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# candle-mnist-training
Training a 2 layer MLP on mnist in Candle.
## Running an example
```bash
$ cargo run --example mnist-training --features candle-datasets
> train-images: [60000, 784]
> train-labels: [60000]
> test-images: [10000, 784]
> test-labels: [10000]
> 1 train loss: 2.30265 test acc: 68.08%
> 2 train loss: 1.50815 test acc: 60.77%
``` | candle/candle-examples/examples/mnist-training/README.md/0 | {
"file_path": "candle/candle-examples/examples/mnist-training/README.md",
"repo_id": "candle",
"token_count": 143
} | 38 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::{Error as E, Result};
use candle::{DType, IndexOp, Shape, Tensor, D};
use candle_nn::VarBuilder;
use candle_transformers::models::nvembed_v2::model::Model;
use clap::Parser;
use hf_hub::{api::sync::Api, Repo, RepoType};
use tokenizers::{PaddingDirection, PaddingParams, Tokenizer, TruncationParams};
#[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>,
/// L2 normalization for embeddings.
#[arg(long, default_value = "true")]
normalize_embeddings: bool,
#[arg(long)]
tokenizer: Option<String>,
#[arg(long)]
model: Option<String>,
/// Comma-separated list of model files (e.g., '/path/file1.safetensors,/path/file2.safetensors,/path/file3.safetensors')
#[arg(long)]
model_files: Option<String>,
}
impl Args {
fn build_model_and_tokenizer(&self) -> anyhow::Result<(Model, tokenizers::Tokenizer)> {
let model_name = match self.model.as_ref() {
Some(model) => model.to_string(),
None => "nvidia/NV-Embed-v2".to_string(),
};
let api = Api::new()?;
let repo = api.repo(Repo::new(model_name.to_string(), RepoType::Model));
let model_files = match &self.model_files {
Some(files) => files
.split(',')
.map(std::path::PathBuf::from)
.collect::<Vec<_>>(),
None => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json")?,
};
let tokenizer_file = match &self.tokenizer {
Some(file) => std::path::PathBuf::from(file),
None => repo.get("tokenizer.json")?,
};
let device = candle_examples::device(self.cpu)?;
let mut tokenizer = tokenizers::Tokenizer::from_file(tokenizer_file).map_err(E::msg)?;
let _ = tokenizer
.with_padding(Some(PaddingParams {
direction: PaddingDirection::Right,
pad_id: 2,
pad_token: "</s>".to_string(),
..Default::default()
}))
.with_truncation(Some(TruncationParams {
max_length: 32768,
..Default::default()
}));
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&model_files, DType::F32, &device) }?;
let nvembed_model = Model::new(vb);
Ok((nvembed_model?, tokenizer))
}
}
fn encode(
model: &mut Model,
tokenizer: &Tokenizer,
examples: Vec<String>,
instruction: &str,
) -> Result<Tensor> {
let device = &model.device;
let dtype = model.dtype;
// Format input text
let eos_token = if let Some(padding) = tokenizer.get_padding() {
padding.pad_token.clone()
} else {
"".to_string()
};
let bos = "<s>".to_string();
let input_texts = examples
.iter()
.map(|input_example| format!("{bos}{instruction}{input_example}{eos_token}"))
.collect::<Vec<String>>();
// Tokenize
let encodings = tokenizer.encode_batch(input_texts, false).map_err(E::msg)?;
let input_ids_list = encodings
.iter()
.map(|encoding| {
Tensor::from_slice(
encoding.get_ids(),
Shape::from(encoding.get_ids().len()),
device,
)
})
.collect::<Result<Vec<_>, _>>()?;
let input_ids = Tensor::stack(&input_ids_list, 0)?;
// Mask out padding tokens for both embedding model and latent attention model
let attention_masks: Vec<Tensor> = encodings
.iter()
.map(|encoding| {
Tensor::from_slice(
encoding.get_attention_mask(),
Shape::from(encoding.get_attention_mask().len()),
device,
)?
.to_dtype(dtype)
})
.collect::<Result<Vec<_>, _>>()?;
let attention_mask = Tensor::stack(&attention_masks, 0)?;
// Mask out instruction tokens for latent attention model
let pool_mask = if !instruction.is_empty() {
let encoded_instruction = tokenizer.encode(instruction, false).map_err(E::msg)?;
let instruction_lens = encoded_instruction.get_tokens().len();
let zeros = Tensor::zeros(
attention_mask.i((.., ..instruction_lens))?.shape(),
dtype,
device,
)?;
let b = attention_mask.dims()[0];
attention_mask.slice_assign(&[..b, ..instruction_lens], &zeros)?
} else {
attention_mask.clone()
};
let hiddens = model
.forward(&input_ids, &attention_mask, &pool_mask)?
.squeeze(1)?;
// Normalize embedding
div_l2_norm(&hiddens)
}
fn div_l2_norm(v: &Tensor) -> Result<Tensor> {
let l2_norm = v.sqr()?.sum_keepdim(D::Minus1)?.sqrt()?;
Ok(v.broadcast_div(&l2_norm)?)
}
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 (mut model, tokenizer) = args.build_model_and_tokenizer()?;
if let Some(prompt) = args.prompt {
let emb = encode(&mut model, &tokenizer, vec![prompt], "")?;
println!("Embedding: {emb}");
} else {
let queries = [
"are judo throws allowed in wrestling?",
"how to become a radiology technician in michigan?",
];
let passages = [
"Since you're reading this, you are probably someone from a judo background or someone who is just wondering how judo techniques can be applied under wrestling rules. So without further ado, let's get to the question. Are Judo throws allowed in wrestling? Yes, judo throws are allowed in freestyle and folkstyle wrestling. You only need to be careful to follow the slam rules when executing judo throws. In wrestling, a slam is lifting and returning an opponent to the mat with unnecessary force.",
"Below are the basic steps to becoming a radiologic technologist in Michigan:Earn a high school diploma. As with most careers in health care, a high school education is the first step to finding entry-level employment. Taking classes in math and science, such as anatomy, biology, chemistry, physiology, and physics, can help prepare students for their college studies and future careers.Earn an associate degree. Entry-level radiologic positions typically require at least an Associate of Applied Science. Before enrolling in one of these degree programs, students should make sure it has been properly accredited by the Joint Review Committee on Education in Radiologic Technology (JRCERT).Get licensed or certified in the state of Michigan."
];
let passage_instruction = "".to_string();
let query_instruction =
"Instruct: Given a question, retrieve passages that answer the question\nQuery: "
.to_string();
let passages: Vec<String> = passages.iter().map(|s| s.to_string()).collect();
let queries: Vec<String> = queries.iter().map(|s| s.to_string()).collect();
let emb_query = encode(&mut model, &tokenizer, queries, &query_instruction)?;
let emb_passage = encode(&mut model, &tokenizer, passages, &passage_instruction)?;
let scores = (emb_query.matmul(&emb_passage.t()?)? * 100.0)?;
println!("scores: {scores}");
}
Ok(())
}
| candle/candle-examples/examples/nvembed_v2/main.rs/0 | {
"file_path": "candle/candle-examples/examples/nvembed_v2/main.rs",
"repo_id": "candle",
"token_count": 3339
} | 39 |
#[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_examples::token_output_stream::TokenOutputStream;
use candle_transformers::models::mixformer::{Config, MixFormerSequentialForCausalLM as MixFormer};
use candle_transformers::models::phi::{Config as PhiConfig, Model as Phi};
use candle_transformers::models::phi3::{Config as Phi3Config, Model as Phi3};
use candle_transformers::models::quantized_mixformer::MixFormerSequentialForCausalLM as QMixFormer;
use candle::{DType, Device, IndexOp, 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),
Phi3(Phi3),
Quantized(QMixFormer),
}
struct TextGeneration {
model: Model,
device: Device,
tokenizer: TokenOutputStream,
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: TokenOutputStream::new(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
.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_token("<|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();
let mut pos = 0;
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)?,
Model::Phi3(m) => m.forward(&input, pos)?.i((.., 0, ..))?,
};
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 {
if let Some(t) = self.tokenizer.decode_rest()? {
print!("{t}");
std::io::stdout().flush()?;
}
break;
}
if let Some(t) = self.tokenizer.next_token(next_token)? {
print!("{t}");
std::io::stdout().flush()?;
}
pos += context_size;
}
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 = "3")]
V3,
#[value(name = "3-medium")]
V3Medium,
#[value(name = "4-mini")]
V4Mini,
#[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,
/// The dtype to be used for running the model, e.g. f32, bf16, or f16.
#[arg(long)]
dtype: Option<String>,
}
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::V3 => "microsoft/Phi-3-mini-4k-instruct".to_string(),
WhichModel::V3Medium => "microsoft/Phi-3-medium-4k-instruct".to_string(),
WhichModel::V4Mini => "microsoft/Phi-4-mini-instruct".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::V3
| WhichModel::V3Medium
| WhichModel::V4Mini
| 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
| WhichModel::V3
| WhichModel::V3Medium
| WhichModel::V4Mini => 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")?],
WhichModel::V3 | WhichModel::V3Medium | WhichModel::V4Mini => anyhow::bail!(
"use the quantized or quantized-phi examples for quantized phi-v3"
),
}
} else {
match args.model {
WhichModel::V1 | WhichModel::V1_5 => vec![repo.get("model.safetensors")?],
WhichModel::V2
| WhichModel::V2Old
| WhichModel::V3
| WhichModel::V3Medium
| WhichModel::V4Mini => 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(),
WhichModel::V3 | WhichModel::V3Medium | WhichModel::V4Mini => {
panic!("use the quantized or quantized-phi examples for quantized phi-v3")
}
};
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 dtype = match args.dtype {
Some(dtype) => std::str::FromStr::from_str(&dtype)?,
None => {
if args.model == WhichModel::V3
|| args.model == WhichModel::V3Medium
|| args.model == WhichModel::V4Mini
{
device.bf16_default_to_f32()
} else {
DType::F32
}
}
};
let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &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::V3 | WhichModel::V3Medium | WhichModel::V4Mini => {
let config_filename = repo.get("config.json")?;
let config = std::fs::read_to_string(config_filename)?;
let config: Phi3Config = serde_json::from_str(&config)?;
let phi3 = Phi3::new(&config, vb)?;
Model::Phi3(phi3)
}
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::Phi3(m) => {
m.clear_kv_cache();
m.forward(&input, 0)?
}
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(())
}
| candle/candle-examples/examples/phi/main.rs/0 | {
"file_path": "candle/candle-examples/examples/phi/main.rs",
"repo_id": "candle",
"token_count": 9709
} | 40 |
# candle-qwen: large language model series from Alibaba Cloud
Qwen 1.5 is a series of large language models that provide strong performances
on English and Chinese.
- [Blog post](https://qwenlm.github.io/blog/qwen1.5/) introducing Qwen1.5.
- [Model card](https://huggingface.co/Qwen/Qwen1.5-0.5B) on the HuggingFace Hub.
- [Blog post](https://qwenlm.github.io/blog/qwen-moe/) for the
mixture-of-experts (MoE) variant.
## Running the example
```bash
$ cargo run --example qwen --release -- --prompt "Hello there "
```
Various model sizes are available via the `--model` argument, including the MoE
variant.
```bash
$ cargo run --example qwen --release -- --model moe-a2.7b --prompt 'def print_prime(n: int): '
def print_prime(n: int): # n is the number of primes to be printed
for i in range(2, n + 1):
if all(i % j != 0 for j in range(2, i)):
print(i)
```
The qwen3 MoE variant is also an option.
```bash
$ cargo run --example qwen --features metal --release -- --prompt "Write a poem about butterflies. <think></think>." --model "3-moe-a3b"
> In morning's hush, where daisies sleep,
> A fleeting dance through sunlit deep—
> They flutter soft on gossamer thread,
> The messengers of spring’s own head.
>
> With painted sails and delicate grace,
> They drift from bloom to blossom's face.
> Each wing a tale in hues unseen,
> Of ancient dreams and secrets between.
>
> No sound they make, yet still they speak—
> Of time that flies, of life so brief.
> A fleeting kiss on summer’s breath,
> A whisper lost before death.
>
> Yet in their flight, the soul takes wing,
> And for a moment, all is spring.
> For though they fade, they never die—
> Their beauty lives where hearts can fly.
> 161 tokens generated (3.00 token/s)
```
| candle/candle-examples/examples/qwen/README.md/0 | {
"file_path": "candle/candle-examples/examples/qwen/README.md",
"repo_id": "candle",
"token_count": 616
} | 41 |
# 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 candle-examples/examples/yolo-v8/assets/bike.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%
```
| candle/candle-examples/examples/resnet/README.md/0 | {
"file_path": "candle/candle-examples/examples/resnet/README.md",
"repo_id": "candle",
"token_count": 220
} | 42 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use anyhow::Result;
use clap::Parser;
use candle::{DType, Tensor};
#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
enum Which {
#[value(name = "silero")]
Silero,
}
#[derive(Clone, Debug, Copy, PartialEq, Eq, clap::ValueEnum)]
enum SampleRate {
#[value(name = "8000")]
Sr8k,
#[value(name = "16000")]
Sr16k,
}
#[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)]
input: Option<String>,
#[arg(long)]
sample_rate: SampleRate,
#[arg(long)]
model_id: Option<String>,
#[arg(long)]
config_file: Option<String>,
/// The model to use.
#[arg(long, default_value = "silero")]
which: Which,
}
/// an iterator which reads consecutive frames of le i16 values from a reader
struct I16Frames<R> {
rdr: R,
buf: Box<[u8]>,
len: usize,
eof: bool,
}
impl<R> I16Frames<R> {
fn new(rdr: R, frame_size: usize) -> Self {
I16Frames {
rdr,
buf: vec![0; frame_size * std::mem::size_of::<i16>()].into_boxed_slice(),
len: 0,
eof: false,
}
}
}
impl<R: std::io::Read> Iterator for I16Frames<R> {
type Item = std::io::Result<Vec<f32>>;
fn next(&mut self) -> Option<Self::Item> {
if self.eof {
return None;
}
self.len += match self.rdr.read(&mut self.buf[self.len..]) {
Ok(0) => {
self.eof = true;
0
}
Ok(n) => n,
Err(e) => return Some(Err(e)),
};
if self.eof || self.len == self.buf.len() {
let buf = self.buf[..self.len]
.chunks(2)
.map(|bs| match bs {
[a, b] => i16::from_le_bytes([*a, *b]),
_ => unreachable!(),
})
.map(|i| i as f32 / i16::MAX as f32)
.collect();
self.len = 0;
Some(Ok(buf))
} else {
self.next()
}
}
}
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()
);
let start = std::time::Instant::now();
let model_id = match &args.model_id {
Some(model_id) => std::path::PathBuf::from(model_id),
None => match args.which {
Which::Silero => hf_hub::api::sync::Api::new()?
.model("onnx-community/silero-vad".into())
.get("onnx/model.onnx")?,
// TODO: candle-onnx doesn't support Int8 dtype
// Which::SileroQuantized => hf_hub::api::sync::Api::new()?
// .model("onnx-community/silero-vad".into())
// .get("onnx/model_quantized.onnx")?,
},
};
let (sample_rate, frame_size, context_size): (i64, usize, usize) = match args.sample_rate {
SampleRate::Sr8k => (8000, 256, 32),
SampleRate::Sr16k => (16000, 512, 64),
};
println!("retrieved the files in {:?}", start.elapsed());
let start = std::time::Instant::now();
let device = candle_examples::device(args.cpu)?;
let model = candle_onnx::read_file(model_id)?;
println!("loaded the model in {:?}", start.elapsed());
let start = std::time::Instant::now();
struct State {
frame_size: usize,
sample_rate: Tensor,
state: Tensor,
context: Tensor,
}
let mut state = State {
frame_size,
sample_rate: Tensor::new(sample_rate, &device)?,
state: Tensor::zeros((2, 1, 128), DType::F32, &device)?,
context: Tensor::zeros((1, context_size), DType::F32, &device)?,
};
let mut res = vec![];
for chunk in I16Frames::new(std::io::stdin().lock(), state.frame_size) {
let chunk = chunk.unwrap();
if chunk.len() < state.frame_size {
continue;
}
let next_context = Tensor::from_slice(
&chunk[state.frame_size - context_size..],
(1, context_size),
&device,
)?;
let chunk = Tensor::from_vec(chunk, (1, state.frame_size), &device)?;
let chunk = Tensor::cat(&[&state.context, &chunk], 1)?;
let inputs = std::collections::HashMap::from_iter([
("input".to_string(), chunk),
("sr".to_string(), state.sample_rate.clone()),
("state".to_string(), state.state.clone()),
]);
let out = candle_onnx::simple_eval(&model, inputs).unwrap();
let out_names = &model.graph.as_ref().unwrap().output;
let output = out.get(&out_names[0].name).unwrap().clone();
state.state = out.get(&out_names[1].name).unwrap().clone();
assert_eq!(state.state.dims(), &[2, 1, 128]);
state.context = next_context;
let output = output.flatten_all()?.to_vec1::<f32>()?;
assert_eq!(output.len(), 1);
let output = output[0];
println!("vad chunk prediction: {output}");
res.push(output);
}
println!("calculated prediction in {:?}", start.elapsed());
let res_len = res.len() as f32;
let prediction = res.iter().sum::<f32>() / res_len;
println!("vad average prediction: {prediction}");
Ok(())
}
| candle/candle-examples/examples/silero-vad/main.rs/0 | {
"file_path": "candle/candle-examples/examples/silero-vad/main.rs",
"repo_id": "candle",
"token_count": 2894
} | 43 |
# candle-starcoder2
Candle implementation of Star Coder 2 family of code generation model from [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/pdf/2402.19173).
## Running an example
```bash
$ cargo run --example starcoder2 -- --prompt "write a recursive fibonacci function in python "
> # that returns the nth number in the sequence.
>
> def fib(n):
> if n
``` | candle/candle-examples/examples/starcoder2/README.md/0 | {
"file_path": "candle/candle-examples/examples/starcoder2/README.md",
"repo_id": "candle",
"token_count": 126
} | 44 |
use std::path::PathBuf;
use anyhow::Result;
use hf_hub::{api::sync::Api, Repo, RepoType};
/// # Errors
///
/// Returns an error if the model files cannot be downloaded.
///
/// # Panics
///
/// Panics if the model files cannot be downloaded.
pub fn model_files(model_id: &str) -> Result<((PathBuf, Vec<PathBuf>), PathBuf)> {
let revision = "main";
let api = Api::new().unwrap();
let repo = api.repo(Repo::with_revision(
model_id.to_string(),
RepoType::Model,
revision.to_string(),
));
let config = repo.get("config.json")?;
// Download model files - look for safetensors
let mut model_files = Vec::new();
// Common Voxtral/Ultravox safetensors file patterns
let safetensors_files = match model_id {
"mistralai/Voxtral-Mini-3B-2507" => vec![
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
],
"mistralai/Voxtral-Small-24B-2507" => vec![
"model-00001-of-00011.safetensors",
"model-00001-of-00011.safetensors",
"model-00002-of-00011.safetensors",
"model-00003-of-00011.safetensors",
"model-00004-of-00011.safetensors",
"model-00005-of-00011.safetensors",
"model-00006-of-00011.safetensors",
"model-00007-of-00011.safetensors",
"model-00008-of-00011.safetensors",
"model-00009-of-00011.safetensors",
"model-00010-of-00011.safetensors",
"model-00011-of-00011.safetensors",
],
_ => vec![
"model.safetensors",
"pytorch_model.safetensors",
"model-00001-of-00001.safetensors",
"model-00001-of-00002.safetensors",
"model-00002-of-00002.safetensors",
],
};
println!("Downloading safetensors files...");
for filename in &safetensors_files {
if let Ok(file) = repo.get(filename) {
println!("{} downloaded", filename);
model_files.push(file);
}
}
if model_files.is_empty() {
anyhow::bail!("No safetensors files found in model repository {model_id}",);
}
// Download tokenizer
let tokenizer_file = repo
.get("tekken.json")
.or_else(|_| repo.get("tokenizer/tokenizer.json"))?;
Ok(((config, model_files), tokenizer_file))
}
| candle/candle-examples/examples/voxtral/download.rs/0 | {
"file_path": "candle/candle-examples/examples/voxtral/download.rs",
"repo_id": "candle",
"token_count": 1158
} | 45 |
# candle-xlm-roberta
This example demonstrates how to use the XLM-RoBERTa model in Candle especially known for their use in reranking. It uses the `fill-mask` task to generate a word for a masked token. And a `reranker` task to rerank a list of documents for a given query.
## Usage
Fill Mask:
```bash
cargo run --example xlm-roberta --release -- --task fill-mask --model xlm-roberta-base
```
```markdown
Sentence: 0 : Hello I'm a fashion model.
Sentence: 1 : I'm a little boy.
Sentence: 2 : I'm living in berlin.
```
Reranker:
```bash
cargo run --example xlm-roberta --release -- --task reranker --model bge-reranker-base
```
```markdown
Ranking Results:
--------------------------------------------------------------------------------
> Rank #4 | Score: 0.0001 | South Korea is a country in East Asia.
> Rank #5 | Score: 0.0000 | There are forests in the mountains.
> Rank #2 | Score: 0.7314 | Pandas look like bears.
> Rank #3 | Score: 0.6948 | There are some animals with black and white fur.
> Rank #1 | Score: 0.9990 | The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.
