cap",
"bath towel",
"bathtub, bathing tub, bath, tub",
"beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon",
"beacon, lighthouse, beacon light, pharos",
"beaker",
"bearskin, busby, shako",
"beer bottle",
"beer glass",
"bell cote, bell cot",
"bib",
"bicycle-built-for-two, tandem bicycle, tandem",
"bikini, two-piece",
"binder, ring-binder",
"binoculars, field glasses, opera glasses",
"birdhouse",
"boathouse",
"bobsled, bobsleigh, bob",
"bolo tie, bolo, bola tie, bola",
"bonnet, poke bonnet",
"bookcase",
"bookshop, bookstore, bookstall",
"bottlecap",
"bow",
"bow tie, bow-tie, bowtie",
"brass, memorial tablet, plaque",
"brassiere, bra, bandeau",
"breakwater, groin, groyne, mole, bulwark, seawall, jetty",
"breastplate, aegis, egis",
"broom",
"bucket, pail",
"buckle",
"bulletproof vest",
"bullet train, bullet",
"butcher shop, meat market",
"cab, hack, taxi, taxicab",
"caldron, cauldron",
"candle, taper, wax light",
"cannon",
"canoe",
"can opener, tin opener",
"cardigan",
"car mirror",
"carousel, carrousel, merry-go-round, roundabout, whirligig",
"carpenter's kit, tool kit",
"carton",
"car wheel",
"cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM",
"cassette",
"cassette player",
"castle",
"catamaran",
"CD player",
"cello, violoncello",
"cellular telephone, cellular phone, cellphone, cell, mobile phone",
"chain",
"chainlink fence",
"chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour",
"chain saw, chainsaw",
"chest",
"chiffonier, commode",
"chime, bell, gong",
"china cabinet, china closet",
"Christmas stocking",
"church, church building",
"cinema, movie theater, movie theatre, movie house, picture palace",
"cleaver, meat cleaver, chopper",
"cliff dwelling",
"cloak",
"clog, geta, patten, sabot",
"cocktail shaker",
"coffee mug",
"coffeepot",
"coil, spiral, volute, whorl, helix",
"combination lock",
"computer keyboard, keypad",
"confectionery, confectionary, candy store",
"container ship, containership, container vessel",
"convertible",
"corkscrew, bottle screw",
"cornet, horn, trumpet, trump",
"cowboy boot",
"cowboy hat, ten-gallon hat",
"cradle",
"crane",
"crash helmet",
"crate",
"crib, cot",
"Crock Pot",
"croquet ball",
"crutch",
"cuirass",
"dam, dike, dyke",
"desk",
"desktop computer",
"dial telephone, dial phone",
"diaper, nappy, napkin",
"digital clock",
"digital watch",
"dining table, board",
"dishrag, dishcloth",
"dishwasher, dish washer, dishwashing machine",
"disk brake, disc brake",
"dock, dockage, docking facility",
"dogsled, dog sled, dog sleigh",
"dome",
"doormat, welcome mat",
"drilling platform, offshore rig",
"drum, membranophone, tympan",
"drumstick",
"dumbbell",
"Dutch oven",
"electric fan, blower",
"electric guitar",
"electric locomotive",
"entertainment center",
"envelope",
"espresso maker",
"face powder",
"feather boa, boa",
"file, file cabinet, filing cabinet",
"fireboat",
"fire engine, fire truck",
"fire screen, fireguard",
"flagpole, flagstaff",
"flute, transverse flute",
"folding chair",
"football helmet",
"forklift",
"fountain",
"fountain pen",
"four-poster",
"freight car",
"French horn, horn",
"frying pan, frypan, skillet",
"fur coat",
"garbage truck, dustcart",
"gasmask, respirator, gas helmet",
"gas pump, gasoline pump, petrol pump, island dispenser",
"goblet",
"go-kart",
"golf ball",
"golfcart, golf cart",
"gondola",
"gong, tam-tam",
"gown",
"grand piano, grand",
"greenhouse, nursery, glasshouse",
"grille, radiator grille",
"grocery store, grocery, food market, market",
"guillotine",
"hair slide",
"hair spray",
"half track",
"hammer",
"hamper",
"hand blower, blow dryer, blow drier, hair dryer, hair drier",
"hand-held computer, hand-held microcomputer",
"handkerchief, hankie, hanky, hankey",
"hard disc, hard disk, fixed disk",
"harmonica, mouth organ, harp, mouth harp",