--------------------------------------------------------------------------------
```
Text-Classification:
```bash
cargo run --example xlm-roberta -- --task text-classification --model xlmr-formality-classifier
```
```markdown
Formality Scores:
Text 1: "I like you. I love you"
formal: 0.9933
informal: 0.0067
Text 2: "Hey, what's up?"
formal: 0.8812
informal: 0.1188
Text 3: "Siema, co porabiasz?"
formal: 0.9358
informal: 0.0642
Text 4: "I feel deep regret and sadness about the situation in international politics."
formal: 0.9987
informal: 0.0013
``` | candle/candle-examples/examples/xlm-roberta/Readme.md/0 | {
"file_path": "candle/candle-examples/examples/xlm-roberta/Readme.md",
"repo_id": "candle",
"token_count": 547
} | 46 |
#include "kernels.h"
#include "kernel_helpers.h"
#include "flash_fwd_launch_template.h"
void run_mha_fwd(Flash_fwd_params ¶ms, cudaStream_t stream) {
FP16_SWITCH(!params.is_bf16, [&] {
HEADDIM_SWITCH(params.d, [&] {
BOOL_SWITCH(params.is_causal, Is_causal, [&] {
run_mha_fwd_<elem_type, kHeadDim, Is_causal>(params, stream);
});
});
});
}
extern "C" void run_mha(
void *q_ptr,
void *k_ptr,
void *v_ptr,
void *o_ptr,
void *softmax_lse_ptr,
void *alibi_slopes_ptr,
int32_t *cu_seqlens_q_ptr,
int32_t *cu_seqlens_k_ptr,
uint32_t q_batch_stride,
uint32_t k_batch_stride,
uint32_t v_batch_stride,
uint32_t o_batch_stride,
uint32_t alibi_slopes_batch_stride,
uint32_t q_row_stride,
uint32_t k_row_stride,
uint32_t v_row_stride,
uint32_t o_row_stride,
uint32_t q_head_stride,
uint32_t k_head_stride,
uint32_t v_head_stride,
uint32_t o_head_stride,
uint32_t b,
uint32_t h,
uint32_t h_k,
uint32_t d,
uint32_t d_rounded,
float softmax_scale,
uint32_t seqlen_q,
uint32_t seqlen_k,
uint32_t seqlen_q_rounded,
uint32_t seqlen_k_rounded,
int is_bf16,
int is_causal,
int unpadded_lse,
int window_size_left,
int window_size_right,
float softcap
) {
Flash_fwd_params params;
// Reset the parameters
memset(¶ms, 0, sizeof(params));
// Set the pointers and strides.
params.q_ptr = q_ptr;
params.k_ptr = k_ptr;
params.v_ptr = v_ptr;
params.o_ptr = o_ptr;
params.softmax_lse_ptr = softmax_lse_ptr;
params.alibi_slopes_ptr = alibi_slopes_ptr;
// All stride are in elements, not bytes.
params.q_batch_stride = q_batch_stride;
params.k_batch_stride = k_batch_stride;
params.v_batch_stride = v_batch_stride;
params.o_batch_stride = o_batch_stride;
params.alibi_slopes_batch_stride = alibi_slopes_batch_stride;
params.q_row_stride = q_row_stride;
params.k_row_stride = k_row_stride;
params.v_row_stride = v_row_stride;
params.o_row_stride = o_row_stride;
params.q_head_stride = q_head_stride;
params.k_head_stride = k_head_stride;
params.v_head_stride = v_head_stride;
params.o_head_stride = o_head_stride;
// Set the dimensions.
params.b = b;
params.h = h;
params.h_k = h_k;
params.h_h_k_ratio = h / h_k;
params.seqlen_q = seqlen_q;
params.seqlen_k = seqlen_k;
params.seqlen_q_rounded = seqlen_q_rounded;
params.seqlen_k_rounded = seqlen_k_rounded;
params.d = d;
params.d_rounded = d_rounded;
// Set the different scale values.
if (softcap > 0.0) {
params.softcap = softmax_scale / softcap;
params.scale_softmax = softcap;
params.scale_softmax_log2 = softcap * M_LOG2E;
} else{
// Remove potential NaN
params.softcap = 0.0;
params.scale_softmax = softmax_scale;
params.scale_softmax_log2 = softmax_scale * M_LOG2E;
}
params.p_dropout = 1.; // probability to keep
params.p_dropout_in_uint8_t = uint8_t(std::floor(params.p_dropout * 255.0));
params.rp_dropout = 1.f / params.p_dropout;
params.scale_softmax_rp_dropout = params.rp_dropout * params.scale_softmax;
params.is_bf16 = is_bf16;
params.cu_seqlens_q = cu_seqlens_q_ptr;
params.cu_seqlens_k = cu_seqlens_k_ptr;
params.p_ptr = nullptr; // used for `return_softmax`.
params.seqused_k = nullptr;
params.is_causal = is_causal;
params.window_size_left = window_size_left;
params.window_size_right = window_size_right;
params.is_seqlens_k_cumulative = true;
params.num_splits = 1;
params.unpadded_lse = unpadded_lse;
cudaStream_t stream = 0; // Use the default stream.
run_mha_fwd(params, stream);
}
| candle/candle-flash-attn/kernels/flash_api.cu/0 | {
"file_path": "candle/candle-flash-attn/kernels/flash_api.cu",
"repo_id": "candle",
"token_count": 1818
} | 47 |
use anyhow::Result;
use candle::{DType, Device, IndexOp, Tensor, D};
fn to_vec3_round(t: Tensor, digits: i32) -> Result<Vec<Vec<Vec<f32>>>> {
let b = 10f32.powi(digits);
let t = t.to_vec3::<f32>()?;
let t = t
.iter()
.map(|t| {
t.iter()
.map(|t| t.iter().map(|t| f32::round(t * b) / b).collect())
.collect()
})
.collect();
Ok(t)
}
fn fa_acausal(q: &Tensor, k: &Tensor, v: &Tensor, softmax_scale: f32) -> Result<Tensor> {
let in_dtype = q.dtype();
let q = q.to_dtype(DType::F32)?;
let k = k.to_dtype(DType::F32)?;
let v = v.to_dtype(DType::F32)?;
let att = (q.matmul(&k.t()?)? * softmax_scale as f64)?;
let att = candle_nn::ops::softmax(&att, D::Minus1)?;
// Convert to contiguous as matmul doesn't support strided vs for now.
let output = att.matmul(&v.contiguous()?)?.to_dtype(in_dtype)?;
Ok(output)
}
fn fa_acausal_softcap(q: &Tensor, k: &Tensor, v: &Tensor, softcap: f32) -> Result<Tensor> {
let in_dtype = q.dtype();
let q = q.to_dtype(DType::F32)?;
let k = k.to_dtype(DType::F32)?;
let v = v.to_dtype(DType::F32)?;
// let att = (q.matmul(&k.t()?)? * softmax_scale as f64)?;
let att = q.matmul(&k.t()?)?;
let att = (softcap as f64 * ((att / softcap as f64)?.tanh())?)?;
let att = candle_nn::ops::softmax(&att, D::Minus1)?;
// Convert to contiguous as matmul doesn't support strided vs for now.
let output = att.matmul(&v.contiguous()?)?.to_dtype(in_dtype)?;
Ok(output)
}
#[test]
fn flash_attn_acausal() -> Result<()> {
let device = Device::new_cuda(0)?;
let q = Tensor::arange(0u32, 48, &device)?
.to_dtype(DType::F16)?
.reshape((1, 3, 2, 8))?;
let k = (&q / 40.)?;
let v = (&q / 50.)?;
let q = (&q / 30.)?;
let ys1 = fa_acausal(&q, &k, &v, 0.5)?;
let ys1 = ys1.i(0)?.to_dtype(DType::F32)?;
let ys2 = {
let q = q.transpose(1, 2)?;
let k = k.transpose(1, 2)?;
let v = v.transpose(1, 2)?;
candle_flash_attn::flash_attn(&q, &k, &v, 0.5, false)?.transpose(1, 2)?
};
let ys2 = ys2.i(0)?.to_dtype(DType::F32)?;
let diff = ys1.sub(&ys2)?.abs()?.flatten_all()?.max(0)?;
assert_eq!(ys1.dims(), &[3, 2, 8]);
assert_eq!(
to_vec3_round(ys1, 4)?,
&[
[
[0.0837, 0.1038, 0.1238, 0.1438, 0.1637, 0.1837, 0.2037, 0.2238],
[0.0922, 0.1122, 0.1322, 0.1522, 0.1721, 0.1921, 0.2122, 0.2322]
],
[
[0.4204, 0.4404, 0.4604, 0.4805, 0.5005, 0.5205, 0.5405, 0.5605],
[0.428, 0.448, 0.468, 0.488, 0.5083, 0.5283, 0.5483, 0.5684]
],
[
[0.7554, 0.7754, 0.7954, 0.8154, 0.8354, 0.8555, 0.8755, 0.8955],
[0.7622, 0.7822, 0.8022, 0.8223, 0.8423, 0.8623, 0.8823, 0.9023]
]
]
);
assert_eq!(ys2.dims(), &[3, 2, 8]);
assert_eq!(
to_vec3_round(ys2, 4)?,
&[
[
[0.0837, 0.1038, 0.1238, 0.1438, 0.1637, 0.1837, 0.2037, 0.2238],
[0.0922, 0.1122, 0.1322, 0.1522, 0.1721, 0.1921, 0.2122, 0.2322]
],
[
[0.4204, 0.4404, 0.4604, 0.4805, 0.5005, 0.5205, 0.5405, 0.5605],
[0.428, 0.448, 0.468, 0.488, 0.5083, 0.5283, 0.5483, 0.5684]
],
[
[0.7554, 0.7754, 0.7954, 0.8154, 0.8354, 0.8555, 0.8755, 0.8955],
[0.7622, 0.7822, 0.8022, 0.8223, 0.8423, 0.8623, 0.8823, 0.9023]
]
]
);
assert!(diff.to_vec0::<f32>()?.abs() < 1e-5);
Ok(())
}
#[test]
fn flash_attn_acausal_softcap() -> Result<()> {
let device = Device::new_cuda(0)?;
let q = Tensor::arange(0u32, 3 * 5 * 8, &device)?
.to_dtype(DType::F16)?
.reshape((1, 3, 5, 8))?;
let k = (&q / 40.)?;
let v = (&q / 50.)?;
let q = (&q / 30.)?;
let softcap = 5.0f32;
let ys1 = fa_acausal_softcap(&q, &k, &v, softcap.clone())?;
let ys1 = ys1.i(0)?.to_dtype(DType::F32)?;
let ys2 = {
let q = q.transpose(1, 2)?;
let k = k.transpose(1, 2)?;
let v = v.transpose(1, 2)?;
candle_flash_attn::flash_attn_alibi_windowed_softcap(
&q,
&k,
&v,
None, // alibi_slopes //
1.0, // softmax //
None, // window_size_left //
None, // window_size_right //
softcap.clone(), // softcap //
)?
.transpose(1, 2)?
};
let ys2 = ys2.i(0)?.to_dtype(DType::F32)?;
let diff = ys1.sub(&ys2)?.abs()?.flatten_all()?.max(0)?;
assert_eq!(ys1.dims(), &[3, 5, 8]);
assert_eq!(ys2.dims(), &[3, 5, 8]);
assert!(diff.to_vec0::<f32>()?.abs() < 1e-3);
Ok(())
}
#[test]
fn flash_attn_varlen() -> Result<()> {
let device = Device::new_cuda(0)?;
let q = Tensor::arange(0u32, 48, &device)?
.to_dtype(DType::F16)?
.reshape((3, 2, 8))?;
let k = (&q / 40.)?;
let v = (&q / 50.)?;
let q = (&q / 30.)?;
let seqlens_q = Tensor::new(&[0u32, 2u32], &device)?;
let seqlens_k = Tensor::new(&[0u32, 2u32], &device)?;
let ys = {
let q = q.transpose(0, 1)?;
let k = k.transpose(0, 1)?;
let v = v.transpose(0, 1)?;
candle_flash_attn::flash_attn_varlen(
&q, &k, &v, &seqlens_q, &seqlens_k, 32, 32, 0.5, false,
)?
.transpose(0, 1)?
};
let ys = ys.to_dtype(DType::F32)?;
assert_eq!(ys.dims(), &[3, 2, 8]);
assert_eq!(
to_vec3_round(ys, 4)?,
&[
[
[0.0837, 0.1038, 0.1238, 0.1438, 0.1637, 0.1837, 0.2037, 0.2238],
[0.0922, 0.1122, 0.1322, 0.1522, 0.1721, 0.1921, 0.2122, 0.2322]
],
[
[0.4204, 0.4404, 0.4604, 0.4805, 0.5005, 0.5205, 0.5405, 0.5605],
[0.428, 0.448, 0.468, 0.488, 0.5083, 0.5283, 0.5483, 0.5684]
],
[
[0.7554, 0.7754, 0.7954, 0.8154, 0.8354, 0.8555, 0.8755, 0.8955],
[0.7622, 0.7822, 0.8022, 0.8223, 0.8423, 0.8623, 0.8823, 0.9023]
]
]
);
Ok(())
}
| candle/candle-flash-attn/tests/flash_attn_tests.rs/0 | {
"file_path": "candle/candle-flash-attn/tests/flash_attn_tests.rs",
"repo_id": "candle",
"token_count": 3779
} | 48 |
#include "cuda_utils.cuh"
#include <cmath>
#include <stdint.h>
#define WARP_SIZE 32
const int BLOCK_SIZE = 1024;
// TODO: Maybe add some fast_sum_f16_f32 variant that not only accumulate in f32
// but also expect a f32 output so that this can be used for normalization e.g.
// in softmax.
// Fast reduce sum kernel, this assumes that the dimensions to loop over are at
// the end, each block is responsible for populating one value in the output
// array. There are at most 1024 threads per block.
template <typename T>
__device__ void
fast_sum(const size_t src_numel, const size_t el_to_sum_per_block,
const size_t num_dims, const size_t *info, const T *src, T *dst) {
const size_t *dims = info;
const size_t *strides = info + num_dims;
__shared__ T shr[BLOCK_SIZE];
size_t tid = threadIdx.x;
size_t dst_id = blockIdx.x;
shr[tid] = 0;
// Elements summed in this block range from dst_id * el_to_sum_per_block
// to (dst_id + 1) * el_to_sum_per_block.
size_t start_idx = dst_id * el_to_sum_per_block;
size_t stop_idx = min(start_idx + el_to_sum_per_block, src_numel);
size_t idx = start_idx + tid;
while (idx < stop_idx) {
// TODO: Fast version for the contiguous case.
size_t strided_i = get_strided_index(idx, num_dims, dims, strides);
shr[tid] += src[strided_i];
idx += blockDim.x;
}
// Parallel reduction, see the slides:
// https://www.olcf.ornl.gov/wp-content/uploads/2019/12/05_Atomics_Reductions_Warp_Shuffle.pdf
// https://stackoverflow.com/questions/66078814/is-cuda-atomicadd-operation-faster-than-launch-another-kernel-when-we-do-reduce
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
__syncthreads();
if (tid < s)
shr[tid] += shr[tid + s];
}
if (tid == 0)
dst[dst_id] = shr[0];
}
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32);
a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32);
}
return a;
}
static __device__ __forceinline__ float warp_reduce_sum(float x) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
x += __shfl_xor_sync(0xffffffff, x, mask, 32);
}
return x;
}
// LayerNorm implementation adapted from ggml, accumulation is made using f32.
// https://github.com/ggerganov/llama.cpp/blob/d59bd97065cd7ded6c4ecab54b1d5e0b1b11e318/ggml-cuda.cu#L477
template <typename T>
__device__ void layernorm(const T * x, T * dst, const T * alpha, const T * beta, const int ncols, const int block_size, const float eps) {
const int row = blockIdx.x*blockDim.y + threadIdx.y;
const int tid = threadIdx.x;
float2 mean_var = make_float2(0.f, 0.f);
for (int col = tid; col < ncols; col += block_size) {
const float xi = x[row*ncols + col];
mean_var.x += xi;
mean_var.y += xi * xi;
}
// sum up partial sums
mean_var = warp_reduce_sum(mean_var);
if (block_size > WARP_SIZE) {
__shared__ float2 s_sum[32];
int warp_id = threadIdx.x / WARP_SIZE;
int lane_id = threadIdx.x % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = mean_var;
}
__syncthreads();
mean_var = s_sum[lane_id];
mean_var = warp_reduce_sum(mean_var);
}
const float mean = mean_var.x / ncols;
const float var = mean_var.y / ncols - mean * mean;
const float inv_std = rsqrtf(var + eps);
if (alpha == nullptr && beta == nullptr) {
for (int col = tid; col < ncols; col += block_size) {
float lhs = (static_cast<float>(x[row*ncols + col]) - mean) * inv_std;
dst[row*ncols + col] = static_cast<T>(lhs);
}
}
else if (alpha == nullptr && beta != nullptr) {
for (int col = tid; col < ncols; col += block_size) {
float b = static_cast<float>(beta[col]);
float lhs = (static_cast<float>(x[row*ncols + col]) - mean) * inv_std;
dst[row*ncols + col] = static_cast<T>(lhs + b);
}
}
else if (alpha != nullptr && beta == nullptr) {
for (int col = tid; col < ncols; col += block_size) {
float a = static_cast<float>(alpha[col]);
float lhs = (static_cast<float>(x[row*ncols + col]) - mean) * inv_std;
dst[row*ncols + col] = static_cast<T>(lhs * a);
}
}
else {
for (int col = tid; col < ncols; col += block_size) {
float a = static_cast<float>(alpha[col]);
float b = static_cast<float>(beta[col]);
float lhs = (static_cast<float>(x[row*ncols + col]) - mean) * inv_std;
dst[row*ncols + col] = static_cast<T>(lhs * a + b);
}
}
}
// RmsNorm implementation adapted from ggml, accumulation is made using f32.
// https://github.com/ggerganov/llama.cpp/blob/d59bd97065cd7ded6c4ecab54b1d5e0b1b11e318/ggml-cuda.cu#L523
template <typename T>
__device__ void rmsnorm(const T * x, T * dst, const T * alpha, const int ncols, const int block_size, const float eps) {
const int row = blockIdx.x*blockDim.y + threadIdx.y;
const int tid = threadIdx.x;
float tmp = 0.0f; // partial sum for thread in warp
for (int col = tid; col < ncols; col += block_size) {
const float xi = static_cast<float>(x[row*ncols + col]);
tmp += xi * xi;
}
// sum up partial sums
tmp = warp_reduce_sum(tmp);
if (block_size > WARP_SIZE) {
__shared__ float s_sum[32];
int warp_id = threadIdx.x / WARP_SIZE;
int lane_id = threadIdx.x % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = tmp;
}
__syncthreads();
tmp = s_sum[lane_id];
tmp = warp_reduce_sum(tmp);
}
const float mean = tmp / ncols;
const float scale = rsqrtf(mean + eps);
if (alpha == nullptr) {
for (int col = tid; col < ncols; col += block_size) {
dst[row*ncols + col] = static_cast<T>(scale * static_cast<float>(x[row*ncols + col]));
}
}
else {
for (int col = tid; col < ncols; col += block_size) {
float a = static_cast<float>(alpha[col]);
dst[row*ncols + col] = static_cast<T>(scale * static_cast<float>(x[row*ncols + col]) * a);
}
}
}
// Softmax implementation adapted from ggml.
// https://github.com/ggerganov/llama.cpp/blob/d59bd97065cd7ded6c4ecab54b1d5e0b1b11e318/ggml-cuda.cu#L4159
template <typename T, typename ACC>
__device__ void softmax(const T * x, T * dst, const int ncols) {
const int row = blockDim.x*blockIdx.x + threadIdx.x;
const int block_size = blockDim.y;
const int tid = threadIdx.y;
T max_val = -INFINITY;
for (int col = tid; col < ncols; col += block_size) {
const int i = row*ncols + col;
max_val = maxg(max_val, x[i]);
}
// find the max value in the block
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
max_val = maxg(max_val, __shfl_xor_sync(0xffffffff, max_val, mask, 32));
}
ACC tmp = 0.;
for (int col = tid; col < ncols; col += block_size) {
const int i = row*ncols + col;
const T val = expg(x[i] - max_val);
tmp += static_cast<ACC>(val);
dst[i] = val;
}
// sum up partial sums
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
}
const ACC inv_tmp = 1. / tmp;
for (int col = tid; col < ncols; col += block_size) {
const int i = row*ncols + col;
dst[i] *= inv_tmp;
}
}
template <typename T>
__device__ void ropei(const T * src, const T * cos, const T * sin, T * dst, const uint32_t bh, const uint32_t td, const uint32_t stride_b) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (2 * idx >= bh * td) return;
uint32_t rope_idx = idx % (td / 2);
if (stride_b > 0) {
uint32_t b_idx = (2 * idx) / stride_b;
rope_idx += b_idx * (td / 2);
}
T c = cos[rope_idx];
T s = sin[rope_idx];
dst[2 * idx] = src[2 * idx] * c - src[2 * idx + 1] * s;
dst[2 * idx + 1] = src[2 * idx] * s + src[2 * idx + 1] * c;
}
template <typename T>
__device__ void rope(const T * src, const T * cos, const T * sin, T * dst, const uint32_t bh, const uint32_t td, const uint32_t d, const uint32_t stride_b) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (2 * idx >= bh * td) return;
uint32_t i_bh = idx / (td / 2);
uint32_t i_td = idx - (td / 2) * i_bh;
uint32_t i_t = i_td / (d / 2);
uint32_t i_d = i_td - (d / 2) * i_t;
uint32_t i1 = i_bh * td + i_t * d + i_d;
uint32_t i2 = i1 + d / 2;
uint32_t i_cs = i_t * (d / 2) + i_d;
if (stride_b > 0) {
uint32_t b_idx = (2 * idx) / stride_b;
i_cs += b_idx * (td / 2);
}
T c = cos[i_cs];
T s = sin[i_cs];
dst[i1] = src[i1] * c - src[i2] * s;
dst[i2] = src[i1] * s + src[i2] * c;
}
template <typename T>
__device__ void rope_thd(
const T * src,
const T * cos,
const T * sin,
T * dst,
const uint32_t b,
const uint32_t t,
const uint32_t h,
const uint32_t d,
const uint32_t stride_b
) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (2 * idx >= b * t * h * d) return;
uint32_t i_bth = idx / (d / 2);
uint32_t i_d = idx - (d / 2) * i_bth;
uint32_t i_t = (i_bth / h) % t;
uint32_t i1 = i_bth * d + i_d;
uint32_t i2 = i1 + d / 2;
uint32_t i_cs = i_t * (d / 2) + i_d;
if (stride_b > 0) {
uint32_t b_idx = (2 * idx) / stride_b;
i_cs += b_idx * ((t * d) / 2);
}
T c = cos[i_cs];
T s = sin[i_cs];
dst[i1] = src[i1] * c - src[i2] * s;
dst[i2] = src[i1] * s + src[i2] * c;
}
template <typename T>
__device__ void
fast_max(const size_t src_numel, const size_t el_to_sum_per_block,
const size_t num_dims, const size_t *info, const T *src, T *dst) {
const size_t *dims = info;
const size_t *strides = info + num_dims;
__shared__ T shr[BLOCK_SIZE];
size_t tid = threadIdx.x;
size_t dst_id = blockIdx.x;
shr[tid] = -INFINITY;
// Elements summed in this block range from dst_id * el_to_sum_per_block
// to (dst_id + 1) * el_to_sum_per_block.
size_t start_idx = dst_id * el_to_sum_per_block;
size_t stop_idx = min(start_idx + el_to_sum_per_block, src_numel);
size_t idx = start_idx + tid;
while (idx < stop_idx) {
// TODO: Fast version for the contiguous case.
size_t strided_i = get_strided_index(idx, num_dims, dims, strides);
shr[tid] = maxg(shr[tid], src[strided_i]);
idx += blockDim.x;
}
// Parallel reduction, see the slides:
// https://www.olcf.ornl.gov/wp-content/uploads/2019/12/05_Atomics_Reductions_Warp_Shuffle.pdf
// https://stackoverflow.com/questions/66078814/is-cuda-atomicadd-operation-faster-than-launch-another-kernel-when-we-do-reduce
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
__syncthreads();
if (tid < s)
shr[tid] = maxg(shr[tid], shr[tid + s]);
}
if (tid == 0)
dst[dst_id] = shr[0];
}
template <typename T>
__device__ void
fast_min(const size_t src_numel, const size_t el_to_sum_per_block,
const size_t num_dims, const size_t *info, const T *src, T *dst) {
const size_t *dims = info;
const size_t *strides = info + num_dims;
__shared__ T shr[BLOCK_SIZE];
size_t tid = threadIdx.x;
size_t dst_id = blockIdx.x;
shr[tid] = INFINITY;
// Elements summed in this block range from dst_id * el_to_sum_per_block
// to (dst_id + 1) * el_to_sum_per_block.
size_t start_idx = dst_id * el_to_sum_per_block;
size_t stop_idx = min(start_idx + el_to_sum_per_block, src_numel);
size_t idx = start_idx + tid;
while (idx < stop_idx) {
// TODO: Fast version for the contiguous case.
size_t strided_i = get_strided_index(idx, num_dims, dims, strides);
shr[tid] = ming(shr[tid], src[strided_i]);
idx += blockDim.x;
}
// Parallel reduction, see the slides:
// https://www.olcf.ornl.gov/wp-content/uploads/2019/12/05_Atomics_Reductions_Warp_Shuffle.pdf
// https://stackoverflow.com/questions/66078814/is-cuda-atomicadd-operation-faster-than-launch-another-kernel-when-we-do-reduce
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
__syncthreads();
if (tid < s)
shr[tid] = ming(shr[tid], shr[tid + s]);
}
if (tid == 0)
dst[dst_id] = shr[0];
}
template <typename T>
__device__ void
fast_argmin(const size_t src_numel, const size_t el_to_sum_per_block,
const size_t num_dims, const size_t *info, const T *src, uint32_t *dst) {
const size_t *dims = info;
const size_t *strides = info + num_dims;
__shared__ T shr[BLOCK_SIZE];
__shared__ uint32_t shr_index[BLOCK_SIZE];
size_t tid = threadIdx.x;
size_t dst_id = blockIdx.x;
// Not sure how that works on uint32_t and uint8_t but it seems to do ok.
shr[tid] = INFINITY;
shr_index[tid] = 0xFFFFFFFF;
bool not_set = true;
// Elements summed in this block range from dst_id * el_to_sum_per_block
// to (dst_id + 1) * el_to_sum_per_block.
size_t start_idx = dst_id * el_to_sum_per_block;
size_t stop_idx = min(start_idx + el_to_sum_per_block, src_numel);
size_t idx = start_idx + tid;
while (idx < stop_idx) {
// TODO: Fast version for the contiguous case.
size_t strided_i = get_strided_index(idx, num_dims, dims, strides);
if (not_set || src[strided_i] < shr[tid]) {
shr[tid] = src[strided_i];
// Assume that the reduction takes place over the last dimension which is contiguous.
shr_index[tid] = idx % dims[num_dims - 1];
not_set = false;
}
idx += blockDim.x;
}
// Parallel reduction, see the slides:
// https://www.olcf.ornl.gov/wp-content/uploads/2019/12/05_Atomics_Reductions_Warp_Shuffle.pdf
// https://stackoverflow.com/questions/66078814/is-cuda-atomicadd-operation-faster-than-launch-another-kernel-when-we-do-reduce
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
__syncthreads();
if (tid < s && shr[tid + s] < shr[tid]) {
shr[tid] = shr[tid + s];
shr_index[tid] = shr_index[tid + s];
}
}
if (tid == 0)
dst[dst_id] = shr_index[0];
}
template <typename T>
__device__ void
fast_argmax(const size_t src_numel, const size_t el_to_sum_per_block,
const size_t num_dims, const size_t *info, const T *src, uint32_t *dst) {
const size_t *dims = info;
const size_t *strides = info + num_dims;
__shared__ T shr[BLOCK_SIZE];
__shared__ uint32_t shr_index[BLOCK_SIZE];
size_t tid = threadIdx.x;
size_t dst_id = blockIdx.x;
shr[tid] = -INFINITY;
shr_index[tid] = 0xFFFFFFFF;
bool not_set = true;
// Elements summed in this block range from dst_id * el_to_sum_per_block
// to (dst_id + 1) * el_to_sum_per_block.
size_t start_idx = dst_id * el_to_sum_per_block;
size_t stop_idx = min(start_idx + el_to_sum_per_block, src_numel);
size_t idx = start_idx + tid;
while (idx < stop_idx) {
// TODO: Fast version for the contiguous case.
size_t strided_i = get_strided_index(idx, num_dims, dims, strides);
if (not_set || src[strided_i] > shr[tid]) {
shr[tid] = src[strided_i];
// Assume that the reduction takes place over the last dimension which is contiguous.
shr_index[tid] = idx % dims[num_dims - 1];
not_set = false;
}
idx += blockDim.x;
}
// Parallel reduction, see the slides:
// https://www.olcf.ornl.gov/wp-content/uploads/2019/12/05_Atomics_Reductions_Warp_Shuffle.pdf
// https://stackoverflow.com/questions/66078814/is-cuda-atomicadd-operation-faster-than-launch-another-kernel-when-we-do-reduce
for (int s = blockDim.x / 2; s > 0; s >>= 1) {
__syncthreads();
if (tid < s && shr[tid + s] > shr[tid]) {
shr[tid] = shr[tid + s];
shr_index[tid] = shr_index[tid + s];
}
}
if (tid == 0)
dst[dst_id] = shr_index[0];
}
#define FAST_OP(TYPENAME, MIN_NAME, MAX_NAME, ARGMIN_NAME, ARGMAX_NAME, SUM_NAME) \
extern "C" __global__ void ARGMIN_NAME( \
const size_t src_numel, const size_t el_to_sum_per_block, \
const size_t num_dims, const size_t *info, const TYPENAME *src, \
uint32_t *dst) { \
fast_argmin(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
} \
extern "C" __global__ void ARGMAX_NAME( \
const size_t src_numel, const size_t el_to_sum_per_block, \
const size_t num_dims, const size_t *info, const TYPENAME *src, \
uint32_t *dst) { \
fast_argmax(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
} \
extern "C" __global__ void MIN_NAME( \
const size_t src_numel, const size_t el_to_sum_per_block, \
const size_t num_dims, const size_t *info, const TYPENAME *src, \
TYPENAME *dst) { \
fast_min(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
} \
extern "C" __global__ void MAX_NAME( \
const size_t src_numel, const size_t el_to_sum_per_block, \
const size_t num_dims, const size_t *info, const TYPENAME *src, \
TYPENAME *dst) { \
fast_max(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
} \
extern "C" __global__ void SUM_NAME( \
const size_t src_numel, const size_t el_to_sum_per_block, \
const size_t num_dims, const size_t *info, const TYPENAME *src, \
TYPENAME *dst) { \
fast_sum(src_numel, el_to_sum_per_block, num_dims, info, src, dst); \
}
#define SUM_OP(TYPENAME, FN_NAME) \
extern "C" __global__ void FN_NAME( \
const size_t numel, const size_t num_dims, const size_t num_sum_dims, \
const size_t *info, const TYPENAME *inp, TYPENAME *out) { \
const size_t *dims = info; \
const size_t *strides = info + num_dims; \
const size_t *sum_dims_l = info + 2 * num_dims; \
const size_t *sum_dims_s = info + 2 * num_dims + num_sum_dims; \
if (is_contiguous(num_dims, dims, strides)) { \
for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; \
i += blockDim.x * gridDim.x) { \
size_t dst_index = i; \
for (unsigned int nd = 0; nd < num_sum_dims; ++nd) { \
size_t stride = sum_dims_s[nd]; \
size_t pre = dst_index / stride; \
size_t post = dst_index % stride; \
dst_index = (pre / sum_dims_l[nd]) * stride + post; \
} \
atomicAdd(out + dst_index, inp[i]); \
} \
} else { \
for (unsigned int i = blockIdx.x * blockDim.x + threadIdx.x; i < numel; \
i += blockDim.x * gridDim.x) { \
unsigned strided_i = get_strided_index(i, num_dims, dims, strides); \
size_t dst_index = i; \
for (unsigned int nd = 0; nd < num_sum_dims; ++nd) { \
size_t stride = sum_dims_s[nd]; \
size_t pre = dst_index / stride; \
size_t post = dst_index % stride; \
dst_index = (pre / sum_dims_l[nd]) * stride + post; \
} \
atomicAdd(out + dst_index, inp[strided_i]); \
} \
} \
}
#define SOFTMAX_OP(TYPENAME, ACC_TYPENAME, FN_NAME) \
extern "C" __global__ void FN_NAME( \
const TYPENAME *src, TYPENAME *dst, \
const int n_cols) { \
softmax<TYPENAME, ACC_TYPENAME>(src, dst, n_cols); \
} \
#define RMSNORM_OP(TYPENAME, FN_NAME) \
extern "C" __global__ void FN_NAME( \
const TYPENAME *src, TYPENAME *dst, const TYPENAME *alpha, \
const int n_cols, const int block_size, const float eps) { \
rmsnorm<TYPENAME>(src, dst, alpha, n_cols, block_size, eps); \
} \
#define LAYERNORM_OP(TYPENAME, FN_NAME) \
extern "C" __global__ void FN_NAME( \
const TYPENAME *src, TYPENAME *dst, const TYPENAME *alpha, \
const TYPENAME *beta, const int n_cols, const int block_size, const float eps) { \
layernorm<TYPENAME>(src, dst, alpha, beta, n_cols, block_size, eps); \
} \
#define ROPE_OP(TYPENAME, FN_NAME, FN_NAME_I, FN_NAME_THD) \
extern "C" __global__ void FN_NAME_I( \
const TYPENAME *src, \
const TYPENAME *cos, \
const TYPENAME *sin, \
TYPENAME *dst, \
const uint32_t bh, \
const uint32_t td, \
const uint32_t stride_b) { \
ropei<TYPENAME>(src, cos, sin, dst, bh, td, stride_b); \
} \
extern "C" __global__ void FN_NAME( \
const TYPENAME *src, \
const TYPENAME *cos, \
const TYPENAME *sin, \
TYPENAME *dst, \
const uint32_t bh, \
const uint32_t td, \
const uint32_t d, \
const uint32_t stride_b) { \
rope<TYPENAME>(src, cos, sin, dst, bh, td, d, stride_b); \
} \
extern "C" __global__ void FN_NAME_THD( \
const TYPENAME *src, \
const TYPENAME *cos, \
const TYPENAME *sin, \
TYPENAME *dst, \
const uint32_t b, \
const uint32_t t, \
const uint32_t h, \
const uint32_t d, \
const uint32_t stride_b) { \
rope_thd<TYPENAME>(src, cos, sin, dst, b, t, h, d, stride_b); \
} \
#if __CUDA_ARCH__ >= 800
SOFTMAX_OP(__nv_bfloat16, float, softmax_bf16)
RMSNORM_OP(__nv_bfloat16, rmsnorm_bf16)
LAYERNORM_OP(__nv_bfloat16, layernorm_bf16)
ROPE_OP(__nv_bfloat16, rope_bf16, rope_i_bf16, rope_thd_bf16)
SUM_OP(__nv_bfloat16, sum_bf16)
FAST_OP(__nv_bfloat16, fast_min_bf16, fast_max_bf16, fast_argmin_bf16, fast_argmax_bf16, fast_sum_bf16)
// NOTE: No reduce ops for f8
// SUM_OP(__nv_fp8_e4m3, sum_fp8_e4m3)
// SOFTMAX_OP(__nv_fp8_e4m3, float, softmax_fp8_e4m3)
// RMSNORM_OP(__nv_fp8_e4m3, rmsnorm_fp8_e4m3)
// LAYERNORM_OP(__nv_fp8_e4m3, layernorm_fp8_e4m3)
// ROPE_OP(__nv_fp8_e4m3, rope_fp8_e4m3, rope_i_fp8_e4m3, rope_thd_fp8_e4m3)
// FAST_OP(__nv_fp8_e4m3, fast_min_fp8_e4m3, fast_max_fp8_e4m3, fast_argmin_fp8_e4m3, fast_argmax_fp8_e4m3, fast_sum_fp8_e4m3)
#endif
#if __CUDA_ARCH__ >= 530
SOFTMAX_OP(__half, float, softmax_f16)
RMSNORM_OP(__half, rmsnorm_f16)
LAYERNORM_OP(__half, layernorm_f16)
ROPE_OP(__half, rope_f16, rope_i_f16, rope_thd_f16)
SUM_OP(__half, sum_f16)
FAST_OP(__half, fast_min_f16, fast_max_f16, fast_argmin_f16, fast_argmax_f16, fast_sum_f16)
#endif
SUM_OP(float, sum_f32)
SUM_OP(double, sum_f64)
SUM_OP(uint32_t, sum_u32)
SOFTMAX_OP(float, float, softmax_f32)
SOFTMAX_OP(double, double, softmax_f64)
RMSNORM_OP(float, rmsnorm_f32)
RMSNORM_OP(double, rmsnorm_f64)
LAYERNORM_OP(float, layernorm_f32)
LAYERNORM_OP(double, layernorm_f64)
ROPE_OP(float, rope_f32, rope_i_f32, rope_thd_f32)
ROPE_OP(double, rope_f64, rope_i_f64, rope_thd_f64)
FAST_OP(float, fast_min_f32, fast_max_f32, fast_argmin_f32, fast_argmax_f32, fast_sum_f32)
FAST_OP(double, fast_min_f64, fast_max_f64, fast_argmin_f64, fast_argmax_f64, fast_sum_f64)
FAST_OP(uint32_t, fast_min_u32, fast_max_u32, fast_argmin_u32, fast_argmax_u32, fast_sum_u32)
FAST_OP(int64_t, fast_min_i64, fast_max_i64, fast_argmin_i64, fast_argmax_i64, fast_sum_i64)
FAST_OP(uint8_t, fast_min_u8, fast_max_u8, fast_argmin_u8, fast_argmax_u8, fast_sum_u8)
| candle/candle-kernels/src/reduce.cu/0 | {
"file_path": "candle/candle-kernels/src/reduce.cu",
"repo_id": "candle",
"token_count": 13341
} | 49 |
// The implementation below comes from MLX.
// https://github.com/ml-explore/mlx/blob/0cea88bcc5e98e81a24d92eed8870a6976999f05/mlx/backend/metal/kernels/sort.h
// Copyright © 2023-2024 Apple Inc.
#define MLX_MTL_CONST static constant constexpr const
#define MLX_MTL_LOOP_UNROLL _Pragma("clang loop unroll(full)")
#include <metal_stdlib>
using namespace metal;
typedef bfloat bfloat16_t;
// From utils.h
///////////////////////////////////////////////////////////////////////////////
// Type limits utils
///////////////////////////////////////////////////////////////////////////////
template <typename U>
struct Limits {
static const constant U max = metal::numeric_limits<U>::max();
static const constant U min = metal::numeric_limits<U>::min();
static const constant U finite_max = metal::numeric_limits<U>::max();
static const constant U finite_min = metal::numeric_limits<U>::min();
};
#define instantiate_default_limit(type) \
template <> \
struct Limits<type> { \
static constexpr constant type max = metal::numeric_limits<type>::max(); \
static constexpr constant type min = metal::numeric_limits<type>::min(); \
static constexpr constant type finite_max = \
metal::numeric_limits<type>::max(); \
static constexpr constant type finite_min = \
metal::numeric_limits<type>::min(); \
};
instantiate_default_limit(uint8_t);
instantiate_default_limit(uint16_t);
instantiate_default_limit(uint32_t);
instantiate_default_limit(uint64_t);
instantiate_default_limit(int8_t);
instantiate_default_limit(int16_t);
instantiate_default_limit(int32_t);
instantiate_default_limit(int64_t);
#define instantiate_float_limit(type) \
template <> \
struct Limits<type> { \
static constexpr constant type max = \
metal::numeric_limits<type>::infinity(); \
static constexpr constant type min = \
-metal::numeric_limits<type>::infinity(); \
static constexpr constant type finite_max = \
metal::numeric_limits<type>::max(); \
static constexpr constant type finite_min = \
-metal::numeric_limits<type>::max(); \
};
instantiate_float_limit(half);
instantiate_float_limit(float);
instantiate_float_limit(bfloat16_t);
template <>
struct Limits<bool> {
static constexpr constant bool max = true;
static constexpr constant bool min = false;
};
///////////////////////////////////////////////////////////////////////////////
// Single Array with generic dims
template <typename IdxT = int64_t>
METAL_FUNC IdxT elem_to_loc(
IdxT elem,
constant const int* shape,
constant const int64_t* strides,
int ndim) {
IdxT loc = 0;
for (int i = ndim - 1; i >= 0 && elem > 0; --i) {
loc += (elem % shape[i]) * IdxT(strides[i]);
elem /= shape[i];
}
return loc;
}
// Non templated version to handle arbitrary dims
template <typename IdxT = int64_t>
METAL_FUNC IdxT elem_to_loc(
uint3 elem,
constant const int* shape,
constant const int64_t* strides,
int ndim) {
IdxT loc =
elem.x * IdxT(strides[ndim - 1]) + elem.y * IdxT(strides[ndim - 2]);
for (int d = ndim - 3; d >= 0; --d) {
loc += (elem.z % shape[d]) * IdxT(strides[d]);
elem.z /= shape[d];
}
return loc;
}
// Instantiate a templated kernel.