"harp",
"harvester, reaper",
"hatchet",
"holster",
"home theater, home theatre",
"honeycomb",
"hook, claw",
"hoopskirt, crinoline",
"horizontal bar, high bar",
"horse cart, horse-cart",
"hourglass",
"iPod",
"iron, smoothing iron",
"jack-o'-lantern",
"jean, blue jean, denim",
"jeep, landrover",
"jersey, T-shirt, tee shirt",
"jigsaw puzzle",
"jinrikisha, ricksha, rickshaw",
"joystick",
"kimono",
"knee pad",
"knot",
"lab coat, laboratory coat",
"ladle",
"lampshade, lamp shade",
"laptop, laptop computer",
"lawn mower, mower",
"lens cap, lens cover",
"letter opener, paper knife, paperknife",
"library",
"lifeboat",
"lighter, light, igniter, ignitor",
"limousine, limo",
"liner, ocean liner",
"lipstick, lip rouge",
"Loafer",
"lotion",
"loudspeaker, speaker, speaker unit, loudspeaker system, speaker system",
"loupe, jeweler's loupe",
"lumbermill, sawmill",
"magnetic compass",
"mailbag, postbag",
"mailbox, letter box",
"maillot",
"maillot, tank suit",
"manhole cover",
"maraca",
"marimba, xylophone",
"mask",
"matchstick",
"maypole",
"maze, labyrinth",
"measuring cup",
"medicine chest, medicine cabinet",
"megalith, megalithic structure",
"microphone, mike",
"microwave, microwave oven",
"military uniform",
"milk can",
"minibus",
"miniskirt, mini",
"minivan",
"missile",
"mitten",
"mixing bowl",
"mobile home, manufactured home",
"Model T",
"modem",
"monastery",
"monitor",
"moped",
"mortar",
"mortarboard",
"mosque",
"mosquito net",
"motor scooter, scooter",
"mountain bike, all-terrain bike, off-roader",
"mountain tent",
"mouse, computer mouse",
"mousetrap",
"moving van",
"muzzle",
"nail",
"neck brace",
"necklace",
"nipple",
"notebook, notebook computer",
"obelisk",
"oboe, hautboy, hautbois",
"ocarina, sweet potato",
"odometer, hodometer, mileometer, milometer",
"oil filter",
"organ, pipe organ",
"oscilloscope, scope, cathode-ray oscilloscope, CRO",
"overskirt",
"oxcart",
"oxygen mask",
"packet",
"paddle, boat paddle",
"paddlewheel, paddle wheel",
"padlock",
"paintbrush",
"pajama, pyjama, pj's, jammies",
"palace",
"panpipe, pandean pipe, syrinx",
"paper towel",
"parachute, chute",
"parallel bars, bars",
"park bench",
"parking meter",
"passenger car, coach, carriage",
"patio, terrace",
"pay-phone, pay-station",
"pedestal, plinth, footstall",
"pencil box, pencil case",
"pencil sharpener",
"perfume, essence",
"Petri dish",
"photocopier",
"pick, plectrum, plectron",
"pickelhaube",
"picket fence, paling",
"pickup, pickup truck",
"pier",
"piggy bank, penny bank",
"pill bottle",
"pillow",
"ping-pong ball",
"pinwheel",
"pirate, pirate ship",
"pitcher, ewer",
"plane, carpenter's plane, woodworking plane",
"planetarium",
"plastic bag",
"plate rack",
"plow, plough",
"plunger, plumber's helper",
"Polaroid camera, Polaroid Land camera",
"pole",
"police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria",
"poncho",
"pool table, billiard table, snooker table",
"pop bottle, soda bottle",
"pot, flowerpot",
"potter's wheel",
"power drill",
"prayer rug, prayer mat",
"printer",
"prison, prison house",
"projectile, missile",
"projector",
"puck, hockey puck",
"punching bag, punch bag, punching ball, punchball",
"purse",
"quill, quill pen",
"quilt, comforter, comfort, puff",
"racer, race car, racing car",
"racket, racquet",
"radiator",
"radio, wireless",
"radio telescope, radio reflector",
"rain barrel",
"recreational vehicle, RV, R.V.",
"reel",
"reflex camera",
"refrigerator, icebox",
"remote control, remote",
"restaurant, eating house, eating place, eatery",
"revolver, six-gun, six-shooter",
"rifle",
"rocking chair, rocker",
"rotisserie",
"rubber eraser, rubber, pencil eraser",
"rugby ball",
"rule, ruler",
"running shoe",
"safe",
"safety pin",
"saltshaker, salt shaker",
"sandal",
"sarong",
"sax, saxophone",