// Extra args are used as template parameters:
// e.g. instantiate_kernel(binary_int, binary, a, b) ->
// [[host_name(binary_int)]] [kernel] binary<a, b>
#define instantiate_kernel(name, func, ...) \
template [[host_name( \
name)]] [[kernel]] decltype(func<__VA_ARGS__>) func<__VA_ARGS__>;
// Based on GPU merge sort algorithm at
// https://github.com/NVIDIA/cccl/tree/main/cub/cub
///////////////////////////////////////////////////////////////////////////////
// Thread-level sort
///////////////////////////////////////////////////////////////////////////////
template <typename T>
METAL_FUNC void thread_swap(thread T& a, thread T& b) {
T w = a;
a = b;
b = w;
}
template <typename T>
struct LessThan {
static constexpr constant T init = Limits<T>::max;
METAL_FUNC bool operator()(T a, T b) {
return a < b;
}
};
template <
typename val_t,
typename idx_t,
bool ARG_SORT,
short N_PER_THREAD,
typename CompareOp>
struct ThreadSort {
static METAL_FUNC void sort(
thread val_t (&vals)[N_PER_THREAD],
thread idx_t (&idxs)[N_PER_THREAD]) {
CompareOp op;
MLX_MTL_LOOP_UNROLL
for (short i = 0; i < N_PER_THREAD; ++i) {
MLX_MTL_LOOP_UNROLL
for (short j = i & 1; j < N_PER_THREAD - 1; j += 2) {
if (op(vals[j + 1], vals[j])) {
thread_swap(vals[j + 1], vals[j]);
thread_swap(idxs[j + 1], idxs[j]);
}
}
}
}
};
///////////////////////////////////////////////////////////////////////////////
// Threadgroup-level sort
///////////////////////////////////////////////////////////////////////////////
template <
typename val_t,
typename idx_t,
bool ARG_SORT,
short BLOCK_THREADS,
short N_PER_THREAD,
typename CompareOp>
struct BlockMergeSort {
using thread_sort_t =
ThreadSort<val_t, idx_t, ARG_SORT, N_PER_THREAD, CompareOp>;
static METAL_FUNC int merge_partition(
const threadgroup val_t* As,
const threadgroup val_t* Bs,
short A_sz,
short B_sz,
short sort_md) {
CompareOp op;
short A_st = max(0, sort_md - B_sz);
short A_ed = min(sort_md, A_sz);
while (A_st < A_ed) {
short md = A_st + (A_ed - A_st) / 2;
auto a = As[md];
auto b = Bs[sort_md - 1 - md];
if (op(b, a)) {
A_ed = md;
} else {
A_st = md + 1;
}
}
return A_ed;
}
static METAL_FUNC void merge_step(
const threadgroup val_t* As,
const threadgroup val_t* Bs,
const threadgroup idx_t* As_idx,
const threadgroup idx_t* Bs_idx,
short A_sz,
short B_sz,
thread val_t (&vals)[N_PER_THREAD],
thread idx_t (&idxs)[N_PER_THREAD]) {
CompareOp op;
short a_idx = 0;
short b_idx = 0;
for (int i = 0; i < N_PER_THREAD; ++i) {
auto a = As[a_idx];
auto b = Bs[b_idx];
bool pred = (b_idx < B_sz) && (a_idx >= A_sz || op(b, a));
vals[i] = pred ? b : a;
idxs[i] = pred ? Bs_idx[b_idx] : As_idx[a_idx];
b_idx += short(pred);
a_idx += short(!pred);
}
}
static METAL_FUNC void sort(
threadgroup val_t* tgp_vals [[threadgroup(0)]],
threadgroup idx_t* tgp_idxs [[threadgroup(1)]],
int size_sorted_axis,
uint3 lid [[thread_position_in_threadgroup]]) {
// Get thread location
int idx = lid.x * N_PER_THREAD;
// Load from shared memory
thread val_t thread_vals[N_PER_THREAD];
thread idx_t thread_idxs[N_PER_THREAD];
for (int i = 0; i < N_PER_THREAD; ++i) {
thread_vals[i] = tgp_vals[idx + i];
if (ARG_SORT) {
thread_idxs[i] = tgp_idxs[idx + i];
}
}
// Per thread sort
if (idx < size_sorted_axis) {
thread_sort_t::sort(thread_vals, thread_idxs);
}
// Do merges using threadgroup memory
for (int merge_threads = 2; merge_threads <= BLOCK_THREADS;
merge_threads *= 2) {
// Update threadgroup memory
threadgroup_barrier(mem_flags::mem_threadgroup);
for (int i = 0; i < N_PER_THREAD; ++i) {
tgp_vals[idx + i] = thread_vals[i];
if (ARG_SORT) {
tgp_idxs[idx + i] = thread_idxs[i];
}
}
threadgroup_barrier(mem_flags::mem_threadgroup);
// Find location in merge step
int merge_group = lid.x / merge_threads;
int merge_lane = lid.x % merge_threads;
int sort_sz = N_PER_THREAD * merge_threads;
int sort_st = N_PER_THREAD * merge_threads * merge_group;
// As = tgp_vals[A_st:A_ed] is sorted
// Bs = tgp_vals[B_st:B_ed] is sorted
int A_st = sort_st;
int A_ed = sort_st + sort_sz / 2;
int B_st = sort_st + sort_sz / 2;
int B_ed = sort_st + sort_sz;
const threadgroup val_t* As = tgp_vals + A_st;
const threadgroup val_t* Bs = tgp_vals + B_st;
int A_sz = A_ed - A_st;
int B_sz = B_ed - B_st;
// Find a partition of merge elements
// Ci = merge(As[partition:], Bs[sort_md - partition:])
// of size N_PER_THREAD for each merge lane i
// C = [Ci] is sorted
int sort_md = N_PER_THREAD * merge_lane;
int partition = merge_partition(As, Bs, A_sz, B_sz, sort_md);
As += partition;
Bs += sort_md - partition;
A_sz -= partition;
B_sz -= sort_md - partition;
const threadgroup idx_t* As_idx =
ARG_SORT ? tgp_idxs + A_st + partition : nullptr;
const threadgroup idx_t* Bs_idx =
ARG_SORT ? tgp_idxs + B_st + sort_md - partition : nullptr;
// Merge starting at the partition and store results in thread registers
merge_step(As, Bs, As_idx, Bs_idx, A_sz, B_sz, thread_vals, thread_idxs);
}
// Write out to shared memory
threadgroup_barrier(mem_flags::mem_threadgroup);
for (int i = 0; i < N_PER_THREAD; ++i) {
tgp_vals[idx + i] = thread_vals[i];
if (ARG_SORT) {
tgp_idxs[idx + i] = thread_idxs[i];
}
}
}
};
///////////////////////////////////////////////////////////////////////////////
// Kernel sort
///////////////////////////////////////////////////////////////////////////////
template <
typename T,
typename U,
bool ARG_SORT,
short BLOCK_THREADS,
short N_PER_THREAD,
typename CompareOp = LessThan<T>>
struct KernelMergeSort {
using val_t = T;
using idx_t = uint;
using block_merge_sort_t = BlockMergeSort<
val_t,
idx_t,
ARG_SORT,
BLOCK_THREADS,
N_PER_THREAD,
CompareOp>;
MLX_MTL_CONST short N_PER_BLOCK = BLOCK_THREADS * N_PER_THREAD;
static METAL_FUNC void block_sort(
const device T* inp,
device U* out,
const constant int& size_sorted_axis,
const constant int& in_stride_sorted_axis,
const constant int& out_stride_sorted_axis,
const constant int& in_stride_segment_axis,
const constant int& out_stride_segment_axis,
threadgroup val_t* tgp_vals,
threadgroup idx_t* tgp_idxs,
uint3 tid [[threadgroup_position_in_grid]],
uint3 lid [[thread_position_in_threadgroup]]) {
// tid.y tells us the segment index
inp += tid.y * in_stride_segment_axis;
out += tid.y * out_stride_segment_axis;
// Copy into threadgroup memory
for (short i = lid.x; i < N_PER_BLOCK; i += BLOCK_THREADS) {
tgp_vals[i] = i < size_sorted_axis ? inp[i * in_stride_sorted_axis]
: val_t(CompareOp::init);
if (ARG_SORT) {
tgp_idxs[i] = i;
}
}
// Sort elements within the block
threadgroup_barrier(mem_flags::mem_threadgroup);
block_merge_sort_t::sort(tgp_vals, tgp_idxs, size_sorted_axis, lid);
threadgroup_barrier(mem_flags::mem_threadgroup);
// Write output
for (int i = lid.x; i < size_sorted_axis; i += BLOCK_THREADS) {
if (ARG_SORT) {
out[i * out_stride_sorted_axis] = tgp_idxs[i];
} else {
out[i * out_stride_sorted_axis] = tgp_vals[i];
}
}
}
};
template <
typename T,
typename U,
bool ARG_SORT,
short BLOCK_THREADS,
short N_PER_THREAD>
[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void block_sort(
const device T* inp [[buffer(0)]],
device U* out [[buffer(1)]],
const constant int& size_sorted_axis [[buffer(2)]],
const constant int& in_stride_sorted_axis [[buffer(3)]],
const constant int& out_stride_sorted_axis [[buffer(4)]],
const constant int& in_stride_segment_axis [[buffer(5)]],
const constant int& out_stride_segment_axis [[buffer(6)]],
uint3 tid [[threadgroup_position_in_grid]],
uint3 lid [[thread_position_in_threadgroup]]) {
using sort_kernel =
KernelMergeSort<T, U, ARG_SORT, BLOCK_THREADS, N_PER_THREAD>;
using val_t = typename sort_kernel::val_t;
using idx_t = typename sort_kernel::idx_t;
if (ARG_SORT) {
threadgroup val_t tgp_vals[sort_kernel::N_PER_BLOCK];
threadgroup idx_t tgp_idxs[sort_kernel::N_PER_BLOCK];
sort_kernel::block_sort(
inp,
out,
size_sorted_axis,
in_stride_sorted_axis,
out_stride_sorted_axis,
in_stride_segment_axis,
out_stride_segment_axis,
tgp_vals,
tgp_idxs,
tid,
lid);
} else {
threadgroup val_t tgp_vals[sort_kernel::N_PER_BLOCK];
sort_kernel::block_sort(
inp,
out,
size_sorted_axis,
in_stride_sorted_axis,
out_stride_sorted_axis,
in_stride_segment_axis,
out_stride_segment_axis,
tgp_vals,
nullptr,
tid,
lid);
}
}
constant constexpr const int zero_helper = 0;
template <
typename T,
typename U,
bool ARG_SORT,
short BLOCK_THREADS,
short N_PER_THREAD>
[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void block_sort_nc(
const device T* inp [[buffer(0)]],
device U* out [[buffer(1)]],
const constant int& size_sorted_axis [[buffer(2)]],
const constant int& in_stride_sorted_axis [[buffer(3)]],
const constant int& out_stride_sorted_axis [[buffer(4)]],
const constant int& nc_dim [[buffer(5)]],
const constant int* nc_shape [[buffer(6)]],
const constant int64_t* in_nc_strides [[buffer(7)]],
const constant int64_t* out_nc_strides [[buffer(8)]],
uint3 tid [[threadgroup_position_in_grid]],
uint3 lid [[thread_position_in_threadgroup]]) {
using sort_kernel =
KernelMergeSort<T, U, ARG_SORT, BLOCK_THREADS, N_PER_THREAD>;
using val_t = typename sort_kernel::val_t;
using idx_t = typename sort_kernel::idx_t;
auto in_block_idx = elem_to_loc(tid.y, nc_shape, in_nc_strides, nc_dim);
auto out_block_idx = elem_to_loc(tid.y, nc_shape, out_nc_strides, nc_dim);
inp += in_block_idx;
out += out_block_idx;
if (ARG_SORT) {
threadgroup val_t tgp_vals[sort_kernel::N_PER_BLOCK];
threadgroup idx_t tgp_idxs[sort_kernel::N_PER_BLOCK];
sort_kernel::block_sort(
inp,
out,
size_sorted_axis,
in_stride_sorted_axis,
out_stride_sorted_axis,
zero_helper,
zero_helper,
tgp_vals,
tgp_idxs,
tid,
lid);
} else {
threadgroup val_t tgp_vals[sort_kernel::N_PER_BLOCK];
sort_kernel::block_sort(
inp,
out,
size_sorted_axis,
in_stride_sorted_axis,
out_stride_sorted_axis,
zero_helper,
zero_helper,
tgp_vals,
nullptr,
tid,
lid);
}
}
template <
typename val_t,
typename idx_t,
bool ARG_SORT,
short BLOCK_THREADS,
short N_PER_THREAD,
typename CompareOp = LessThan<val_t>>
struct KernelMultiBlockMergeSort {
using block_merge_sort_t = BlockMergeSort<
val_t,
idx_t,
ARG_SORT,
BLOCK_THREADS,
N_PER_THREAD,
CompareOp>;
MLX_MTL_CONST short N_PER_BLOCK = BLOCK_THREADS * N_PER_THREAD;
static METAL_FUNC void block_sort(
const device val_t* inp,
device val_t* out_vals,
device idx_t* out_idxs,
const constant int& size_sorted_axis,
const constant int& stride_sorted_axis,
threadgroup val_t* tgp_vals,
threadgroup idx_t* tgp_idxs,
uint3 tid [[threadgroup_position_in_grid]],
uint3 lid [[thread_position_in_threadgroup]]) {
// tid.y tells us the segment index
int base_idx = tid.x * N_PER_BLOCK;
// Copy into threadgroup memory
for (short i = lid.x; i < N_PER_BLOCK; i += BLOCK_THREADS) {
int idx = base_idx + i;
tgp_vals[i] = idx < size_sorted_axis ? inp[idx * stride_sorted_axis]
: val_t(CompareOp::init);
tgp_idxs[i] = idx;
}
// Sort elements within the block
threadgroup_barrier(mem_flags::mem_threadgroup);
block_merge_sort_t::sort(tgp_vals, tgp_idxs, size_sorted_axis, lid);
threadgroup_barrier(mem_flags::mem_threadgroup);
// Write output
for (int i = lid.x; i < N_PER_BLOCK; i += BLOCK_THREADS) {
int idx = base_idx + i;
if (idx < size_sorted_axis) {
out_vals[idx] = tgp_vals[i];
out_idxs[idx] = tgp_idxs[i];
}
}
}
static METAL_FUNC int merge_partition(
const device val_t* As,
const device val_t* Bs,
int A_sz,
int B_sz,
int sort_md) {
CompareOp op;
int A_st = max(0, sort_md - B_sz);
int A_ed = min(sort_md, A_sz);
while (A_st < A_ed) {
int md = A_st + (A_ed - A_st) / 2;
auto a = As[md];
auto b = Bs[sort_md - 1 - md];
if (op(b, a)) {
A_ed = md;
} else {
A_st = md + 1;
}
}
return A_ed;
}
};
template <
typename val_t,
typename idx_t,
bool ARG_SORT,
short BLOCK_THREADS,
short N_PER_THREAD>
[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void mb_block_sort(
const device val_t* inp [[buffer(0)]],
device val_t* out_vals [[buffer(1)]],
device idx_t* out_idxs [[buffer(2)]],
const constant int& size_sorted_axis [[buffer(3)]],
const constant int& stride_sorted_axis [[buffer(4)]],
const constant int& nc_dim [[buffer(5)]],
const constant int* nc_shape [[buffer(6)]],
const constant int64_t* nc_strides [[buffer(7)]],
uint3 tid [[threadgroup_position_in_grid]],
uint3 lid [[thread_position_in_threadgroup]]) {
using sort_kernel = KernelMultiBlockMergeSort<
val_t,
idx_t,
ARG_SORT,
BLOCK_THREADS,
N_PER_THREAD>;
auto block_idx = elem_to_loc(tid.y, nc_shape, nc_strides, nc_dim);
inp += block_idx;
out_vals += tid.y * size_sorted_axis;
out_idxs += tid.y * size_sorted_axis;
threadgroup val_t tgp_vals[sort_kernel::N_PER_BLOCK];
threadgroup idx_t tgp_idxs[sort_kernel::N_PER_BLOCK];
sort_kernel::block_sort(
inp,
out_vals,
out_idxs,
size_sorted_axis,
stride_sorted_axis,
tgp_vals,
tgp_idxs,
tid,
lid);
}
template <
typename val_t,
typename idx_t,
bool ARG_SORT,
short BLOCK_THREADS,
short N_PER_THREAD>
[[kernel]] void mb_block_partition(
device idx_t* block_partitions [[buffer(0)]],
const device val_t* dev_vals [[buffer(1)]],
const device idx_t* dev_idxs [[buffer(2)]],
const constant int& size_sorted_axis [[buffer(3)]],
const constant int& merge_tiles [[buffer(4)]],
const constant int& n_blocks [[buffer(5)]],
uint3 tid [[threadgroup_position_in_grid]],
uint3 lid [[thread_position_in_threadgroup]],
uint3 tgp_dims [[threads_per_threadgroup]]) {
using sort_kernel = KernelMultiBlockMergeSort<
val_t,
idx_t,
ARG_SORT,
BLOCK_THREADS,
N_PER_THREAD>;
block_partitions += tid.y * tgp_dims.x;
dev_vals += tid.y * size_sorted_axis;
dev_idxs += tid.y * size_sorted_axis;
for (int i = lid.x; i <= n_blocks; i += tgp_dims.x) {
// Find location in merge step
int merge_group = i / merge_tiles;
int merge_lane = i % merge_tiles;
int sort_sz = sort_kernel::N_PER_BLOCK * merge_tiles;
int sort_st = sort_kernel::N_PER_BLOCK * merge_tiles * merge_group;
int A_st = min(size_sorted_axis, sort_st);
int A_ed = min(size_sorted_axis, sort_st + sort_sz / 2);
int B_st = A_ed;
int B_ed = min(size_sorted_axis, B_st + sort_sz / 2);
int partition_at = min(B_ed - A_st, sort_kernel::N_PER_BLOCK * merge_lane);
int partition = sort_kernel::merge_partition(
dev_vals + A_st,
dev_vals + B_st,
A_ed - A_st,
B_ed - B_st,
partition_at);
block_partitions[i] = A_st + partition;
}
}
template <
typename val_t,
typename idx_t,
bool ARG_SORT,
short BLOCK_THREADS,
short N_PER_THREAD,
typename CompareOp = LessThan<val_t>>
[[kernel, max_total_threads_per_threadgroup(BLOCK_THREADS)]] void
mb_block_merge(
const device idx_t* block_partitions [[buffer(0)]],
const device val_t* dev_vals_in [[buffer(1)]],
const device idx_t* dev_idxs_in [[buffer(2)]],
device val_t* dev_vals_out [[buffer(3)]],
device idx_t* dev_idxs_out [[buffer(4)]],
const constant int& size_sorted_axis [[buffer(5)]],
const constant int& merge_tiles [[buffer(6)]],
const constant int& num_tiles [[buffer(7)]],
uint3 tid [[threadgroup_position_in_grid]],
uint3 lid [[thread_position_in_threadgroup]]) {
using sort_kernel = KernelMultiBlockMergeSort<
val_t,
idx_t,
ARG_SORT,
BLOCK_THREADS,
N_PER_THREAD,
CompareOp>;
using block_sort_t = typename sort_kernel::block_merge_sort_t;
block_partitions += tid.y * (num_tiles + 1);
dev_vals_in += tid.y * size_sorted_axis;
dev_idxs_in += tid.y * size_sorted_axis;
dev_vals_out += tid.y * size_sorted_axis;
dev_idxs_out += tid.y * size_sorted_axis;
int block_idx = tid.x;
int merge_group = block_idx / merge_tiles;
int sort_st = sort_kernel::N_PER_BLOCK * merge_tiles * merge_group;
int sort_sz = sort_kernel::N_PER_BLOCK * merge_tiles;
int sort_md = sort_kernel::N_PER_BLOCK * block_idx - sort_st;
int A_st = block_partitions[block_idx + 0];
int A_ed = block_partitions[block_idx + 1];
int B_st = min(size_sorted_axis, 2 * sort_st + sort_sz / 2 + sort_md - A_st);
int B_ed = min(
size_sorted_axis,
2 * sort_st + sort_sz / 2 + sort_md + sort_kernel::N_PER_BLOCK - A_ed);
if ((block_idx % merge_tiles) == merge_tiles - 1) {
A_ed = min(size_sorted_axis, sort_st + sort_sz / 2);
B_ed = min(size_sorted_axis, sort_st + sort_sz);
}
int A_sz = A_ed - A_st;
int B_sz = B_ed - B_st;
// Load from global memory
thread val_t thread_vals[N_PER_THREAD];
thread idx_t thread_idxs[N_PER_THREAD];
for (int i = 0; i < N_PER_THREAD; i++) {
int idx = BLOCK_THREADS * i + lid.x;
if (idx < (A_sz + B_sz)) {
thread_vals[i] = (idx < A_sz) ? dev_vals_in[A_st + idx]
: dev_vals_in[B_st + idx - A_sz];
thread_idxs[i] = (idx < A_sz) ? dev_idxs_in[A_st + idx]
: dev_idxs_in[B_st + idx - A_sz];
} else {
thread_vals[i] = CompareOp::init;
thread_idxs[i] = 0;
}
}
// Write to shared memory
threadgroup val_t tgp_vals[sort_kernel::N_PER_BLOCK];
threadgroup idx_t tgp_idxs[sort_kernel::N_PER_BLOCK];
threadgroup_barrier(mem_flags::mem_threadgroup);
for (int i = 0; i < N_PER_THREAD; i++) {
int idx = BLOCK_THREADS * i + lid.x;
tgp_vals[idx] = thread_vals[i];
tgp_idxs[idx] = thread_idxs[i];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
// Merge
int sort_md_local = min(A_sz + B_sz, N_PER_THREAD * int(lid.x));
int A_st_local = block_sort_t::merge_partition(
tgp_vals, tgp_vals + A_sz, A_sz, B_sz, sort_md_local);
int A_ed_local = A_sz;
int B_st_local = sort_md_local - A_st_local;
int B_ed_local = B_sz;
int A_sz_local = A_ed_local - A_st_local;
int B_sz_local = B_ed_local - B_st_local;
// Do merge
block_sort_t::merge_step(
tgp_vals + A_st_local,
tgp_vals + A_ed_local + B_st_local,
tgp_idxs + A_st_local,
tgp_idxs + A_ed_local + B_st_local,
A_sz_local,
B_sz_local,
thread_vals,
thread_idxs);
threadgroup_barrier(mem_flags::mem_threadgroup);
for (int i = 0; i < N_PER_THREAD; ++i) {
int idx = lid.x * N_PER_THREAD;
tgp_vals[idx + i] = thread_vals[i];
tgp_idxs[idx + i] = thread_idxs[i];
}
threadgroup_barrier(mem_flags::mem_threadgroup);
// Write output
int base_idx = tid.x * sort_kernel::N_PER_BLOCK;
for (int i = lid.x; i < sort_kernel::N_PER_BLOCK; i += BLOCK_THREADS) {
int idx = base_idx + i;
if (idx < size_sorted_axis) {
dev_vals_out[idx] = tgp_vals[i];
dev_idxs_out[idx] = tgp_idxs[i];
}
}
}
#define instantiate_block_sort( \
name, itname, itype, otname, otype, arg_sort, bn, tn) \
instantiate_kernel("c" #name "_" #itname "_" #otname "_bn" #bn "_tn" #tn, \
block_sort, itype, otype, arg_sort, bn, tn) \
instantiate_kernel("nc" #name "_" #itname "_" #otname "_bn" #bn "_tn" #tn, \
block_sort_nc, itype, otype, arg_sort, bn, tn)
#define instantiate_arg_block_sort_base(itname, itype, bn, tn) \
instantiate_block_sort( \
arg_block_sort, itname, itype, uint32, uint32_t, true, bn, tn)
#define instantiate_block_sort_base(itname, itype, bn, tn) \
instantiate_block_sort( \
_block_sort, itname, itype, itname, itype, false, bn, tn)
#define instantiate_block_sort_tn(itname, itype, bn) \
instantiate_block_sort_base(itname, itype, bn, 8) \
instantiate_arg_block_sort_base(itname, itype, bn, 8)
#define instantiate_block_sort_bn(itname, itype) \
instantiate_block_sort_tn(itname, itype, 128) \
instantiate_block_sort_tn(itname, itype, 256) \
instantiate_block_sort_tn(itname, itype, 512)
instantiate_block_sort_bn(uint8, uint8_t)
instantiate_block_sort_bn(uint32, uint32_t)
instantiate_block_sort_bn(float16, half)
instantiate_block_sort_bn(float32, float)
instantiate_block_sort_bn(bfloat16, bfloat16_t)
#define instantiate_block_sort_long(itname, itype) \
instantiate_block_sort_tn(itname, itype, 128) \
instantiate_block_sort_tn(itname, itype, 256)
instantiate_block_sort_long(int64, int64_t)
#define instantiate_multi_block_sort( \
vtname, vtype, itname, itype, arg_sort, bn, tn) \
instantiate_kernel("sort_mbsort_" #vtname "_" #itname "_bn" #bn "_tn" #tn, \
mb_block_sort, vtype, itype, arg_sort, bn, tn) \
instantiate_kernel("partition_mbsort_" #vtname "_" #itname "_bn" #bn "_tn" #tn, \
mb_block_partition, vtype, itype, arg_sort, bn, tn) \
instantiate_kernel("merge_mbsort_" #vtname "_" #itname "_bn" #bn "_tn" #tn, \
mb_block_merge, vtype, itype, arg_sort, bn, tn)
#define instantiate_multi_block_sort_base(vtname, vtype) \
instantiate_multi_block_sort(vtname, vtype, uint32, uint32_t, true, 512, 8)
instantiate_multi_block_sort_base(uint8, uint8_t)
instantiate_multi_block_sort_base(uint32, uint32_t)
instantiate_multi_block_sort_base(float16, half)
instantiate_multi_block_sort_base(float32, float)
instantiate_multi_block_sort_base(bfloat16, bfloat16_t)
#define instantiate_multi_block_sort_long(vtname, vtype) \
instantiate_multi_block_sort(vtname, vtype, uint32, uint32_t, true, 256, 8)
instantiate_multi_block_sort_long(int64, int64_t) // clang-format on
| candle/candle-metal-kernels/src/mlx_sort.metal/0 | {
"file_path": "candle/candle-metal-kernels/src/mlx_sort.metal",
"repo_id": "candle",
"token_count": 12675
} | 50 |
[package]
name = "candle-nn"
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 }
half = { workspace = true }
thiserror = { workspace = true }
intel-mkl-src = { workspace = true, optional = true }
num-traits = { workspace = true }
rayon = { workspace = true }
safetensors = { workspace = true }
serde = { workspace = true }
metal = { workspace = true, optional = true }
candle-metal-kernels = { workspace = true, optional = true }
[dev-dependencies]
anyhow = { workspace = true }
clap = { workspace = true }
rand = { workspace = true }
rand_distr = { workspace = true }
criterion = { workspace = true }
[features]
default = []
accelerate = ["dep:accelerate-src", "candle/accelerate"]
cuda = ["candle/cuda"]
cudnn = ["candle/cudnn"]
mkl = ["dep:intel-mkl-src", "candle/mkl"]
metal = ["candle/metal", "dep:candle-metal-kernels", "dep:metal"]
[[bench]]
name = "bench_main"
harness = false
| candle/candle-nn/Cargo.toml/0 | {
"file_path": "candle/candle-nn/Cargo.toml",
"repo_id": "candle",
"token_count": 384
} | 51 |
//! Variable initialization.
// This is based on:
// https://github.com/pytorch/pytorch/blob/07107919297db3f8ab37f11c12666b6d6d5f692e/torch/nn/init.py#
use candle::{DType, Device, Result, Shape, Tensor, Var};
/// Number of features as input or output of a layer.
/// In Kaiming initialization, choosing `FanIn` preserves
/// the magnitude of the variance of the weights in the
/// forward pass, choosing `FanOut` preserves this
/// magnitude in the backward pass.
#[derive(Debug, Copy, Clone)]
pub enum FanInOut {
FanIn,
FanOut,
}
impl FanInOut {
/// Compute the fan-in or fan-out value for a weight tensor of
/// the specified dimensions.
/// <https://github.com/pytorch/pytorch/blob/dbeacf11820e336e803bb719b7aaaf2125ae4d9c/torch/nn/init.py#L284>
pub fn for_shape(&self, shape: &Shape) -> usize {
let dims = shape.dims();
let receptive_field_size: usize = dims.iter().skip(2).product();
match &self {
FanInOut::FanIn => {
if dims.len() < 2 {
1
} else {
dims[1] * receptive_field_size
}
}
FanInOut::FanOut => {
if dims.is_empty() {
1
} else {
dims[0] * receptive_field_size
}
}
}
}
}
#[derive(Debug, Copy, Clone)]
pub enum NormalOrUniform {
Normal,
Uniform,
}
/// The non-linear function that follows this layer. ReLU is the
/// recommended value.
#[derive(Debug, Copy, Clone)]
pub enum NonLinearity {
ReLU,
Linear,
Sigmoid,
Tanh,
SELU,
ExplicitGain(f64),
}
impl NonLinearity {
// https://github.com/pytorch/pytorch/blob/07107919297db3f8ab37f11c12666b6d6d5f692e/torch/nn/init.py#L67
pub fn gain(&self) -> f64 {
match *self {
NonLinearity::ReLU => 2f64.sqrt(),
NonLinearity::Tanh => 5. / 3.,
NonLinearity::Linear | NonLinearity::Sigmoid => 1.,
NonLinearity::SELU => 0.75,
NonLinearity::ExplicitGain(g) => g,
}
}
}
/// Variable initializations.
#[derive(Debug, Copy, Clone)]
pub enum Init {
/// Constant value.
Const(f64),
/// Random normal with some mean and standard deviation.
Randn { mean: f64, stdev: f64 },
/// Uniform initialization between some lower and upper bounds.
Uniform { lo: f64, up: f64 },
/// Kaiming uniform initialization.
/// See "Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification"
/// He, K. et al. (2015). This uses a uniform distribution.
Kaiming {
dist: NormalOrUniform,
fan: FanInOut,
non_linearity: NonLinearity,
},
}
pub const ZERO: Init = Init::Const(0.);
pub const ONE: Init = Init::Const(1.);
pub const DEFAULT_KAIMING_UNIFORM: Init = Init::Kaiming {
dist: NormalOrUniform::Uniform,
fan: FanInOut::FanIn,
non_linearity: NonLinearity::ReLU,
};
pub const DEFAULT_KAIMING_NORMAL: Init = Init::Kaiming {
dist: NormalOrUniform::Normal,
fan: FanInOut::FanIn,
non_linearity: NonLinearity::ReLU,
};
impl Init {
/// Creates a new tensor with the specified shape, device, and initialization.
pub fn var<S: Into<Shape>>(&self, s: S, dtype: DType, device: &Device) -> Result<Var> {
match self {
Self::Const(v) if *v == 0. => Var::zeros(s, dtype, device),
Self::Const(v) if *v == 1. => Var::ones(s, dtype, device),
Self::Const(cst) => {
Var::from_tensor(&Tensor::ones(s, dtype, device)?.affine(*cst, 0.)?)
}
Self::Uniform { lo, up } => Var::rand_f64(*lo, *up, s, dtype, device),
Self::Randn { mean, stdev } => Var::randn_f64(*mean, *stdev, s, dtype, device),
Self::Kaiming {
dist,
fan,
non_linearity,
} => {
let s = s.into();
let fan = fan.for_shape(&s);
let gain = non_linearity.gain();
let std = gain / (fan as f64).sqrt();
match dist {
NormalOrUniform::Uniform => {
let bound = 3f64.sqrt() * std;
Var::rand_f64(-bound, bound, s, dtype, device)
}
NormalOrUniform::Normal => Var::randn_f64(0., std, s, dtype, device),
}
}
}
}
}
impl Default for Init {
fn default() -> Self {
Self::Const(0.)
}
}
| candle/candle-nn/src/init.rs/0 | {
"file_path": "candle/candle-nn/src/init.rs",
"repo_id": "candle",
"token_count": 2212
} | 52 |
#[cfg(feature = "mkl")]
extern crate intel_mkl_src;
#[cfg(feature = "accelerate")]
extern crate accelerate_src;
use candle::{Device, Result, Tensor};
#[test]
fn kv_cache() -> Result<()> {
let mut cache = candle_nn::kv_cache::Cache::new(0, 16);
for _ in [0, 1] {
assert_eq!(cache.current_seq_len(), 0);
let data = cache.current_data()?;
assert!(data.is_none());
let t = Tensor::new(&[1f32, 2., 3.], &Device::Cpu)?;
cache.append(&t)?;
let data = cache.current_data()?.unwrap();
assert_eq!(data.to_vec1::<f32>()?, [1., 2., 3.]);
let t = Tensor::new(&[4f32], &Device::Cpu)?;
cache.append(&t)?;
let data = cache.current_data()?.unwrap();
assert_eq!(data.to_vec1::<f32>()?, [1., 2., 3., 4.]);
let t = Tensor::new(&[0f32, 5., 6., 7.], &Device::Cpu)?;
cache.append(&t)?;
let data = cache.current_data()?.unwrap();
assert_eq!(data.to_vec1::<f32>()?, [1., 2., 3., 4., 0., 5., 6., 7.]);
assert_eq!(cache.current_seq_len(), 8);
cache.reset();
}
Ok(())
}
#[test]
fn rotating_kv_cache() -> Result<()> {
let mut cache = candle_nn::kv_cache::RotatingCache::new(0, 6);
for _ in [0, 1] {
assert_eq!(cache.offset(), 0);
assert_eq!(cache.current_seq_len(), 0);
let data = cache.current_data()?;
assert!(data.is_none());
assert_eq!(cache.positions(1), &[0]);
assert_eq!(cache.positions(2), &[0, 1]);
let t = Tensor::new(&[1., 2., 3.], &Device::Cpu)?;
let data = cache.append(&t)?;
assert_eq!(data.to_vec1::<f64>()?, [1., 2., 3.]);
assert_eq!(cache.positions(0), &[0, 1, 2]);
assert_eq!(cache.positions(1), &[0, 1, 2, 3]);
assert_eq!(cache.positions(2), &[0, 1, 2, 3, 4]);
assert_eq!(cache.positions(3), &[0, 1, 2, 3, 4, 5]);
assert_eq!(cache.positions(4), &[6, 1, 2, 3, 4, 5]);
let t = Tensor::new(&[4.], &Device::Cpu)?;
let data = cache.append(&t)?;
assert_eq!(data.to_vec1::<f64>()?, [1., 2., 3., 4.]);
let t = Tensor::new(&[0., 5., 6., 7.], &Device::Cpu)?;
let data = cache.append(&t)?;
assert_eq!(data.to_vec1::<f64>()?, [6., 7., 3., 4., 0., 5.]);
assert_eq!(cache.current_seq_len(), 8);
assert_eq!(cache.offset(), 2);
let t = Tensor::new(&[8.], &Device::Cpu)?;
let data = cache.append(&t)?;
assert_eq!(data.to_vec1::<f64>()?, [6., 7., 8., 4., 0., 5.]);
assert_eq!(cache.current_seq_len(), 9);
assert_eq!(cache.offset(), 3);
let t = Tensor::new(&[9., 10., 11.], &Device::Cpu)?;
let data = cache.append(&t)?;
assert_eq!(data.to_vec1::<f64>()?, [6., 7., 8., 9., 10., 11.]);
assert_eq!(cache.current_seq_len(), 12);
assert_eq!(cache.offset(), 0);
let t = Tensor::new(&[12.], &Device::Cpu)?;
let data = cache.append(&t)?;
assert_eq!(data.to_vec1::<f64>()?, [12., 7., 8., 9., 10., 11.]);
assert_eq!(cache.current_seq_len(), 13);
assert_eq!(cache.offset(), 1);
let mask = cache.attn_mask(2, &Device::Cpu)?.unwrap();
assert_eq!(
mask.to_vec2::<u8>()?,
&[[0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
);
let mask = cache.attn_mask(3, &Device::Cpu)?.unwrap();
assert_eq!(
mask.to_vec2::<u8>()?,
&[[0, 0, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0]],
);
assert_eq!(cache.positions(0), &[12, 7, 8, 9, 10, 11]);
assert_eq!(cache.positions(2), &[12, 13, 14, 9, 10, 11]);
assert_eq!(cache.positions(3), &[12, 13, 14, 15, 10, 11]);
assert_eq!(cache.positions(8), &[13, 14, 15, 16, 17, 18, 19, 20]);
let t = Tensor::new(&[0., 1., 2., 3., 4., 5., 6., 7., 8.], &Device::Cpu)?;
let data = cache.append(&t)?;
assert_eq!(data.to_vec1::<f64>()?, [0., 1., 2., 3., 4., 5., 6., 7., 8.]);
assert_eq!(cache.current_seq_len(), 22);
assert_eq!(cache.offset(), 0);
assert_eq!(cache.positions(0), &[16, 17, 18, 19, 20, 21]);
assert_eq!(cache.positions(1), &[22, 17, 18, 19, 20, 21]);
let mask = cache.attn_mask(1, &Device::Cpu)?;
assert!(mask.is_none());
let mask = cache.attn_mask(2, &Device::Cpu)?.unwrap();
assert_eq!(
mask.to_vec2::<u8>()?,
&[[0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
);
let mask = cache.attn_mask(3, &Device::Cpu)?.unwrap();
assert_eq!(
mask.to_vec2::<u8>()?,
&[[0, 1, 1, 0, 0, 0], [0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0]]
);
let t = Tensor::new(&[42.], &Device::Cpu)?;
let data = cache.append(&t)?;
assert_eq!(data.to_vec1::<f64>()?, [42., 4., 5., 6., 7., 8.]);
assert_eq!(cache.current_seq_len(), 23);
assert_eq!(cache.offset(), 1);
cache.reset();
}
Ok(())
}
| candle/candle-nn/tests/kv_cache.rs/0 | {
"file_path": "candle/candle-nn/tests/kv_cache.rs",
"repo_id": "candle",
"token_count": 2618
} | 53 |
[package]
name = "candle-pyo3"
version.workspace = true
edition.workspace = true
description.workspace = true
repository.workspace = true
keywords.workspace = true
categories.workspace = true
license.workspace = true
readme = "README.md"
[lib]
name = "candle"
crate-type = ["cdylib"]
[dependencies]
accelerate-src = { workspace = true, optional = true }
candle = { workspace = true }
candle-nn = { workspace = true }
candle-onnx = { workspace = true, optional = true }
half = { workspace = true }
intel-mkl-src = { workspace = true, optional = true }
pyo3 = { version = "0.22.0", features = ["extension-module", "abi3-py311"] }
float8 = { workspace = true }
[build-dependencies]
pyo3-build-config = "0.22"
[features]
default = []
accelerate = ["dep:accelerate-src", "candle/accelerate"]
cuda = ["candle/cuda"]
mkl = ["dep:intel-mkl-src", "candle/mkl"]
onnx = ["dep:candle-onnx"]
| candle/candle-pyo3/Cargo.toml/0 | {
"file_path": "candle/candle-pyo3/Cargo.toml",
"repo_id": "candle",
"token_count": 325
} | 54 |
from candle import Tensor, QTensor, DType
from typing import (
Dict,
Tuple,
Any,
Optional,
Union,
Iterator,
Set,
overload,
Mapping,
TypeVar,
List,
)
from collections import OrderedDict, namedtuple
TensorLike = Union[Tensor, QTensor]
T = TypeVar("T", bound="Module")
class _IncompatibleKeys(namedtuple("IncompatibleKeys", ["missing_keys", "unexpected_keys"])):
def __repr__(self):
if not self.missing_keys and not self.unexpected_keys:
return "<All keys matched successfully>"
return super().__repr__()
__str__ = __repr__
# see: https://github.com/pytorch/pytorch/blob/main/torch/nn/modules/module.py
class Module:
"""
Pytorch like Module.
Base class for all neural network modules.
Your models should also subclass this class.
"""
_modules: Dict[str, Optional["Module"]]
_buffers: Dict[str, Optional[TensorLike]]
_non_persistent_buffers_set: Set[str]
_quantizable_buffers: Set[str]
_version: int = 1
def __init__(self, *args, **kwargs) -> None:
"""
Initializes internal Module state
"""
super().__setattr__("_modules", OrderedDict())
super().__setattr__("_buffers", OrderedDict())
super().__setattr__("_non_persistent_buffers_set", set())
super().__setattr__("_quantizable_buffers", set())
def __call__(self, *input):
"""
Call self as a function.
"""
return self.forward(*input)
def forward(self, *input):
"""
Defines the computation performed at every call.
Should be overridden by all subclasses.
"""
pass
def children(self) -> Iterator["Module"]:
r"""Returns an iterator over immediate children modules.
Yields:
Module: a child module
"""
for name, module in self.named_children():
yield module
def named_children(self) -> Iterator[Tuple[str, "Module"]]:
r"""Returns an iterator over immediate children modules, yielding both
the name of the module as well as the module itself.
Yields:
(str, Module): Tuple containing a name and child module
Example::
>>> for name, module in model.named_children():
>>> if name in ['conv4', 'conv5']:
>>> print(module)
"""
memo = set()
for name, module in self._modules.items():
if module is not None and module not in memo:
memo.add(module)
yield name, module
def add_module(self, name: str, module: Optional["Module"]) -> None:
r"""Adds a child module to the current module.
The module can be accessed as an attribute using the given name.
Args:
name (str): name of the child module. The child module can be
accessed from this module using the given name
module (Module): child module to be added to the module.
"""
if not isinstance(module, Module) and module is not None:
raise TypeError(f"{str(module)} is not a Module subclass")
elif not isinstance(name, str):
raise TypeError(f"module name should be a string. Got {name}")
elif hasattr(self, name) and name not in self._modules:
raise KeyError(f"attribute '{name}' already exists")
elif "." in name:
raise KeyError(f'module name can\'t contain ".", got: {name}')
elif name == "":
raise KeyError('module name can\'t be empty string ""')
self._modules[name] = module
def register_module(self, name: str, module: Optional["Module"]) -> None:
r"""Alias for :func:`add_module`."""
self.add_module(name, module)
def modules(self) -> Iterator["Module"]:
r"""Returns an iterator over all modules in the network."""
for _, module in self.named_modules():
yield module
def named_modules(
self,
memo: Optional[Set["Module"]] = None,
prefix: str = "",
remove_duplicate: bool = True,
):
r"""Returns an iterator over all modules in the network, yielding
both the name of the module as well as the module itself.
Args:
memo: a memo to store the set of modules already added to the result
prefix: a prefix that will be added to the name of the module
remove_duplicate: whether to remove the duplicated module instances in the result
or not
Yields:
(str, Module): Tuple of name and module
Note:
Duplicate modules are returned only once. In the following
example, ``l`` will be returned only once.
"""
if memo is None:
memo = set()
if self not in memo:
if remove_duplicate:
memo.add(self)
yield prefix, self
for name, module in self._modules.items():
if module is None:
continue
submodule_prefix = prefix + ("." if prefix else "") + name
for m in module.named_modules(memo, submodule_prefix, remove_duplicate):
yield m
def buffers(self, recurse: bool = True) -> Iterator[TensorLike]:
"""
Returns an iterator over module buffers.
"""
for name, buf in self.named_buffers(recurse=recurse):
yield buf
def named_buffers(
self, prefix: str = "", recurse: bool = True, remove_duplicate: bool = True
) -> Iterator[Tuple[str, TensorLike]]:
r"""Returns an iterator over module buffers, yielding both the
name of the buffer as well as the buffer itself.
Args:
prefix (str): prefix to prepend to all buffer names.
recurse (bool, optional): if True, then yields buffers of this module
and all submodules. Otherwise, yields only buffers that
are direct members of this module. Defaults to True.
remove_duplicate (bool, optional): whether to remove the duplicated buffers in the result. Defaults to True.
Yields:
(str, Tensor): Tuple containing the name and buffer
Example::
>>> for name, buf in self.named_buffers():
>>> if name in ['running_var']:
>>> print(buf.size())
"""
gen = self._named_members(
lambda module: module._buffers.items(),
prefix=prefix,
recurse=recurse,
remove_duplicate=remove_duplicate,
)
yield from gen
# The user can pass an optional arbitrary mappable object to `state_dict`, in which case `state_dict` returns
# back that same object. But if they pass nothing, an `OrderedDict` is created and returned.
T_destination = TypeVar("T_destination", bound=Dict[str, Any])
@overload
def state_dict(self, *, destination: T_destination, prefix: str = ..., keep_vars: bool = ...) -> T_destination: ...
@overload
def state_dict(self, *, prefix: str = ..., keep_vars: bool = ...) -> Dict[str, Any]: ...
def state_dict(self, *args, destination=None, prefix="", keep_vars=False):
r"""Returns a dictionary containing references to the whole state of the module.
Both parameters and persistent buffers (e.g. running averages) are
included. Keys are corresponding parameter and buffer names.
Parameters and buffers set to ``None`` are not included.
.. note::
The returned object is a shallow copy. It contains references
to the module's parameters and buffers.
.. warning::
Currently ``state_dict()`` also accepts positional arguments for
``destination``, ``prefix`` and ``keep_vars`` in order. However,
this is being deprecated and keyword arguments will be enforced in
future releases.
.. warning::
Please avoid the use of argument ``destination`` as it is not
designed for end-users.
Args:
destination (dict, optional): If provided, the state of module will
be updated into the dict and the same object is returned.
Otherwise, an ``OrderedDict`` will be created and returned.
Default: ``None``.
prefix (str, optional): a prefix added to parameter and buffer
names to compose the keys in state_dict. Default: ``''``.
keep_vars (bool, optional): by default the :class:`~candle.Tensor` s
returned in the state dict are detached from autograd. If it's
set to ``True``, detaching will not be performed.
Default: ``False``.
Returns:
dict:
a dictionary containing a whole state of the module
Example::
>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']
"""
# TODO: Remove `args` and the parsing logic when BC allows.
if len(args) > 0:
if destination is None:
destination = args[0]
if len(args) > 1 and prefix == "":
prefix = args[1]
if len(args) > 2 and keep_vars is False:
keep_vars = args[2]
if destination is None:
destination = OrderedDict()
destination._metadata = OrderedDict()
local_metadata = dict(version=self._version)
if hasattr(destination, "_metadata"):
destination._metadata[prefix[:-1]] = local_metadata
self._save_to_state_dict(destination, prefix, keep_vars)
for name, module in self._modules.items():
if module is not None:
module.state_dict(
destination=destination,
prefix=prefix + name + ".",
keep_vars=keep_vars,
)
return destination
def _save_to_state_dict(self, destination, prefix, keep_vars):
r"""Saves module state to `destination` dictionary, containing a state
of the module, but not its descendants. This is called on every
submodule in :meth:`~candle.nn.Module.state_dict`.
In rare cases, subclasses can achieve class-specific behavior by
overriding this method with custom logic.
Args:
destination (dict): a dict where state will be stored
prefix (str): the prefix for parameters and buffers used in this
module
"""
for name, buf in self._buffers.items():
if buf is not None and name not in self._non_persistent_buffers_set:
if isinstance(buf, Tensor):
destination[prefix + name] = buf if keep_vars else buf.detach()
else:
destination[prefix + name] = buf
def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False):
r"""Copies parameters and buffers from :attr:`state_dict` into
this module and its descendants. If :attr:`strict` is ``True``, then
the keys of :attr:`state_dict` must exactly match the keys returned
by this module's :meth:`~candle.nn.Module.state_dict` function.
.. warning::
If :attr:`assign` is ``True`` the optimizer must be created after
the call to :attr:`load_state_dict`.
Args:
state_dict (dict): a dict containing parameters and
persistent buffers.
strict (bool, optional): whether to strictly enforce that the keys
in :attr:`state_dict` match the keys returned by this module's
:meth:`~candle.nn.Module.state_dict` function. Default: ``True``
assign (bool, optional): whether to assign items in the state
dictionary to their corresponding keys in the module instead
of copying them inplace into the module's current parameters and buffers.
When ``False``, the properties of the tensors in the current
module are preserved while when ``True``, the properties of the
Tensors in the state dict are preserved.
Default: ``False``
Returns:
``NamedTuple`` with ``missing_keys`` and ``unexpected_keys`` fields:
* **missing_keys** is a list of str containing the missing keys
* **unexpected_keys** is a list of str containing the unexpected keys
Note:
If a parameter or buffer is registered as ``None`` and its corresponding key
exists in :attr:`state_dict`, :meth:`load_state_dict` will raise a
``RuntimeError``.
"""
if not isinstance(state_dict, Mapping):
raise TypeError(f"Expected state_dict to be dict-like, got {type(state_dict)}.")
missing_keys: List[str] = []
unexpected_keys: List[str] = []
error_msgs: List[str] = []
# copy state_dict so _load_from_state_dict can modify it
metadata = getattr(state_dict, "_metadata", None)
state_dict = OrderedDict(state_dict)
if metadata is not None:
# mypy isn't aware that "_metadata" exists in state_dict
state_dict._metadata = metadata # type: ignore[attr-defined]
def load(module, local_state_dict, prefix=""):
local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
if assign:
local_metadata["assign_to_params_buffers"] = assign
module._load_from_state_dict(
local_state_dict,
prefix,
local_metadata,
True,
missing_keys,
unexpected_keys,
error_msgs,
)
for name, child in module._modules.items():
if child is not None:
child_prefix = prefix + name + "."
child_state_dict = {k: v for k, v in local_state_dict.items() if k.startswith(child_prefix)}
load(child, child_state_dict, child_prefix)
load(self, state_dict)
del load
if strict:
if len(unexpected_keys) > 0:
error_msgs.insert(
0,
"Unexpected key(s) in state_dict: {}. ".format(", ".join(f'"{k}"' for k in unexpected_keys)),
)
if len(missing_keys) > 0:
error_msgs.insert(
0,
"Missing key(s) in state_dict: {}. ".format(", ".join(f'"{k}"' for k in missing_keys)),
)
if len(error_msgs) > 0:
raise RuntimeError(
"Error(s) in loading state_dict for {}:\n\t{}".format(self.__class__.__name__, "\n\t".join(error_msgs))
)
return _IncompatibleKeys(missing_keys, unexpected_keys)
def _load_from_state_dict(
self,
state_dict,
prefix,
local_metadata,
strict,
missing_keys,
unexpected_keys,
error_msgs,
):
r"""Copies parameters and buffers from :attr:`state_dict` into only
this module, but not its descendants. This is called on every submodule
in :meth:`~candle.nn.Module.load_state_dict`. Metadata saved for this
module in input :attr:`state_dict` is provided as :attr:`local_metadata`.