"scabbard",
"scale, weighing machine",
"school bus",
"schooner",
"scoreboard",
"screen, CRT screen",
"screw",
"screwdriver",
"seat belt, seatbelt",
"sewing machine",
"shield, buckler",
"shoe shop, shoe-shop, shoe store",
"shoji",
"shopping basket",
"shopping cart",
"shovel",
"shower cap",
"shower curtain",
"ski",
"ski mask",
"sleeping bag",
"slide rule, slipstick",
"sliding door",
"slot, one-armed bandit",
"snorkel",
"snowmobile",
"snowplow, snowplough",
"soap dispenser",
"soccer ball",
"sock",
"solar dish, solar collector, solar furnace",
"sombrero",
"soup bowl",
"space bar",
"space heater",
"space shuttle",
"spatula",
"speedboat",
"spider web, spider's web",
"spindle",
"sports car, sport car",
"spotlight, spot",
"stage",
"steam locomotive",
"steel arch bridge",
"steel drum",
"stethoscope",
"stole",
"stone wall",
"stopwatch, stop watch",
"stove",
"strainer",
"streetcar, tram, tramcar, trolley, trolley car",
"stretcher",
"studio couch, day bed",
"stupa, tope",
"submarine, pigboat, sub, U-boat",
"suit, suit of clothes",
"sundial",
"sunglass",
"sunglasses, dark glasses, shades",
"sunscreen, sunblock, sun blocker",
"suspension bridge",
"swab, swob, mop",
"sweatshirt",
"swimming trunks, bathing trunks",
"swing",
"switch, electric switch, electrical switch",
"syringe",
"table lamp",
"tank, army tank, armored combat vehicle, armoured combat vehicle",
"tape player",
"teapot",
"teddy, teddy bear",
"television, television system",
"tennis ball",
"thatch, thatched roof",
"theater curtain, theatre curtain",
"thimble",
"thresher, thrasher, threshing machine",
"throne",
"tile roof",
"toaster",
"tobacco shop, tobacconist shop, tobacconist",
"toilet seat",
"torch",
"totem pole",
"tow truck, tow car, wrecker",
"toyshop",
"tractor",
"trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi",
"tray",
"trench coat",
"tricycle, trike, velocipede",
"trimaran",
"tripod",
"triumphal arch",
"trolleybus, trolley coach, trackless trolley",
"trombone",
"tub, vat",
"turnstile",
"typewriter keyboard",
"umbrella",
"unicycle, monocycle",
"upright, upright piano",
"vacuum, vacuum cleaner",
"vase",
"vault",
"velvet",
"vending machine",
"vestment",
"viaduct",
"violin, fiddle",
"volleyball",
"waffle iron",
"wall clock",
"wallet, billfold, notecase, pocketbook",
"wardrobe, closet, press",
"warplane, military plane",
"washbasin, handbasin, washbowl, lavabo, wash-hand basin",
"washer, automatic washer, washing machine",
"water bottle",
"water jug",
"water tower",
"whiskey jug",
"whistle",
"wig",
"window screen",
"window shade",
"Windsor tie",
"wine bottle",
"wing",
"wok",
"wooden spoon",
"wool, woolen, woollen",
"worm fence, snake fence, snake-rail fence, Virginia fence",
"wreck",
"yawl",
"yurt",
"web site, website, internet site, site",
"comic book",
"crossword puzzle, crossword",
"street sign",
"traffic light, traffic signal, stoplight",
"book jacket, dust cover, dust jacket, dust wrapper",
"menu",
"plate",
"guacamole",
"consomme",
"hot pot, hotpot",
"trifle",
"ice cream, icecream",
"ice lolly, lolly, lollipop, popsicle",
"French loaf",
"bagel, beigel",
"pretzel",
"cheeseburger",
"hotdog, hot dog, red hot",
"mashed potato",
"head cabbage",
"broccoli",
"cauliflower",
"zucchini, courgette",
"spaghetti squash",
"acorn squash",
"butternut squash",
"cucumber, cuke",
"artichoke, globe artichoke",
"bell pepper",
"cardoon",
"mushroom",
"Granny Smith",
"strawberry",
"orange",
"lemon",
"fig",
"pineapple, ananas",
"banana",
"jackfruit, jak, jack",
"custard apple",
"pomegranate",
"hay",
"carbonara",
"chocolate sauce, chocolate syrup",
"dough",
"meat loaf, meatloaf",
"pizza, pizza pie",
"potpie",
"burrito",
"red wine",
"espresso",
"cup",
"eggnog",
"alp",
"bubble",
"cliff, drop, drop-off",
"coral reef",
"geyser",
"lakeside, lakeshore",
"promontory, headland, head, foreland",
"sandbar, sand bar",
"seashore, coast, seacoast, sea-coast",
"valley, vale",
"volcano",
"ballplayer, baseball player",
"groom, bridegroom",
"scuba diver",
"rapeseed",
"daisy",
"yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
"corn",
"acorn",
"hip, rose hip, rosehip",
"buckeye, horse chestnut, conker",
"coral fungus",
"agaric",
"gyromitra",
"stinkhorn, carrion fungus",
"earthstar",
"hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa",
"bolete",
"ear, spike, capitulum",
"toilet tissue, toilet paper, bathroom tissue",
];
| 0 |
hf_public_repos/candle | hf_public_repos/candle/.cargo/config.toml | [build]
rustflags = ["-C", "target-cpu=native"]
[target.wasm32-unknown-unknown]
rustflags = ["-C", "target-feature=+simd128"]
[target.x86_64-apple-darwin]
rustflags = ["-C", "target-feature=-avx,-avx2"] | 0 |
hf_public_repos/candle | hf_public_repos/candle/.vscode/settings.json | {
"[python]": {
"editor.defaultFormatter": "ms-python.black-formatter"
},
"python.formatting.provider": "none",
"python.testing.pytestArgs": [
"candle-pyo3"
],
"python.testing.unittestEnabled": false,
"python.testing.pytestEnabled": true
} | 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-book/book.toml | [book]
authors = ["Nicolas Patry"]
language = "en"
multilingual = false
src = "src"
title = "Candle Documentation"
| 0 |
hf_public_repos/candle | hf_public_repos/candle/candle-book/Cargo.toml | [package]
name = "candle-book"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[dependencies]
accelerate-src = { workspace = true, optional = true }
candle = { workspace = true }
candle-datasets = { workspace = true }
candle-nn = { workspace = true }
candle-transformers = { workspace = true }
candle-flash-attn = { workspace = true, optional = true }
safetensors = { workspace = true }
serde = { workspace = true }
serde_json = { workspace = true }
num-traits = { workspace = true }
intel-mkl-src = { workspace = true, optional = true }
cudarc = { workspace = true, optional = true }
half = { workspace = true, optional = true }
image = { workspace = true, optional = true }
anyhow = { workspace = true }
tokio = "1.29.1"
[dev-dependencies]
byteorder = { workspace = true }
hf-hub = { workspace = true, features=["tokio"]}
clap = { workspace = true }
memmap2 = { workspace = true }
rand = { workspace = true }
tokenizers = { workspace = true, features = ["onig"] }
tracing = { workspace = true }
tracing-chrome = { workspace = true }
tracing-subscriber = { workspace = true }
wav = { workspace = true }
# Necessary to disambiguate with tokio in wasm examples which are 1.28.1
parquet = { workspace = true }
image = { workspace = true }
[build-dependencies]
anyhow = { workspace = true }
[features]
default = []
| 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/lib.rs | #[cfg(test)]
pub mod simplified;
#[cfg(test)]
mod tests {
use anyhow::Result;
use candle::{DType, Device, Tensor};
use parquet::file::reader::SerializedFileReader;
// NOTE: Waiting on https://github.com/rust-lang/mdBook/pull/1856
#[rustfmt::skip]
#[tokio::test]
async fn book_hub_1() {
// ANCHOR: book_hub_1
use candle::Device;
use hf_hub::api::tokio::Api;
let api = Api::new().unwrap();
let repo = api.model("bert-base-uncased".to_string());
let weights_filename = repo.get("model.safetensors").await.unwrap();
let weights = candle::safetensors::load(weights_filename, &Device::Cpu).unwrap();
// ANCHOR_END: book_hub_1
assert_eq!(weights.len(), 206);
}
#[rustfmt::skip]
#[test]
fn book_hub_2() {
{
// ANCHOR: book_hub_2
use candle::Device;
use hf_hub::api::sync::Api;
use memmap2::Mmap;
use std::fs;
let api = Api::new().unwrap();
let repo = api.model("bert-base-uncased".to_string());
let weights_filename = repo.get("model.safetensors").unwrap();