For state dicts without metadata, :attr:`local_metadata` is empty.
Subclasses can achieve class-specific backward compatible loading using
the version number at `local_metadata.get("version", None)`.
Additionally, :attr:`local_metadata` can also contain the key
`assign_to_params_buffers` that indicates whether keys should be
assigned their corresponding tensor in the state_dict.
.. note::
:attr:`state_dict` is not the same object as the input
:attr:`state_dict` to :meth:`~candle.nn.Module.load_state_dict`. So
it can be modified.
Args:
state_dict (dict): a dict containing parameters and
persistent buffers.
prefix (str): the prefix for parameters and buffers used in this
module
local_metadata (dict): a dict containing the metadata for this module.
See
strict (bool): whether to strictly enforce that the keys in
:attr:`state_dict` with :attr:`prefix` match the names of
parameters and buffers in this module
missing_keys (list of str): if ``strict=True``, add missing keys to
this list
unexpected_keys (list of str): if ``strict=True``, add unexpected
keys to this list
error_msgs (list of str): error messages should be added to this
list, and will be reported together in
:meth:`~candle.nn.Module.load_state_dict`
"""
persistent_buffers = {k: v for k, v in self._buffers.items() if k not in self._non_persistent_buffers_set}
local_name_params = persistent_buffers.items()
local_state = {k: v for k, v in local_name_params if v is not None}
for name, param in local_state.items():
key = prefix + name
if key in state_dict:
input_param = state_dict[key]
if not isinstance(input_param, (Tensor, QTensor)):
error_msgs.append(
f'While copying the parameter named "{key}", '
"expected Tensor-like object from checkpoint but "
f"received {type(input_param)}"
)
continue
if input_param.shape != param.shape:
# local shape should match the one in checkpoint
error_msgs.append(
"size mismatch for {}: copying a param with shape {} from checkpoint, "
"the shape in current model is {}.".format(key, input_param.shape, param.shape)
)
continue
try:
# Shape checks are already done above -> Just assign tensor
setattr(self, name, input_param)
except Exception as ex:
error_msgs.append(
f'While copying the parameter named "{key}", '
f"whose dimensions in the model are {param.shape} and "
f"whose dimensions in the checkpoint are {input_param.shape}, "
f"an exception occurred : {ex.args}."
)
elif strict:
missing_keys.append(key)
if strict:
for key in state_dict.keys():
if key.startswith(prefix):
input_name = key[len(prefix) :]
input_name = input_name.split(".", 1)[0] # get the name of param/buffer/child
if input_name not in self._modules and input_name not in local_state:
unexpected_keys.append(key)
def _named_members(self, get_members_fn, prefix="", recurse=True, remove_duplicate: bool = True):
r"""Helper method for yielding various names + members of modules."""
memo = set()
modules = self.named_modules(prefix=prefix, remove_duplicate=remove_duplicate) if recurse else [(prefix, self)]
for module_prefix, module in modules:
members = get_members_fn(module)
for k, v in members:
if v is None or v in memo:
continue
if remove_duplicate:
memo.add(v)
name = module_prefix + ("." if module_prefix else "") + k
yield name, v
def _get_name(self):
return self.__class__.__name__
def _apply(self, fn):
for module in self.children():
module._apply(fn)
for key, buf in self._buffers.items():
if buf is not None:
self._buffers[key] = fn(buf)
return self
def __move_tensor_to_device(self, tensor: TensorLike, device: str):
if isinstance(tensor, Tensor):
return tensor.to_device(device)
else:
raise NotImplementedError("Cannot offload QTensor to cuda, yet!")
def device(self) -> str:
"""
Gets the device of the module, by inspecting its tensors.
"""
tensor = next(self.buffers())
if isinstance(tensor, Tensor):
return tensor.device
else:
# QTensors can only be on the CPU
return "cpu"
def cuda(self: T) -> T:
r"""Moves all model parameters and buffers to the GPU.
This also makes associated parameters and buffers different objects. So
it should be called before constructing optimizer if the module will
live on GPU while being optimized.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
def to_cuda(t: TensorLike):
return self.__move_tensor_to_device(t, "cuda")
return self._apply(to_cuda)
def cpu(self: T) -> T:
r"""Moves all model parameters and buffers to the CPU.
.. note::
This method modifies the module in-place.
Returns:
Module: self
"""
def to_cpu(t: TensorLike):
return self.__move_tensor_to_device(t, "cpu")
return self._apply(to_cpu)
def __cast_tensor(self, tensor: TensorLike, dtype: Union[DType, str]):
if isinstance(tensor, Tensor):
return tensor.to_dtype(dtype)
else:
raise TypeError("candle.Module.to only accepts Tensor dtypes, but got desired dtype={}".format(dtype))
def type(self: T, dst_type: Union[DType, str]) -> T:
r"""Casts all parameters and buffers to :attr:`dst_type`.
.. note::
This method modifies the module in-place.
Args:
dst_type (type or string): the desired type
Returns:
Module: self
"""
def cast(t: TensorLike):
return self.__cast_tensor(t, dst_type)
return self._apply(cast)
@overload
def to(
self: T,
device: str = ...,
dtype: Optional[Union[DType, str]] = ...,
) -> T: ...
@overload
def to(self: T, dtype: Union[DType, str]) -> T: ...
def to(self, *args, **kwargs):
r"""Moves and/or casts the parameters and buffers.
This can be called as
.. function:: to(device=None, dtype=None)
:noindex:
.. function:: to(dtype)
:noindex:
See below for examples.
.. note::
This method modifies the module in-place.
Args:
device (:class:`candle.device`): the desired device of the parameters
and buffers in this module
dtype (:class:`candle.dtype`): the desired floating point dtype of
the parameters and buffers in this module
Returns:
Module: self
"""
device = None
dtype = None
if args:
for arg in args:
# Assuming arg can be a string representing a device or a dtype
if isinstance(arg, str):
lower_arg = str(arg).lower()
if lower_arg.startswith("cuda") or lower_arg == "cpu":
device = lower_arg
else:
dtype = arg
elif isinstance(arg, DType):
dtype = str(arg)
else:
raise TypeError("Module.to() received an invalid combination of arguments. Got: {}".format(args))
if kwargs:
device = kwargs.get("device", device)
dtype = str(kwargs.get("dtype", dtype))
if device:
device = device.lower()
if dtype:
dtype = dtype.lower()
if dtype not in ["f32", "f16", "f64"]:
raise TypeError(
"candle.Module.to only accepts floating point" "dtypes, but got desired dtype={}".format(dtype)
)
def convert(t):
if dtype:
t = self.__cast_tensor(t, dtype)
if device:
t = self.__move_tensor_to_device(t, device)
return t
return self._apply(convert)
def __setattr__(self, __name: str, __value: Any) -> None:
if isinstance(__value, Module):
self._modules[__name] = __value
elif isinstance(__value, QTensor):
if __name in self._quantizable_buffers:
type = __value.ggml_dtype.lower()
if type in ["f32", "f16"]:
# It is faster to just dequantize the tensor here and use the normal tensor operations
dequant = __value.dequantize()
if type == "f16":
dequant = dequant.to_dtype("f16")
self._buffers[__name] = dequant
else:
self._buffers[__name] = __value
else:
# We expect a normal tensor here => dequantize it
self._buffers[__name] = __value.dequantize()
elif isinstance(__value, Tensor):
self._buffers[__name] = __value
else:
super().__setattr__(__name, __value)
def __getattr__(self, __name: str) -> Any:
if "_modules" in self.__dict__:
modules = self.__dict__["_modules"]
if __name in modules:
return modules[__name]
if "_buffers" in self.__dict__:
tensors = self.__dict__["_buffers"]
if __name in tensors:
return tensors[__name]
return super().__getattribute__(__name)
def __delattr__(self, name):
if name in self._buffers:
del self._buffers[name]
elif name in self._modules:
del self._modules[name]
else:
super().__delattr__(name)
| candle/candle-pyo3/py_src/candle/nn/module.py/0 | {
"file_path": "candle/candle-pyo3/py_src/candle/nn/module.py",
"repo_id": "candle",
"token_count": 12028
} | 55 |
import candle
print(f"mkl: {candle.utils.has_mkl()}")
print(f"accelerate: {candle.utils.has_accelerate()}")
print(f"num-threads: {candle.utils.get_num_threads()}")
print(f"cuda: {candle.utils.cuda_is_available()}")
t = candle.Tensor(42.0)
print(t)
print(t.shape, t.rank, t.device)
print(t + t)
t = candle.Tensor([3.0, 1, 4, 1, 5, 9, 2, 6])
print(t)
print(t + t)
t = t.reshape([2, 4])
print(t.matmul(t.t()))
print(t.to_dtype(candle.u8))
print(t.to_dtype("u8"))
t = candle.randn((5, 3))
print(t)
print(t.dtype)
t = candle.randn((16, 256))
quant_t = t.quantize("q6k")
dequant_t = quant_t.dequantize()
diff2 = (t - dequant_t).sqr()
print(diff2.mean_all())
| candle/candle-pyo3/test.py/0 | {
"file_path": "candle/candle-pyo3/test.py",
"repo_id": "candle",
"token_count": 340
} | 56 |
//! BigCode implementation in Rust based on the GPT-BigCode 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. See "StarCoder: A State-of-the-Art LLM for Code", Mukherjee et al. 2023
//! - [Arxiv](https://arxiv.org/abs/2305.06161)
//! - [Github](https://github.com/bigcode-project/starcoder)
//!
//! ## 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)
//! > }
//! > }
//! ```
//!
use candle::{DType, Device, IndexOp, Result, Tensor, D};
use candle_nn::{embedding, linear_b as linear, Embedding, LayerNorm, Linear, Module, VarBuilder};
fn layer_norm(size: usize, eps: f64, vb: VarBuilder) -> Result<LayerNorm> {
let weight = vb.get(size, "weight")?;
let bias = vb.get(size, "bias")?;
Ok(LayerNorm::new(weight, bias, eps))
}
fn make_causal_mask(t: usize, device: &Device) -> Result<Tensor> {
let mask: Vec<_> = (0..t)
.flat_map(|i| (0..t).map(move |j| u8::from(j <= i)))
.collect();
let mask = Tensor::from_slice(&mask, (t, t), device)?;
Ok(mask)
}
#[derive(Debug)]
pub struct Config {
pub vocab_size: usize,
// max_position_embeddings aka n_positions
pub max_position_embeddings: usize,
// num_hidden_layers aka n_layer
pub num_hidden_layers: usize,
// hidden_size aka n_embd
pub hidden_size: usize,
pub layer_norm_epsilon: f64,
pub n_inner: Option<usize>,
// num_attention_heads aka n_head
pub num_attention_heads: usize,
pub multi_query: bool,
pub use_cache: bool,
}
impl Config {
#[allow(dead_code)]
pub fn starcoder_1b() -> Self {
Self {
vocab_size: 49152,
max_position_embeddings: 8192,
num_hidden_layers: 24,
hidden_size: 2048,
layer_norm_epsilon: 1e-5,
n_inner: Some(8192),
num_attention_heads: 16,
multi_query: true,
use_cache: true,
}
}
#[allow(dead_code)]
pub fn starcoder_3b() -> Self {
Self {
vocab_size: 49152,
max_position_embeddings: 8192,
num_hidden_layers: 36,
hidden_size: 2816,
layer_norm_epsilon: 1e-5,
n_inner: Some(11264),
num_attention_heads: 22,
multi_query: true,
use_cache: true,
}
}
#[allow(dead_code)]
pub fn starcoder_7b() -> Self {
Self {
vocab_size: 49152,
max_position_embeddings: 8192,
num_hidden_layers: 42,
hidden_size: 4096,
layer_norm_epsilon: 1e-5,
n_inner: Some(16384),
num_attention_heads: 32,
multi_query: true,
use_cache: true,
}
}
#[allow(dead_code)]
pub fn starcoder() -> Self {
Self {
vocab_size: 49152,
max_position_embeddings: 8192,
num_hidden_layers: 40,
hidden_size: 6144,
layer_norm_epsilon: 1e-5,
n_inner: Some(24576),
num_attention_heads: 48,
multi_query: true,
use_cache: true,
}
}
}
struct Attention {
c_attn: Linear,
c_proj: Linear,
kv_cache: Option<Tensor>,
use_cache: bool,
embed_dim: usize,
kv_dim: usize,
num_heads: usize,
head_dim: usize,
multi_query: bool,
}
impl Attention {
pub fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let hidden_size = cfg.hidden_size;
let head_dim = hidden_size / cfg.num_attention_heads;
let kv_heads = if cfg.multi_query {
1
} else {
cfg.num_attention_heads
};
let kv_dim = kv_heads * head_dim;
let c_attn = linear(hidden_size, hidden_size + 2 * kv_dim, true, vb.pp("c_attn"))?;
let c_proj = linear(hidden_size, hidden_size, true, vb.pp("c_proj"))?;
Ok(Self {
c_proj,
c_attn,
embed_dim: hidden_size,
kv_cache: None,
use_cache: cfg.use_cache,
kv_dim,
head_dim,
num_heads: cfg.num_attention_heads,
multi_query: cfg.multi_query,
})
}
fn attn(
&self,
query: &Tensor,
key: &Tensor,
value: &Tensor,
attention_mask: &Tensor,
) -> Result<Tensor> {
if query.dtype() != DType::F32 {
// If we start supporting f16 models, we may need the upcasting scaling bits.
// https://github.com/huggingface/transformers/blob/a0042379269bea9182c1f87e6b2eee4ba4c8cce8/src/transformers/models/gpt_bigcode/modeling_gpt_bigcode.py#L133
candle::bail!("upcasting is not supported {:?}", query.dtype())
}
let scale_factor = 1f64 / (self.head_dim as f64).sqrt();
let initial_query_shape = query.shape();
let key_len = key.dim(D::Minus1)?;
let (query, key, attn_shape, attn_view) = if self.multi_query {
let (b_sz, query_len, _) = query.dims3()?;
let query = query.reshape((b_sz, query_len * self.num_heads, self.head_dim))?;
let attn_shape = (b_sz, query_len, self.num_heads, key_len);
let attn_view = (b_sz, query_len * self.num_heads, key_len);
(query, key.clone(), attn_shape, attn_view)
} else {
let (b_sz, _num_heads, query_len, _head_dim) = query.dims4()?;
let query = query.reshape((b_sz, query_len * self.num_heads, self.head_dim))?;
let key = key.reshape((b_sz * self.num_heads, self.head_dim, key_len))?;
let attn_shape = (b_sz, self.num_heads, query_len, key_len);
let attn_view = (b_sz * self.num_heads, query_len, key_len);
(query, key, attn_shape, attn_view)
};
let attn_weights =
(query.matmul(&key.contiguous()?)? * scale_factor)?.reshape(attn_shape)?;
let attention_mask = attention_mask.broadcast_as(attn_shape)?;
let mask_value =
Tensor::new(f32::NEG_INFINITY, query.device())?.broadcast_as(attn_shape)?;
let attn_weights = attention_mask.where_cond(&attn_weights, &mask_value)?;
let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
let value = value.contiguous()?;
let attn_output = if self.multi_query {
attn_weights
.reshape(attn_view)?
.matmul(&value)?
.reshape(initial_query_shape)?
} else {
attn_weights.matmul(&value)?
};
Ok(attn_output)
}
fn forward(&mut self, hidden_states: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
let qkv = self.c_attn.forward(hidden_states)?;
let (query, key_value) = if self.multi_query {
let query = qkv.i((.., .., ..self.embed_dim))?;
let key_value = qkv.i((.., .., self.embed_dim..self.embed_dim + 2 * self.kv_dim))?;
(query, key_value)
} else {
let mut dims = qkv.dims().to_vec();
dims.pop();
dims.push(self.embed_dim);
dims.push(self.head_dim * 3);
let qkv = qkv.reshape(dims)?.transpose(1, 2)?;
let query = qkv.i((.., .., .., ..self.head_dim))?;
let key_value = qkv.i((.., .., .., self.head_dim..3 * self.head_dim))?;
(query, key_value)
};
let mut key_value = key_value;
if self.use_cache {
if let Some(kv_cache) = &self.kv_cache {
// TODO: we could trim the tensors to MAX_SEQ_LEN so that this would work for
// arbitrarily large sizes.
key_value = Tensor::cat(&[kv_cache, &key_value], D::Minus2)?.contiguous()?;
}
self.kv_cache = Some(key_value.clone())
}
let key = key_value.narrow(D::Minus1, 0, self.head_dim)?;
let value = key_value.narrow(D::Minus1, self.head_dim, self.head_dim)?;
let attn_output = self.attn(&query, &key.t()?, &value, attention_mask)?;
let attn_output = if self.multi_query {
attn_output
} else {
attn_output
.transpose(1, 2)?
.reshape(hidden_states.shape())?
};
let attn_output = self.c_proj.forward(&attn_output)?;
Ok(attn_output)
}
}
struct Mlp {
c_fc: Linear,
c_proj: Linear,
}
impl Mlp {
fn load(inner_dim: usize, vb: VarBuilder, cfg: &Config) -> Result<Self> {
let c_fc = linear(cfg.hidden_size, inner_dim, true, vb.pp("c_fc"))?;
let c_proj = linear(inner_dim, cfg.hidden_size, true, vb.pp("c_proj"))?;
Ok(Self { c_fc, c_proj })
}
fn forward(&mut self, hidden_states: &Tensor) -> Result<Tensor> {
let hidden_states = self.c_fc.forward(hidden_states)?.gelu()?;
let hidden_states = self.c_proj.forward(&hidden_states)?;
Ok(hidden_states)
}
}
// TODO: Add cross-attention?
struct Block {
ln_1: LayerNorm,
attn: Attention,
ln_2: LayerNorm,
mlp: Mlp,
}
impl Block {
fn load(vb: VarBuilder, cfg: &Config) -> Result<Self> {
let hidden_size = cfg.hidden_size;
let inner_dim = cfg.n_inner.unwrap_or(4 * hidden_size);
let ln_1 = layer_norm(hidden_size, cfg.layer_norm_epsilon, vb.pp("ln_1"))?;
let attn = Attention::load(vb.pp("attn"), cfg)?;
let ln_2 = layer_norm(hidden_size, cfg.layer_norm_epsilon, vb.pp("ln_2"))?;
let mlp = Mlp::load(inner_dim, vb.pp("mlp"), cfg)?;
Ok(Self {
ln_1,
attn,
ln_2,
mlp,
})
}
fn forward(&mut self, hidden_states: &Tensor, attention_mask: &Tensor) -> Result<Tensor> {
let residual = hidden_states;
let hidden_states = self.ln_1.forward(hidden_states)?;
let attn_outputs = self.attn.forward(&hidden_states, attention_mask)?;
let hidden_states = (&attn_outputs + residual)?;
let residual = &hidden_states;
let hidden_states = self.ln_2.forward(&hidden_states)?;
let hidden_states = self.mlp.forward(&hidden_states)?;
let hidden_states = (&hidden_states + residual)?;
Ok(hidden_states)
}
}
pub struct GPTBigCode {
wte: Embedding,
wpe: Embedding,
blocks: Vec<Block>,
ln_f: LayerNorm,
lm_head: Linear,
bias: Tensor,
config: Config,
}
impl GPTBigCode {
pub fn config(&self) -> &Config {
&self.config
}
pub fn load(vb: VarBuilder, cfg: Config) -> Result<Self> {
let hidden_size = cfg.hidden_size;
let vb_t = vb.pp("transformer");
let wte = embedding(cfg.vocab_size, hidden_size, vb_t.pp("wte"))?;
let wpe = embedding(cfg.max_position_embeddings, hidden_size, vb_t.pp("wpe"))?;
let blocks = (0..cfg.num_hidden_layers)
.map(|i| Block::load(vb_t.pp(format!("h.{i}")), &cfg))
.collect::<Result<Vec<_>>>()?;
let ln_f = layer_norm(hidden_size, cfg.layer_norm_epsilon, vb_t.pp("ln_f"))?;
let lm_head = linear(hidden_size, cfg.vocab_size, false, vb_t.pp("wte"))?;
let bias = make_causal_mask(cfg.max_position_embeddings, vb.device())?;
Ok(Self {
wte,
wpe,
blocks,
lm_head,
ln_f,
bias,
config: cfg,
})
}
pub fn forward(&mut self, input_ids: &Tensor, past_len: usize) -> Result<Tensor> {
let dev = input_ids.device();
let (b_sz, seq_len) = input_ids.dims2()?;
let key_len = past_len + seq_len;
let attention_mask = self.bias.i((past_len..key_len, ..key_len))?.unsqueeze(0)?;
// MQA models: (batch_size, query_length, n_heads, key_length)
// MHA models: (batch_size, n_heads, query_length, key_length)
let seq_len_dim = if self.config.multi_query { 2 } else { 1 };
let attention_mask = attention_mask.unsqueeze(seq_len_dim)?;
let position_ids = Tensor::arange(past_len as u32, (past_len + seq_len) as u32, dev)?;
let position_ids = position_ids.unsqueeze(0)?.broadcast_as((b_sz, seq_len))?;
let input_embeds = self.wte.forward(input_ids)?;
let position_embeds = self.wpe.forward(&position_ids)?;
let mut hidden_states = (&input_embeds + &position_embeds)?;
for block in self.blocks.iter_mut() {
hidden_states = block.forward(&hidden_states, &attention_mask)?;
}
let hidden_states = self.ln_f.forward(&hidden_states)?;
let hidden_states = hidden_states
.reshape((b_sz, seq_len, self.config.hidden_size))?