let file = fs::File::open(weights_filename).unwrap();
let mmap = unsafe { Mmap::map(&file).unwrap() };
let weights = candle::safetensors::load_buffer(&mmap[..], &Device::Cpu).unwrap();
// ANCHOR_END: book_hub_2
assert_eq!(weights.len(), 206);
}
// #[rustfmt::skip]
// #[test]
// fn book_hub_3() {
{
// ANCHOR: book_hub_3
use candle::{DType, Device, Tensor};
use hf_hub::api::sync::Api;
use memmap2::Mmap;
use safetensors::slice::IndexOp;
use safetensors::SafeTensors;
use std::fs;
let api = Api::new().unwrap();
let repo = api.model("bert-base-uncased".to_string());
let weights_filename = repo.get("model.safetensors").unwrap();
let file = fs::File::open(weights_filename).unwrap();
let mmap = unsafe { Mmap::map(&file).unwrap() };
// Use safetensors directly
let tensors = SafeTensors::deserialize(&mmap[..]).unwrap();
let view = tensors
.tensor("bert.encoder.layer.0.attention.self.query.weight")
.unwrap();
// We're going to load shard with rank 1, within a world_size of 4
// We're going to split along dimension 0 doing VIEW[start..stop, :]
let rank = 1;
let world_size = 4;
let dim = 0;
let dtype = view.dtype();
let mut tp_shape = view.shape().to_vec();
let size = tp_shape[0];
if size % world_size != 0 {
panic!("The dimension is not divisble by `world_size`");
}
let block_size = size / world_size;
let start = rank * block_size;
let stop = (rank + 1) * block_size;
// Everything is expressed in tensor dimension
// bytes offsets is handled automatically for safetensors.
let iterator = view.slice(start..stop).unwrap();
tp_shape[dim] = block_size;
// Convert safetensors Dtype to candle DType
let dtype: DType = dtype.try_into().unwrap();
// TODO: Implement from_buffer_iterator so we can skip the extra CPU alloc.
let raw: Vec<u8> = iterator.into_iter().flatten().cloned().collect();
let tp_tensor = Tensor::from_raw_buffer(&raw, dtype, &tp_shape, &Device::Cpu).unwrap();
// ANCHOR_END: book_hub_3
assert_eq!(view.shape(), &[768, 768]);
assert_eq!(tp_tensor.dims(), &[192, 768]);
}
}
#[rustfmt::skip]
#[test]
fn book_training_1() -> Result<()>{
// ANCHOR: book_training_1
use hf_hub::{api::sync::Api, Repo, RepoType};
let dataset_id = "mnist".to_string();
let api = Api::new()?;
let repo = Repo::with_revision(
dataset_id,
RepoType::Dataset,
"refs/convert/parquet".to_string(),
);
let repo = api.repo(repo);
let test_parquet_filename = repo.get("mnist/test/0000.parquet")?;
let train_parquet_filename = repo.get("mnist/train/0000.parquet")?;
let test_parquet = SerializedFileReader::new(std::fs::File::open(test_parquet_filename)?)?;
let train_parquet = SerializedFileReader::new(std::fs::File::open(train_parquet_filename)?)?;
// ANCHOR_END: book_training_1
// Ignore unused
let _train = train_parquet;
// ANCHOR: book_training_2
for row in test_parquet {
for (idx, (name, field)) in row?.get_column_iter().enumerate() {
println!("Column id {idx}, name {name}, value {field}");
}
}
// ANCHOR_END: book_training_2
let test_parquet_filename = repo.get("mnist/test/0000.parquet")?;
let train_parquet_filename = repo.get("mnist/train/0000.parquet")?;
let test_parquet = SerializedFileReader::new(std::fs::File::open(test_parquet_filename)?)?;
let train_parquet = SerializedFileReader::new(std::fs::File::open(train_parquet_filename)?)?;
// ANCHOR: book_training_3
let test_samples = 10_000;
let mut test_buffer_images: Vec<u8> = Vec::with_capacity(test_samples * 784);
let mut test_buffer_labels: Vec<u8> = Vec::with_capacity(test_samples);
for row in test_parquet{
for (_name, field) in row?.get_column_iter() {
if let parquet::record::Field::Group(subrow) = field {
for (_name, field) in subrow.get_column_iter() {
if let parquet::record::Field::Bytes(value) = field {
let image = image::load_from_memory(value.data()).unwrap();
test_buffer_images.extend(image.to_luma8().as_raw());
}
}
}else if let parquet::record::Field::Long(label) = field {
test_buffer_labels.push(*label as u8);
}
}
}
let test_images = (Tensor::from_vec(test_buffer_images, (test_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;
let test_labels = Tensor::from_vec(test_buffer_labels, (test_samples, ), &Device::Cpu)?;
let train_samples = 60_000;
let mut train_buffer_images: Vec<u8> = Vec::with_capacity(train_samples * 784);
let mut train_buffer_labels: Vec<u8> = Vec::with_capacity(train_samples);
for row in train_parquet{
for (_name, field) in row?.get_column_iter() {
if let parquet::record::Field::Group(subrow) = field {
for (_name, field) in subrow.get_column_iter() {
if let parquet::record::Field::Bytes(value) = field {
let image = image::load_from_memory(value.data()).unwrap();
train_buffer_images.extend(image.to_luma8().as_raw());
}
}
}else if let parquet::record::Field::Long(label) = field {
train_buffer_labels.push(*label as u8);
}
}
}
let train_images = (Tensor::from_vec(train_buffer_images, (train_samples, 784), &Device::Cpu)?.to_dtype(DType::F32)? / 255.)?;
let train_labels = Tensor::from_vec(train_buffer_labels, (train_samples, ), &Device::Cpu)?;
let mnist = candle_datasets::vision::Dataset {
train_images,
train_labels,
test_images,
test_labels,
labels: 10,
};
// ANCHOR_END: book_training_3
assert_eq!(mnist.test_images.dims(), &[10_000, 784]);
assert_eq!(mnist.test_labels.dims(), &[10_000]);
assert_eq!(mnist.train_images.dims(), &[60_000, 784]);
assert_eq!(mnist.train_labels.dims(), &[60_000]);
Ok(())
}
}
| 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/README.md | # Introduction
{{#include ../../README.md:features}}
This book will introduce step by step how to use `candle`.
| 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/chapter_1.md | # Chapter 1
| 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/simplified.rs | //! #A simplified example in Rust of training a neural network and then using it based on the Candle Framework by Hugging Face.
//! Author: Evgeny Igumnov 2023 [email protected]
//! This program implements a neural network to predict the winner of the second round of elections based on the results of the first round.
//!
//! ##Basic moments:
//!
//! A multilayer perceptron with two hidden layers is used. The first hidden layer has 4 neurons, the second has 2 neurons.
//! The input is a vector of 2 numbers - the percentage of votes for the first and second candidates in the first stage.
//! The output is the number 0 or 1, where 1 means that the first candidate will win in the second stage, 0 means that he will lose.
//! For training, samples with real data on the results of the first and second stages of different elections are used.
//! The model is trained by backpropagation using gradient descent and the cross-entropy loss function.
//! Model parameters (weights of neurons) are initialized randomly, then optimized during training.
//! After training, the model is tested on a deferred sample to evaluate the accuracy.
//! If the accuracy on the test set is below 100%, the model is considered underfit and the learning process is repeated.
//! Thus, this neural network learns to find hidden relationships between the results of the first and second rounds of voting in order to make predictions for new data.
#[rustfmt::skip]
mod tests {
use candle::{DType, Result, Tensor, D, Device};
use candle_nn::{loss, ops, Linear, Module, VarBuilder, VarMap, Optimizer};
// ANCHOR: book_training_simplified1
const VOTE_DIM: usize = 2;
const RESULTS: usize = 1;
const EPOCHS: usize = 10;
const LAYER1_OUT_SIZE: usize = 4;
const LAYER2_OUT_SIZE: usize = 2;
const LEARNING_RATE: f64 = 0.05;
#[derive(Clone)]
pub struct Dataset {
pub train_votes: Tensor,