.narrow(1, seq_len - 1, 1)?;
let logits = self.lm_head.forward(&hidden_states)?.squeeze(1)?;
Ok(logits)
}
}
| candle/candle-transformers/src/models/bigcode.rs/0 | {
"file_path": "candle/candle-transformers/src/models/bigcode.rs",
"repo_id": "candle",
"token_count": 6580
} | 57 |
use std::collections::HashMap;
use candle::{bail, Context, DType, Device, Module, Result, Tensor, D};
use candle_nn::{
conv1d, embedding, layer_norm, Conv1d, Conv1dConfig, Embedding, LayerNorm, VarBuilder,
};
use serde::{Deserialize, Deserializer};
pub const DTYPE: DType = DType::F32;
// NOTE: HiddenAct and HiddenActLayer are both direct copies from bert.rs.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize)]
#[serde(rename_all = "lowercase")]
pub enum HiddenAct {
Gelu,
GeluApproximate,
Relu,
}
pub struct HiddenActLayer {
act: HiddenAct,
span: tracing::Span,
}
impl HiddenActLayer {
fn new(act: HiddenAct) -> Self {
let span = tracing::span!(tracing::Level::TRACE, "hidden-act");
Self { act, span }
}
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let _enter = self.span.enter();
match self.act {
// https://github.com/huggingface/transformers/blob/cd4584e3c809bb9e1392ccd3fe38b40daba5519a/src/transformers/activations.py#L213
HiddenAct::Gelu => xs.gelu_erf(),
HiddenAct::GeluApproximate => xs.gelu(),
HiddenAct::Relu => xs.relu(),
}
}
}
#[derive(Debug, Clone, Copy, PartialEq, Eq, Deserialize, Default)]
#[serde(rename_all = "lowercase")]
enum PositionEmbeddingType {
#[default]
Absolute,
}
pub type Id2Label = HashMap<u32, String>;
pub type Label2Id = HashMap<String, u32>;
#[derive(Debug, Clone, PartialEq, Deserialize)]
pub struct Config {
pub vocab_size: usize,
pub hidden_size: usize,
pub num_hidden_layers: usize,
pub num_attention_heads: usize,
pub intermediate_size: usize,
pub hidden_act: HiddenAct,
pub hidden_dropout_prob: f64,
pub attention_probs_dropout_prob: f64,
pub max_position_embeddings: usize,
pub type_vocab_size: usize,
pub initializer_range: f64,
pub layer_norm_eps: f64,
pub relative_attention: bool,
pub max_relative_positions: isize,
pub pad_token_id: Option<usize>,
pub position_biased_input: bool,
#[serde(deserialize_with = "deserialize_pos_att_type")]
pub pos_att_type: Vec<String>,
pub position_buckets: Option<isize>,
pub share_att_key: Option<bool>,
pub attention_head_size: Option<usize>,
pub embedding_size: Option<usize>,
pub norm_rel_ebd: Option<String>,
pub conv_kernel_size: Option<usize>,
pub conv_groups: Option<usize>,
pub conv_act: Option<String>,
pub id2label: Option<Id2Label>,
pub label2id: Option<Label2Id>,
pub pooler_dropout: Option<f64>,
pub pooler_hidden_act: Option<HiddenAct>,
pub pooler_hidden_size: Option<usize>,
pub cls_dropout: Option<f64>,
}
fn deserialize_pos_att_type<'de, D>(deserializer: D) -> std::result::Result<Vec<String>, D::Error>
where
D: Deserializer<'de>,
{
#[derive(Deserialize, Debug)]
#[serde(untagged)]
enum StringOrVec {
String(String),
Vec(Vec<String>),
}
match StringOrVec::deserialize(deserializer)? {
StringOrVec::String(s) => Ok(s.split('|').map(String::from).collect()),
StringOrVec::Vec(v) => Ok(v),
}
}
// NOTE: Dropout is probably not needed for now since this will primarily be used
// in inferencing. However, for training/fine-tuning it will be necessary.
pub struct StableDropout {
_drop_prob: f64,
_count: usize,
}
impl StableDropout {
pub fn new(drop_prob: f64) -> Self {
Self {
_drop_prob: drop_prob,
_count: 0,
}
}
pub fn forward(&self, x: &Tensor) -> Result<Tensor> {
Ok(x.clone())
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L823
pub struct DebertaV2Embeddings {
device: Device,
word_embeddings: Embedding,
position_embeddings: Option<Embedding>,
token_type_embeddings: Option<Embedding>,
layer_norm: LayerNorm,
dropout: StableDropout,
position_ids: Tensor,
config: Config,
embedding_size: usize,
embed_proj: Option<candle_nn::Linear>,
}
impl DebertaV2Embeddings {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let device = vb.device().clone();
let config = config.clone();
let embedding_size = config.embedding_size.unwrap_or(config.hidden_size);
let word_embeddings =
embedding(config.vocab_size, embedding_size, vb.pp("word_embeddings"))?;
let position_embeddings = if config.position_biased_input {
Some(embedding(
config.max_position_embeddings,
embedding_size,
vb.pp("position_embeddings"),
)?)
} else {
None
};
let token_type_embeddings: Option<Embedding> = if config.type_vocab_size > 0 {
Some(candle_nn::embedding(
config.type_vocab_size,
config.hidden_size,
vb.pp("token_type_embeddings"),
)?)
} else {
None
};
let embed_proj: Option<candle_nn::Linear> = if embedding_size != config.hidden_size {
Some(candle_nn::linear_no_bias(
embedding_size,
config.hidden_size,
vb.pp("embed_proj"),
)?)
} else {
None
};
let layer_norm = layer_norm(
config.hidden_size,
config.layer_norm_eps,
vb.pp("LayerNorm"),
)?;
let dropout = StableDropout::new(config.hidden_dropout_prob);
let position_ids =
Tensor::arange(0, config.max_position_embeddings as u32, &device)?.unsqueeze(0)?;
Ok(Self {
word_embeddings,
position_embeddings,
token_type_embeddings,
layer_norm,
dropout,
position_ids,
device,
config,
embedding_size,
embed_proj,
})
}
pub fn forward(
&self,
input_ids: Option<&Tensor>,
token_type_ids: Option<&Tensor>,
position_ids: Option<&Tensor>,
mask: Option<&Tensor>,
inputs_embeds: Option<&Tensor>,
) -> Result<Tensor> {
let (input_shape, input_embeds) = match (input_ids, inputs_embeds) {
(Some(ids), None) => {
let embs = self.word_embeddings.forward(ids)?;
(ids.dims(), embs)
}
(None, Some(e)) => (e.dims(), e.clone()),
(None, None) => {
bail!("Must specify either input_ids or inputs_embeds")
}
(Some(_), Some(_)) => {
bail!("Can't specify both input_ids and inputs_embeds")
}
};
let seq_length = match input_shape.last() {
Some(v) => *v,
None => bail!("DebertaV2Embeddings invalid input shape"),
};
let position_ids = match position_ids {
Some(v) => v.clone(),
None => self.position_ids.narrow(1, 0, seq_length)?,
};
let token_type_ids = match token_type_ids {
Some(ids) => ids.clone(),
None => Tensor::zeros(input_shape, DType::U32, &self.device)?,
};
let position_embeddings = match &self.position_embeddings {
Some(emb) => emb.forward(&position_ids)?,
None => Tensor::zeros_like(&input_embeds)?,
};
let mut embeddings = input_embeds;
if self.config.position_biased_input {
embeddings = embeddings.add(&position_embeddings)?;
}
if self.config.type_vocab_size > 0 {
embeddings = self.token_type_embeddings.as_ref().map_or_else(
|| bail!("token_type_embeddings must be set when type_vocab_size > 0"),
|token_type_embeddings| {
embeddings.add(&token_type_embeddings.forward(&token_type_ids)?)
},
)?;
}
if self.embedding_size != self.config.hidden_size {
embeddings = if let Some(embed_proj) = &self.embed_proj {
embed_proj.forward(&embeddings)?
} else {
bail!("embed_proj must exist if embedding_size != config.hidden_size");
}
}
embeddings = self.layer_norm.forward(&embeddings)?;
if let Some(mask) = mask {
let mut mask = mask.clone();
if mask.dims() != embeddings.dims() {
if mask.dims().len() == 4 {
mask = mask.squeeze(1)?.squeeze(1)?;
}
mask = mask.unsqueeze(2)?;
}
mask = mask.to_dtype(embeddings.dtype())?;
embeddings = embeddings.broadcast_mul(&mask)?;
}
self.dropout.forward(&embeddings)
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L72
struct XSoftmax {}
impl XSoftmax {
pub fn apply(input: &Tensor, mask: &Tensor, dim: D, device: &Device) -> Result<Tensor> {
// NOTE: At the time of this writing, candle does not have a logical-not operator.
let mut rmask = mask.broadcast_as(input.shape())?.to_dtype(DType::F32)?;
rmask = rmask
.broadcast_lt(&Tensor::new(&[1.0_f32], device)?)?
.to_dtype(DType::U8)?;
let min_value_tensor = Tensor::new(&[f32::MIN], device)?.broadcast_as(input.shape())?;
let mut output = rmask.where_cond(&min_value_tensor, input)?;
output = candle_nn::ops::softmax(&output, dim)?;
let t_zeroes = Tensor::new(&[0f32], device)?.broadcast_as(input.shape())?;
output = rmask.where_cond(&t_zeroes, &output)?;
Ok(output)
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L605
pub struct DebertaV2DisentangledSelfAttention {
config: Config,
num_attention_heads: usize,
query_proj: candle_nn::Linear,
key_proj: candle_nn::Linear,
value_proj: candle_nn::Linear,
dropout: StableDropout,
device: Device,
relative_attention: bool,
pos_dropout: Option<StableDropout>,
position_buckets: isize,
max_relative_positions: isize,
pos_ebd_size: isize,
share_att_key: bool,
pos_key_proj: Option<candle_nn::Linear>,
pos_query_proj: Option<candle_nn::Linear>,
}
impl DebertaV2DisentangledSelfAttention {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let config = config.clone();
let vb = vb.clone();
if config.hidden_size % config.num_attention_heads != 0 {
return Err(candle::Error::Msg(format!(
"The hidden size {} is not a multiple of the number of attention heads {}",
config.hidden_size, config.num_attention_heads
)));
}
let num_attention_heads = config.num_attention_heads;
let attention_head_size = config
.attention_head_size
.unwrap_or(config.hidden_size / config.num_attention_heads);
let all_head_size = num_attention_heads * attention_head_size;
let query_proj = candle_nn::linear(config.hidden_size, all_head_size, vb.pp("query_proj"))?;
let key_proj = candle_nn::linear(config.hidden_size, all_head_size, vb.pp("key_proj"))?;
let value_proj = candle_nn::linear(config.hidden_size, all_head_size, vb.pp("value_proj"))?;
let share_att_key = config.share_att_key.unwrap_or(false);
let relative_attention = config.relative_attention;
let mut max_relative_positions = config.max_relative_positions;
let mut pos_ebd_size: isize = 0;
let position_buckets = config.position_buckets.unwrap_or(-1);
let mut pos_dropout: Option<StableDropout> = None;
let mut pos_key_proj: Option<candle_nn::Linear> = None;
let mut pos_query_proj: Option<candle_nn::Linear> = None;
if relative_attention {
if max_relative_positions < 1 {
max_relative_positions = config.max_position_embeddings as isize;
}
pos_ebd_size = max_relative_positions;
if position_buckets > 0 {
pos_ebd_size = position_buckets
}
pos_dropout = Some(StableDropout::new(config.hidden_dropout_prob));
if !share_att_key {
if config.pos_att_type.iter().any(|s| s == "c2p") {
pos_key_proj = Some(candle_nn::linear(
config.hidden_size,
all_head_size,
vb.pp("pos_key_proj"),
)?);
}
if config.pos_att_type.iter().any(|s| s == "p2c") {
pos_query_proj = Some(candle_nn::linear(
config.hidden_size,
all_head_size,
vb.pp("pos_query_proj"),
)?);
}
}
}
let dropout = StableDropout::new(config.attention_probs_dropout_prob);
let device = vb.device().clone();
Ok(Self {
config,
num_attention_heads,
query_proj,
key_proj,
value_proj,
dropout,
device,
relative_attention,
pos_dropout,
position_buckets,
max_relative_positions,
pos_ebd_size,
share_att_key,
pos_key_proj,
pos_query_proj,
})
}
pub fn forward(
&self,
hidden_states: &Tensor,
attention_mask: &Tensor,
query_states: Option<&Tensor>,
relative_pos: Option<&Tensor>,
rel_embeddings: Option<&Tensor>,
) -> Result<Tensor> {
let query_states = match query_states {
Some(qs) => qs,
None => hidden_states,
};
let query_layer = self.transpose_for_scores(&self.query_proj.forward(query_states)?)?;
let key_layer = self.transpose_for_scores(&self.key_proj.forward(query_states)?)?;
let value_layer = self.transpose_for_scores(&self.value_proj.forward(query_states)?)?;
let mut rel_att: Option<Tensor> = None;
let mut scale_factor: usize = 1;
if self.config.pos_att_type.iter().any(|s| s == "c2p") {
scale_factor += 1;
}
if self.config.pos_att_type.iter().any(|s| s == "p2c") {
scale_factor += 1;
}
let scale = {
let q_size = query_layer.dim(D::Minus1)?;
Tensor::new(&[(q_size * scale_factor) as f32], &self.device)?.sqrt()?
};
let mut attention_scores: Tensor = {
let key_layer_transposed = key_layer.t()?;
let div = key_layer_transposed
.broadcast_div(scale.to_dtype(query_layer.dtype())?.as_ref())?;
query_layer.matmul(&div)?
};
if self.relative_attention {
if let Some(rel_embeddings) = rel_embeddings {
let rel_embeddings = self
.pos_dropout
.as_ref()
.context("relative_attention requires pos_dropout")?
.forward(rel_embeddings)?;
rel_att = Some(self.disentangled_attention_bias(
query_layer,
key_layer,
relative_pos,
rel_embeddings,
scale_factor,
)?);
}
}
if let Some(rel_att) = rel_att {
attention_scores = attention_scores.broadcast_add(&rel_att)?;
}
attention_scores = attention_scores.reshape((
(),
self.num_attention_heads,
attention_scores.dim(D::Minus2)?,
attention_scores.dim(D::Minus1)?,
))?;
let mut attention_probs =
XSoftmax::apply(&attention_scores, attention_mask, D::Minus1, &self.device)?;
attention_probs = self.dropout.forward(&attention_probs)?;
let mut context_layer = attention_probs
.reshape((
(),
attention_probs.dim(D::Minus2)?,
attention_probs.dim(D::Minus1)?,
))?
.matmul(&value_layer)?;
context_layer = context_layer
.reshape((
(),
self.num_attention_heads,
context_layer.dim(D::Minus2)?,
context_layer.dim(D::Minus1)?,
))?
.permute((0, 2, 1, 3))?
.contiguous()?;
let dims = context_layer.dims();
context_layer = match dims.len() {
2 => context_layer.reshape(())?,
3 => context_layer.reshape((dims[0], ()))?,
4 => context_layer.reshape((dims[0], dims[1], ()))?,
5 => context_layer.reshape((dims[0], dims[1], dims[2], ()))?,
_ => {
bail!(
"Invalid shape for DisentabgledSelfAttention context layer: {:?}",
dims
)
}
};
Ok(context_layer)
}
fn transpose_for_scores(&self, xs: &Tensor) -> Result<Tensor> {
let dims = xs.dims().to_vec();
match dims.len() {
3 => {
let reshaped = xs.reshape((dims[0], dims[1], self.num_attention_heads, ()))?;
reshaped.transpose(1, 2)?.contiguous()?.reshape((
(),
reshaped.dim(1)?,
reshaped.dim(D::Minus1)?,
))
}
shape => {
bail!("Invalid shape for transpose_for_scores. Expected 3 dimensions, got {shape}")
}
}
}
fn disentangled_attention_bias(
&self,
query_layer: Tensor,
key_layer: Tensor,
relative_pos: Option<&Tensor>,
rel_embeddings: Tensor,
scale_factor: usize,
) -> Result<Tensor> {
let mut relative_pos = relative_pos.map_or(
build_relative_position(
query_layer.dim(D::Minus2)?,
key_layer.dim(D::Minus2)?,
&self.device,
Some(self.position_buckets),
Some(self.max_relative_positions),
)?,
|pos| pos.clone(),
);
relative_pos = match relative_pos.dims().len() {
2 => relative_pos.unsqueeze(0)?.unsqueeze(0)?,
3 => relative_pos.unsqueeze(1)?,
other => {
bail!("Relative position ids must be of dim 2 or 3 or 4. Got dim of size {other}")
}
};
let att_span = self.pos_ebd_size;
let rel_embeddings = rel_embeddings
.narrow(0, 0, (att_span * 2) as usize)?
.unsqueeze(0)?;
let mut pos_query_layer: Option<Tensor> = None;
let mut pos_key_layer: Option<Tensor> = None;
let repeat_with = query_layer.dim(0)? / self.num_attention_heads;
if self.share_att_key {
pos_query_layer = Some(
self.transpose_for_scores(&self.query_proj.forward(&rel_embeddings)?)?
.repeat(repeat_with)?,
);
pos_key_layer = Some(
self.transpose_for_scores(&self.key_proj.forward(&rel_embeddings)?)?
.repeat(repeat_with)?,
)
} else {
if self.config.pos_att_type.iter().any(|s| s == "c2p") {
pos_key_layer = Some(
self.transpose_for_scores(
&self
.pos_key_proj
.as_ref()
.context(
"Need pos_key_proj when share_att_key is false or not specified",
)?
.forward(&rel_embeddings)?,
)?
.repeat(repeat_with)?,
)
}
if self.config.pos_att_type.iter().any(|s| s == "p2c") {
pos_query_layer = Some(self.transpose_for_scores(&self
.pos_query_proj
.as_ref()
.context("Need a pos_query_proj when share_att_key is false or not specified")?
.forward(&rel_embeddings)?)?.repeat(repeat_with)?)
}
}
let mut score = Tensor::new(&[0 as f32], &self.device)?;
if self.config.pos_att_type.iter().any(|s| s == "c2p") {
let pos_key_layer = pos_key_layer.context("c2p without pos_key_layer")?;
let scale = Tensor::new(
&[(pos_key_layer.dim(D::Minus1)? * scale_factor) as f32],
&self.device,
)?
.sqrt()?;
let mut c2p_att = query_layer.matmul(&pos_key_layer.t()?)?;
let c2p_pos = relative_pos
.broadcast_add(&Tensor::new(&[att_span as i64], &self.device)?)?
.clamp(0 as f32, (att_span * 2 - 1) as f32)?;
c2p_att = c2p_att.gather(
&c2p_pos
.squeeze(0)?
.expand(&[
query_layer.dim(0)?,
query_layer.dim(1)?,
relative_pos.dim(D::Minus1)?,
])?
.contiguous()?,
D::Minus1,
)?;
score = score.broadcast_add(
&c2p_att.broadcast_div(scale.to_dtype(c2p_att.dtype())?.as_ref())?,
)?;
}
if self.config.pos_att_type.iter().any(|s| s == "p2c") {
let pos_query_layer = pos_query_layer.context("p2c without pos_key_layer")?;
let scale = Tensor::new(
&[(pos_query_layer.dim(D::Minus1)? * scale_factor) as f32],
&self.device,
)?
.sqrt()?;
let r_pos = {
if key_layer.dim(D::Minus2)? != query_layer.dim(D::Minus2)? {
build_relative_position(
key_layer.dim(D::Minus2)?,
key_layer.dim(D::Minus2)?,
&self.device,
Some(self.position_buckets),
Some(self.max_relative_positions),
)?
.unsqueeze(0)?
} else {
relative_pos
}
};
let p2c_pos = r_pos
.to_dtype(DType::F32)?
.neg()?
.broadcast_add(&Tensor::new(&[att_span as f32], &self.device)?)?
.clamp(0f32, (att_span * 2 - 1) as f32)?;
let p2c_att = key_layer
.matmul(&pos_query_layer.t()?)?
.gather(
&p2c_pos
.squeeze(0)?
.expand(&[
query_layer.dim(0)?,
key_layer.dim(D::Minus2)?,
key_layer.dim(D::Minus2)?,
])?
.contiguous()?
.to_dtype(DType::U32)?,
D::Minus1,
)?
.t()?;
score =
score.broadcast_add(&p2c_att.broadcast_div(&scale.to_dtype(p2c_att.dtype())?)?)?;
}
Ok(score)
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L270
pub struct DebertaV2Attention {
dsa: DebertaV2DisentangledSelfAttention,
output: DebertaV2SelfOutput,
}
impl DebertaV2Attention {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let dsa = DebertaV2DisentangledSelfAttention::load(vb.pp("attention.self"), config)?;
let output = DebertaV2SelfOutput::load(vb.pp("attention.output"), config)?;
Ok(Self { dsa, output })
}
fn forward(
&self,
hidden_states: &Tensor,
attention_mask: &Tensor,
query_states: Option<&Tensor>,
relative_pos: Option<&Tensor>,
rel_embeddings: Option<&Tensor>,
) -> Result<Tensor> {
let self_output = self.dsa.forward(
hidden_states,
attention_mask,
query_states,
relative_pos,
rel_embeddings,
)?;
self.output
.forward(&self_output, query_states.unwrap_or(hidden_states))
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L255
pub struct DebertaV2SelfOutput {
dense: candle_nn::Linear,
layer_norm: LayerNorm,
dropout: StableDropout,
}
impl DebertaV2SelfOutput {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let dense = candle_nn::linear(config.hidden_size, config.hidden_size, vb.pp("dense"))?;
let layer_norm = candle_nn::layer_norm(
config.hidden_size,
config.layer_norm_eps,
vb.pp("LayerNorm"),
)?;
let dropout = StableDropout::new(config.hidden_dropout_prob);
Ok(Self {
dense,
layer_norm,
dropout,
})
}
pub fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> {
let mut hidden_states = self.dense.forward(hidden_states)?;
hidden_states = self.dropout.forward(&hidden_states)?;
self.layer_norm
.forward(&hidden_states.broadcast_add(input_tensor)?)
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L307
pub struct DebertaV2Intermediate {
dense: candle_nn::Linear,
intermediate_act: HiddenActLayer,
}
impl DebertaV2Intermediate {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let dense = candle_nn::linear(
config.hidden_size,
config.intermediate_size,
vb.pp("intermediate.dense"),
)?;
let intermediate_act = HiddenActLayer::new(config.hidden_act);
Ok(Self {
dense,
intermediate_act,
})
}
pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
self.intermediate_act
.forward(&self.dense.forward(hidden_states)?)