pub train_results: Tensor,
pub test_votes: Tensor,
pub test_results: Tensor,
}
struct MultiLevelPerceptron {
ln1: Linear,
ln2: Linear,
ln3: Linear,
}
impl MultiLevelPerceptron {
fn new(vs: VarBuilder) -> Result<Self> {
let ln1 = candle_nn::linear(VOTE_DIM, LAYER1_OUT_SIZE, vs.pp("ln1"))?;
let ln2 = candle_nn::linear(LAYER1_OUT_SIZE, LAYER2_OUT_SIZE, vs.pp("ln2"))?;
let ln3 = candle_nn::linear(LAYER2_OUT_SIZE, RESULTS + 1, vs.pp("ln3"))?;
Ok(Self { ln1, ln2, ln3 })
}
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let xs = self.ln1.forward(xs)?;
let xs = xs.relu()?;
let xs = self.ln2.forward(&xs)?;
let xs = xs.relu()?;
self.ln3.forward(&xs)
}
}
// ANCHOR_END: book_training_simplified1
// ANCHOR: book_training_simplified3
#[tokio::test]
async fn simplified() -> anyhow::Result<()> {
let dev = Device::cuda_if_available(0)?;
let train_votes_vec: Vec<u32> = vec![
15, 10,
10, 15,
5, 12,
30, 20,
16, 12,
13, 25,
6, 14,
31, 21,
];
let train_votes_tensor = Tensor::from_vec(train_votes_vec.clone(), (train_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
let train_results_vec: Vec<u32> = vec![
1,
0,
0,
1,
1,
0,
0,
1,
];
let train_results_tensor = Tensor::from_vec(train_results_vec, train_votes_vec.len() / VOTE_DIM, &dev)?;
let test_votes_vec: Vec<u32> = vec![
13, 9,
8, 14,
3, 10,
];
let test_votes_tensor = Tensor::from_vec(test_votes_vec.clone(), (test_votes_vec.len() / VOTE_DIM, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
let test_results_vec: Vec<u32> = vec![
1,
0,
0,
];
let test_results_tensor = Tensor::from_vec(test_results_vec.clone(), test_results_vec.len(), &dev)?;
let m = Dataset {
train_votes: train_votes_tensor,
train_results: train_results_tensor,
test_votes: test_votes_tensor,
test_results: test_results_tensor,
};
let trained_model: MultiLevelPerceptron;
loop {
println!("Trying to train neural network.");
match train(m.clone(), &dev) {
Ok(model) => {
trained_model = model;
break;
},
Err(e) => {
println!("Error: {}", e);
continue;
}
}
}
let real_world_votes: Vec<u32> = vec![
13, 22,
];
let tensor_test_votes = Tensor::from_vec(real_world_votes.clone(), (1, VOTE_DIM), &dev)?.to_dtype(DType::F32)?;
let final_result = trained_model.forward(&tensor_test_votes)?;
let result = final_result
.argmax(D::Minus1)?
.to_dtype(DType::F32)?
.get(0).map(|x| x.to_scalar::<f32>())??;
println!("real_life_votes: {:?}", real_world_votes);
println!("neural_network_prediction_result: {:?}", result);
Ok(())
}
// ANCHOR_END: book_training_simplified3
// ANCHOR: book_training_simplified2
fn train(m: Dataset, dev: &Device) -> anyhow::Result<MultiLevelPerceptron> {
let train_results = m.train_results.to_device(dev)?;
let train_votes = m.train_votes.to_device(dev)?;
let varmap = VarMap::new();
let vs = VarBuilder::from_varmap(&varmap, DType::F32, dev);
let model = MultiLevelPerceptron::new(vs.clone())?;
let mut sgd = candle_nn::SGD::new(varmap.all_vars(), LEARNING_RATE)?;
let test_votes = m.test_votes.to_device(dev)?;
let test_results = m.test_results.to_device(dev)?;
let mut final_accuracy: f32 = 0.0;
for epoch in 1..EPOCHS + 1 {
let logits = model.forward(&train_votes)?;
let log_sm = ops::log_softmax(&logits, D::Minus1)?;
let loss = loss::nll(&log_sm, &train_results)?;
sgd.backward_step(&loss)?;
let test_logits = model.forward(&test_votes)?;
let sum_ok = test_logits
.argmax(D::Minus1)?
.eq(&test_results)?
.to_dtype(DType::F32)?
.sum_all()?
.to_scalar::<f32>()?;
let test_accuracy = sum_ok / test_results.dims1()? as f32;
final_accuracy = 100. * test_accuracy;
println!("Epoch: {epoch:3} Train loss: {:8.5} Test accuracy: {:5.2}%",
loss.to_scalar::<f32>()?,
final_accuracy
);
if final_accuracy == 100.0 {
break;
}
}
if final_accuracy < 100.0 {
Err(anyhow::Error::msg("The model is not trained well enough."))
} else {
Ok(model)
}
}
// ANCHOR_END: book_training_simplified2
}
| 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/SUMMARY.md | # Summary
[Introduction](README.md)
# User Guide
- [Installation](guide/installation.md)
- [Hello World - MNIST](guide/hello_world.md)
- [PyTorch cheatsheet](guide/cheatsheet.md)
# Reference Guide
- [Running a model](inference/inference.md)
- [Using the hub](inference/hub.md)
- [Error management](error_manage.md)
- [Training](training/training.md)
- [Simplified](training/simplified.md)
- [MNIST](training/mnist.md)
- [Fine-tuning]()
- [Serialization]()
- [Advanced Cuda usage]()
- [Writing a custom kernel]()
- [Porting a custom kernel]()
- [Using MKL]()
- [Creating apps]()
- [Creating a WASM app]()
- [Creating a REST api webserver]()
- [Creating a desktop Tauri app]()
| 0 |
hf_public_repos/candle/candle-book | hf_public_repos/candle/candle-book/src/error_manage.md | # Error management
You might have seen in the code base a lot of `.unwrap()` or `?`.
If you're unfamiliar with Rust check out the [Rust book](https://doc.rust-lang.org/book/ch09-02-recoverable-errors-with-result.html)
for more information.
What's important to know though, is that if you want to know *where* a particular operation failed
You can simply use `RUST_BACKTRACE=1` to get the location of where the model actually failed.
Let's see on failing code:
```rust,ignore
let x = Tensor::zeros((1, 784), DType::F32, &device)?;
let y = Tensor::zeros((1, 784), DType::F32, &device)?;
let z = x.matmul(&y)?;
```
Will print at runtime:
```bash
Error: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: "matmul" }
```
After adding `RUST_BACKTRACE=1`:
```bash
Error: WithBacktrace { inner: ShapeMismatchBinaryOp { lhs: [1, 784], rhs: [1, 784], op: "matmul" }, backtrace: Backtrace [{ fn: "candle::error::Error::bt", file: "/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/error.rs", line: 200 }, { fn: "candle::tensor::Tensor::matmul", file: "/home/nicolas/.cargo/git/checkouts/candle-5bb8ef7e0626d693/f291065/candle-core/src/tensor.rs", line: 816 }, { fn: "myapp::main", file: "./src/main.rs", line: 29 }, { fn: "core::ops::function::FnOnce::call_once", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs", line: 250 }, { fn: "std::sys_common::backtrace::__rust_begin_short_backtrace", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/sys_common/backtrace.rs", line: 135 }, { fn: "std::rt::lang_start::{{closure}}", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 166 }, { fn: "core::ops::function::impls::<impl core::ops::function::FnOnce<A> for &F>::call_once", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/core/src/ops/function.rs", line: 284 }, { fn: "std::panicking::try::do_call", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 500 }, { fn: "std::panicking::try", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 464 }, { fn: "std::panic::catch_unwind", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs", line: 142 }, { fn: "std::rt::lang_start_internal::{{closure}}", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 148 }, { fn: "std::panicking::try::do_call", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 500 }, { fn: "std::panicking::try", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panicking.rs", line: 464 }, { fn: "std::panic::catch_unwind", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/panic.rs", line: 142 }, { fn: "std::rt::lang_start_internal", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 148 }, { fn: "std::rt::lang_start", file: "/rustc/8ede3aae28fe6e4d52b38157d7bfe0d3bceef225/library/std/src/rt.rs", line: 165 }, { fn: "main" }, { fn: "__libc_start_main" }, { fn: "_start" }] }
```
Not super pretty at the moment, but we can see error occurred on `{ fn: "myapp::main", file: "./src/main.rs", line: 29 }`
Another thing to note, is that since Rust is compiled it is not necessarily as easy to recover proper stacktraces
especially in release builds. We're using [`anyhow`](https://docs.rs/anyhow/latest/anyhow/) for that.
The library is still young, please [report](https://github.com/LaurentMazare/candle/issues) any issues detecting where an error is coming from.
## Cuda error management
When running a model on Cuda, you might get a stacktrace not really representing the error.
The reason is that CUDA is async by nature, and therefore the error might be caught while you were sending totally different kernels.
One way to avoid this is to use `CUDA_LAUNCH_BLOCKING=1` as an environment variable. This will force every kernel to be launched sequentially.
You might still however see the error happening on other kernels as the faulty kernel might exit without an error but spoiling some pointer for which the error will happen when dropping the `CudaSlice` only.
If this occurs, you can use [`compute-sanitizer`](https://docs.nvidia.com/compute-sanitizer/ComputeSanitizer/index.html)
This tool is like `valgrind` but for cuda. It will help locate the errors in the kernels.
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hf_public_repos/candle/candle-book/src | hf_public_repos/candle/candle-book/src/advanced/mkl.md | # Using MKL
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