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L323
pub struct DebertaV2Output {
dense: candle_nn::Linear,
layer_norm: LayerNorm,
dropout: StableDropout,
}
impl DebertaV2Output {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let dense = candle_nn::linear(
config.intermediate_size,
config.hidden_size,
vb.pp("output.dense"),
)?;
let layer_norm = candle_nn::layer_norm(
config.hidden_size,
config.layer_norm_eps,
vb.pp("output.LayerNorm"),
)?;
let dropout = StableDropout::new(config.hidden_dropout_prob);
Ok(Self {
dense,
layer_norm,
dropout,
})
}
pub fn forward(&self, hidden_states: &Tensor, input_tensor: &Tensor) -> Result<Tensor> {
let mut hidden_states = self.dense.forward(hidden_states)?;
hidden_states = self.dropout.forward(&hidden_states)?;
hidden_states = {
let to_norm = hidden_states.broadcast_add(input_tensor)?;
self.layer_norm.forward(&to_norm)?
};
Ok(hidden_states)
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L339
pub struct DebertaV2Layer {
attention: DebertaV2Attention,
intermediate: DebertaV2Intermediate,
output: DebertaV2Output,
}
impl DebertaV2Layer {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let attention = DebertaV2Attention::load(vb.clone(), config)?;
let intermediate = DebertaV2Intermediate::load(vb.clone(), config)?;
let output = DebertaV2Output::load(vb.clone(), config)?;
Ok(Self {
attention,
intermediate,
output,
})
}
fn forward(
&self,
hidden_states: &Tensor,
attention_mask: &Tensor,
query_states: Option<&Tensor>,
relative_pos: Option<&Tensor>,
rel_embeddings: Option<&Tensor>,
) -> Result<Tensor> {
let attention_output = self.attention.forward(
hidden_states,
attention_mask,
query_states,
relative_pos,
rel_embeddings,
)?;
let intermediate_output = self.intermediate.forward(&attention_output)?;
let layer_output = self
.output
.forward(&intermediate_output, &attention_output)?;
Ok(layer_output)
}
}
// TODO: In order to fully test ConvLayer a model needs to be found has a configuration where `conv_kernel_size` exists and is > 0
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L373
pub struct ConvLayer {
_conv_act: String,
_conv: Conv1d,
_layer_norm: LayerNorm,
_dropout: StableDropout,
_config: Config,
}
impl ConvLayer {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let config = config.clone();
let kernel_size = config.conv_kernel_size.unwrap_or(3);
let groups = config.conv_groups.unwrap_or(1);
let conv_act: String = config.conv_act.clone().unwrap_or("tanh".to_string());
let conv_conf = Conv1dConfig {
padding: (kernel_size - 1) / 2,
groups,
..Default::default()
};
let conv = conv1d(
config.hidden_size,
config.hidden_size,
kernel_size,
conv_conf,
vb.pp("conv"),
)?;
let layer_norm = layer_norm(
config.hidden_size,
config.layer_norm_eps,
vb.pp("LayerNorm"),
)?;
let dropout = StableDropout::new(config.hidden_dropout_prob);
Ok(Self {
_conv_act: conv_act,
_conv: conv,
_layer_norm: layer_norm,
_dropout: dropout,
_config: config,
})
}
pub fn forward(
&self,
_hidden_states: &Tensor,
_residual_states: &Tensor,
_input_mask: &Tensor,
) -> Result<Tensor> {
todo!("Need a model that contains a conv layer to test against.")
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L409
pub struct DebertaV2Encoder {
layer: Vec<DebertaV2Layer>,
relative_attention: bool,
max_relative_positions: isize,
position_buckets: isize,
rel_embeddings: Option<Embedding>,
norm_rel_ebd: String,
layer_norm: Option<LayerNorm>,
conv: Option<ConvLayer>,
device: Device,
}
impl DebertaV2Encoder {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let layer = (0..config.num_hidden_layers)
.map(|index| DebertaV2Layer::load(vb.pp(format!("layer.{index}")), config))
.collect::<Result<Vec<_>>>()?;
let relative_attention = config.relative_attention;
let mut max_relative_positions = config.max_relative_positions;
let position_buckets = config.position_buckets.unwrap_or(-1);
let mut rel_embeddings: Option<Embedding> = None;
if relative_attention {
if max_relative_positions < 1 {
max_relative_positions = config.max_position_embeddings as isize;
}
let mut pos_ebd_size = max_relative_positions * 2;
if position_buckets > 0 {
pos_ebd_size = position_buckets * 2;
}
rel_embeddings = Some(embedding(
pos_ebd_size as usize,
config.hidden_size,
vb.pp("rel_embeddings"),
)?);
}
// NOTE: The Python code assumes that the config attribute "norm_rel_ebd" is an array of some kind, but most examples have it as a string.
// So it might need to be updated at some point.
let norm_rel_ebd = match config.norm_rel_ebd.as_ref() {
Some(nre) => nre.trim().to_string(),
None => "none".to_string(),
};
let layer_norm: Option<LayerNorm> = if norm_rel_ebd == "layer_norm" {
Some(layer_norm(
config.hidden_size,
config.layer_norm_eps,
vb.pp("LayerNorm"),
)?)
} else {
None
};
let conv: Option<ConvLayer> = if config.conv_kernel_size.unwrap_or(0) > 0 {
Some(ConvLayer::load(vb.pp("conv"), config)?)
} else {
None
};
Ok(Self {
layer,
relative_attention,
max_relative_positions,
position_buckets,
rel_embeddings,
norm_rel_ebd,
layer_norm,
conv,
device: vb.device().clone(),
})
}
pub fn forward(
&self,
hidden_states: &Tensor,
attention_mask: &Tensor,
query_states: Option<&Tensor>,
relative_pos: Option<&Tensor>,
) -> Result<Tensor> {
let input_mask = if attention_mask.dims().len() <= 2 {
attention_mask.clone()
} else {
attention_mask
.sum_keepdim(attention_mask.rank() - 2)?
.gt(0.)?
};
let attention_mask = self.get_attention_mask(attention_mask.clone())?;
let relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)?;
let mut next_kv: Tensor = hidden_states.clone();
let rel_embeddings = self.get_rel_embedding()?;
let mut output_states = next_kv.to_owned();
let mut query_states: Option<Tensor> = query_states.cloned();
for (i, layer_module) in self.layer.iter().enumerate() {
// NOTE: The original python code branches here if this model is being
// used for training vs. inferencing. For now, we will only handle the
// inferencing side of things
output_states = layer_module.forward(
next_kv.as_ref(),
&attention_mask,
query_states.as_ref(),
relative_pos.as_ref(),
rel_embeddings.as_ref(),
)?;
if i == 0 {
if let Some(conv) = &self.conv {
output_states = conv.forward(hidden_states, &output_states, &input_mask)?;
}
}
if query_states.is_some() {
query_states = Some(output_states.clone());
} else {
next_kv = output_states.clone();
}
}
Ok(output_states)
}
fn get_attention_mask(&self, mut attention_mask: Tensor) -> Result<Tensor> {
match attention_mask.dims().len() {
0..=2 => {
let extended_attention_mask = attention_mask.unsqueeze(1)?.unsqueeze(2)?;
attention_mask = extended_attention_mask.broadcast_mul(
&extended_attention_mask
.squeeze(D::Minus2)?
.unsqueeze(D::Minus1)?,
)?;
}
3 => attention_mask = attention_mask.unsqueeze(1)?,
len => bail!("Unsupported attentiom mask size length: {len}"),
}
Ok(attention_mask)
}
fn get_rel_pos(
&self,
hidden_states: &Tensor,
query_states: Option<&Tensor>,
relative_pos: Option<&Tensor>,
) -> Result<Option<Tensor>> {
if self.relative_attention && relative_pos.is_none() {
let q = if let Some(query_states) = query_states {
query_states.dim(D::Minus2)?
} else {
hidden_states.dim(D::Minus2)?
};
return Ok(Some(build_relative_position(
q,
hidden_states.dim(D::Minus2)?,
&self.device,
Some(self.position_buckets),
Some(self.max_relative_positions),
)?));
}
if relative_pos.is_some() {
Ok(relative_pos.cloned())
} else {
Ok(None)
}
}
fn get_rel_embedding(&self) -> Result<Option<Tensor>> {
if !self.relative_attention {
return Ok(None);
}
let rel_embeddings = self
.rel_embeddings
.as_ref()
.context("self.rel_embeddings not present when using relative_attention")?
.embeddings()
.clone();
if !self.norm_rel_ebd.contains("layer_norm") {
return Ok(Some(rel_embeddings));
}
let layer_normed_embeddings = self
.layer_norm
.as_ref()
.context("DebertaV2Encoder layer_norm is None when norm_rel_ebd contains layer_norm")?
.forward(&rel_embeddings)?;
Ok(Some(layer_normed_embeddings))
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L991
pub struct DebertaV2Model {
embeddings: DebertaV2Embeddings,
encoder: DebertaV2Encoder,
z_steps: usize,
pub device: Device,
}
impl DebertaV2Model {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let vb = vb.clone();
let embeddings = DebertaV2Embeddings::load(vb.pp("embeddings"), config)?;
let encoder = DebertaV2Encoder::load(vb.pp("encoder"), config)?;
let z_steps: usize = 0;
Ok(Self {
embeddings,
encoder,
z_steps,
device: vb.device().clone(),
})
}
pub fn forward(
&self,
input_ids: &Tensor,
token_type_ids: Option<Tensor>,
attention_mask: Option<Tensor>,
) -> Result<Tensor> {
let input_ids_shape = input_ids.shape();
let attention_mask = match attention_mask {
Some(mask) => mask,
None => Tensor::ones(input_ids_shape, DType::I64, &self.device)?,
};
let token_type_ids = match token_type_ids {
Some(ids) => ids,
None => Tensor::zeros(input_ids_shape, DType::U32, &self.device)?,
};
let embedding_output = self.embeddings.forward(
Some(input_ids),
Some(&token_type_ids),
None,
Some(&attention_mask),
None,
)?;
let encoder_output =
self.encoder
.forward(&embedding_output, &attention_mask, None, None)?;
if self.z_steps > 1 {
todo!("Complete DebertaV2Model forward() when z_steps > 1 -- Needs a model to test this situation.")
}
Ok(encoder_output)
}
}
#[derive(Debug)]
pub struct NERItem {
pub entity: String,
pub word: String,
pub score: f32,
pub start: usize,
pub end: usize,
pub index: usize,
}
#[derive(Debug)]
pub struct TextClassificationItem {
pub label: String,
pub score: f32,
}
pub struct DebertaV2NERModel {
pub device: Device,
deberta: DebertaV2Model,
dropout: candle_nn::Dropout,
classifier: candle_nn::Linear,
}
fn id2label_len(config: &Config, id2label: Option<HashMap<u32, String>>) -> Result<usize> {
let id2label_len = match (&config.id2label, id2label) {
(None, None) => bail!("Id2Label is either not present in the model configuration or not passed into DebertaV2NERModel::load as a parameter"),
(None, Some(id2label_p)) => id2label_p.len(),
(Some(id2label_c), None) => id2label_c.len(),
(Some(id2label_c), Some(id2label_p)) => {
if *id2label_c == id2label_p {
id2label_c.len()
} else {
bail!("Id2Label is both present in the model configuration and provided as a parameter, and they are different.")
}
}
};
Ok(id2label_len)
}
impl DebertaV2NERModel {
pub fn load(vb: VarBuilder, config: &Config, id2label: Option<Id2Label>) -> Result<Self> {
let id2label_len = id2label_len(config, id2label)?;
let deberta = DebertaV2Model::load(vb.clone(), config)?;
let dropout = candle_nn::Dropout::new(config.hidden_dropout_prob as f32);
let classifier: candle_nn::Linear = candle_nn::linear_no_bias(
config.hidden_size,
id2label_len,
vb.root().pp("classifier"),
)?;
Ok(Self {
device: vb.device().clone(),
deberta,
dropout,
classifier,
})
}
pub fn forward(
&self,
input_ids: &Tensor,
token_type_ids: Option<Tensor>,
attention_mask: Option<Tensor>,
) -> Result<Tensor> {
let output = self
.deberta
.forward(input_ids, token_type_ids, attention_mask)?;
let output = self.dropout.forward(&output, false)?;
self.classifier.forward(&output)
}
}
pub struct DebertaV2SeqClassificationModel {
pub device: Device,
deberta: DebertaV2Model,
dropout: StableDropout,
pooler: DebertaV2ContextPooler,
classifier: candle_nn::Linear,
}
impl DebertaV2SeqClassificationModel {
pub fn load(vb: VarBuilder, config: &Config, id2label: Option<Id2Label>) -> Result<Self> {
let id2label_len = id2label_len(config, id2label)?;
let deberta = DebertaV2Model::load(vb.clone(), config)?;
let pooler = DebertaV2ContextPooler::load(vb.clone(), config)?;
let output_dim = pooler.output_dim()?;
let classifier = candle_nn::linear(output_dim, id2label_len, vb.root().pp("classifier"))?;
let dropout = match config.cls_dropout {
Some(cls_dropout) => StableDropout::new(cls_dropout),
None => StableDropout::new(config.hidden_dropout_prob),
};
Ok(Self {
device: vb.device().clone(),
deberta,
dropout,
pooler,
classifier,
})
}
pub fn forward(
&self,
input_ids: &Tensor,
token_type_ids: Option<Tensor>,
attention_mask: Option<Tensor>,
) -> Result<Tensor> {
let encoder_layer = self
.deberta
.forward(input_ids, token_type_ids, attention_mask)?;
let pooled_output = self.pooler.forward(&encoder_layer)?;
let pooled_output = self.dropout.forward(&pooled_output)?;
self.classifier.forward(&pooled_output)
}
}
pub struct DebertaV2ContextPooler {
dense: candle_nn::Linear,
dropout: StableDropout,
config: Config,
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L49
impl DebertaV2ContextPooler {
pub fn load(vb: VarBuilder, config: &Config) -> Result<Self> {
let pooler_hidden_size = config
.pooler_hidden_size
.context("config.pooler_hidden_size is required for DebertaV2ContextPooler")?;
let pooler_dropout = config
.pooler_dropout
.context("config.pooler_dropout is required for DebertaV2ContextPooler")?;
let dense = candle_nn::linear(
pooler_hidden_size,
pooler_hidden_size,
vb.root().pp("pooler.dense"),
)?;
let dropout = StableDropout::new(pooler_dropout);
Ok(Self {
dense,
dropout,
config: config.clone(),
})
}
pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
let context_token = hidden_states.narrow(1, 0, 1)?.squeeze(1)?;
let context_token = self.dropout.forward(&context_token)?;
let pooled_output = self.dense.forward(&context_token.contiguous()?)?;
let pooler_hidden_act = self
.config
.pooler_hidden_act
.context("Could not obtain pooler hidden act from config")?;
HiddenActLayer::new(pooler_hidden_act).forward(&pooled_output)
}
pub fn output_dim(&self) -> Result<usize> {
self.config.pooler_hidden_size.context("DebertaV2ContextPooler cannot return output_dim (pooler_hidden_size) since it is not specified in the model config")
}
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L557
pub(crate) fn build_relative_position(
query_size: usize,
key_size: usize,
device: &Device,
bucket_size: Option<isize>,
max_position: Option<isize>,
) -> Result<Tensor> {
let q_ids = Tensor::arange(0, query_size as i64, device)?.unsqueeze(0)?;
let k_ids: Tensor = Tensor::arange(0, key_size as i64, device)?.unsqueeze(D::Minus1)?;
let mut rel_pos_ids = k_ids.broadcast_sub(&q_ids)?;
let bucket_size = bucket_size.unwrap_or(-1);
let max_position = max_position.unwrap_or(-1);
if bucket_size > 0 && max_position > 0 {
rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position, device)?;
}
rel_pos_ids = rel_pos_ids.to_dtype(DType::I64)?;
rel_pos_ids = rel_pos_ids.narrow(0, 0, query_size)?;
rel_pos_ids.unsqueeze(0)
}
// https://github.com/huggingface/transformers/blob/78b2929c0554b79e0489b451ce4ece14d265ead2/src/transformers/models/deberta_v2/modeling_deberta_v2.py#L542
pub(crate) fn make_log_bucket_position(
relative_pos: Tensor,
bucket_size: isize,
max_position: isize,
device: &Device,
) -> Result<Tensor> {
let sign = relative_pos.to_dtype(DType::F32)?.sign()?;
let mid = bucket_size / 2;
let lt_mid = relative_pos.lt(mid as i64)?;
let gt_neg_mid = relative_pos.gt(-mid as i64)?;
let condition = lt_mid
.to_dtype(candle::DType::F32)?
.mul(>_neg_mid.to_dtype(candle::DType::F32)?)?
.to_dtype(DType::U8)?;
let on_true = Tensor::new(&[(mid - 1) as u32], device)?
.broadcast_as(relative_pos.shape())?
.to_dtype(relative_pos.dtype())?;
let on_false = relative_pos
.to_dtype(DType::F32)?
.abs()?
.to_dtype(DType::I64)?;
let abs_pos = condition.where_cond(&on_true, &on_false)?;
let mid_as_tensor = Tensor::from_slice(&[mid as f32], (1,), device)?;
let log_pos = {
let first_log = abs_pos
.to_dtype(DType::F32)?
.broadcast_div(&mid_as_tensor)?
.log()?;
let second_log =
Tensor::from_slice(&[((max_position as f32 - 1.0) / mid as f32)], (1,), device)?
.log()?;
let first_div_second = first_log.broadcast_div(&second_log)?;
let to_ceil = first_div_second
.broadcast_mul(Tensor::from_slice(&[(mid - 1) as f32], (1,), device)?.as_ref())?;
let ceil = to_ceil.ceil()?;
ceil.broadcast_add(&mid_as_tensor)?
};
Ok({
let abs_pos_lte_mid = abs_pos.to_dtype(DType::F32)?.broadcast_le(&mid_as_tensor)?;
let relative_pos = relative_pos.to_dtype(relative_pos.dtype())?;
let log_pos_mul_sign = log_pos.broadcast_mul(&sign.to_dtype(DType::F32)?)?;
abs_pos_lte_mid.where_cond(&relative_pos.to_dtype(DType::F32)?, &log_pos_mul_sign)?
})
}
| candle/candle-transformers/src/models/debertav2.rs/0 | {
"file_path": "candle/candle-transformers/src/models/debertav2.rs",
"repo_id": "candle",
"token_count": 24495
} | 58 |
use candle::{Device, Result, Tensor};
pub fn get_noise(
num_samples: usize,
height: usize,
width: usize,
device: &Device,
) -> Result<Tensor> {
let height = height.div_ceil(16) * 2;
let width = width.div_ceil(16) * 2;
Tensor::randn(0f32, 1., (num_samples, 16, height, width), device)
}
#[derive(Debug, Clone)]
pub struct State {
pub img: Tensor,
pub img_ids: Tensor,
pub txt: Tensor,
pub txt_ids: Tensor,
pub vec: Tensor,
}
impl State {
pub fn new(t5_emb: &Tensor, clip_emb: &Tensor, img: &Tensor) -> Result<Self> {
let dtype = img.dtype();
let (bs, c, h, w) = img.dims4()?;
let dev = img.device();
let img = img.reshape((bs, c, h / 2, 2, w / 2, 2))?; // (b, c, h, ph, w, pw)
let img = img.permute((0, 2, 4, 1, 3, 5))?; // (b, h, w, c, ph, pw)
let img = img.reshape((bs, h / 2 * w / 2, c * 4))?;
let img_ids = Tensor::stack(
&[
Tensor::full(0u32, (h / 2, w / 2), dev)?,
Tensor::arange(0u32, h as u32 / 2, dev)?
.reshape(((), 1))?
.broadcast_as((h / 2, w / 2))?,
Tensor::arange(0u32, w as u32 / 2, dev)?
.reshape((1, ()))?
.broadcast_as((h / 2, w / 2))?,
],
2,
)?
.to_dtype(dtype)?;
let img_ids = img_ids.reshape((1, h / 2 * w / 2, 3))?;
let img_ids = img_ids.repeat((bs, 1, 1))?;
let txt = t5_emb.repeat(bs)?;
let txt_ids = Tensor::zeros((bs, txt.dim(1)?, 3), dtype, dev)?;
let vec = clip_emb.repeat(bs)?;
Ok(Self {
img,
img_ids,
txt,
txt_ids,
vec,
})
}
}
fn time_shift(mu: f64, sigma: f64, t: f64) -> f64 {
let e = mu.exp();
e / (e + (1. / t - 1.).powf(sigma))
}
/// `shift` is a triple `(image_seq_len, base_shift, max_shift)`.
pub fn get_schedule(num_steps: usize, shift: Option<(usize, f64, f64)>) -> Vec<f64> {
let timesteps: Vec<f64> = (0..=num_steps)
.map(|v| v as f64 / num_steps as f64)
.rev()
.collect();
match shift {
None => timesteps,
Some((image_seq_len, y1, y2)) => {
let (x1, x2) = (256., 4096.);
let m = (y2 - y1) / (x2 - x1);
let b = y1 - m * x1;
let mu = m * image_seq_len as f64 + b;
timesteps
.into_iter()
.map(|v| time_shift(mu, 1., v))
.collect()
}
}
}
pub fn unpack(xs: &Tensor, height: usize, width: usize) -> Result<Tensor> {
let (b, _h_w, c_ph_pw) = xs.dims3()?;
let height = height.div_ceil(16);
let width = width.div_ceil(16);
xs.reshape((b, height, width, c_ph_pw / 4, 2, 2))? // (b, h, w, c, ph, pw)
.permute((0, 3, 1, 4, 2, 5))? // (b, c, h, ph, w, pw)
.reshape((b, c_ph_pw / 4, height * 2, width * 2))
}
#[allow(clippy::too_many_arguments)]
pub fn denoise<M: super::WithForward>(
model: &M,
img: &Tensor,
img_ids: &Tensor,
txt: &Tensor,
txt_ids: &Tensor,
vec_: &Tensor,
timesteps: &[f64],
guidance: f64,
) -> Result<Tensor> {
let b_sz = img.dim(0)?;
let dev = img.device();
let guidance = Tensor::full(guidance as f32, b_sz, dev)?;
let mut img = img.clone();
for window in timesteps.windows(2) {
let (t_curr, t_prev) = match window {
[a, b] => (a, b),
_ => continue,
};
let t_vec = Tensor::full(*t_curr as f32, b_sz, dev)?;
let pred = model.forward(&img, img_ids, txt, txt_ids, &t_vec, vec_, Some(&guidance))?;
img = (img + pred * (t_prev - t_curr))?
}
Ok(img)
}
| candle/candle-transformers/src/models/flux/sampling.rs/0 | {
"file_path": "candle/candle-transformers/src/models/flux/sampling.rs",
"repo_id": "candle",
"token_count": 2069
} | 59 |
//! Mamba inference implementation.
//!
//! See ["Mamba: Linear-Time Sequence Modeling with Selective State Spaces"](https://arxiv.org/abs/2312.00752)
//!
//! Based on reference implementation from the AlbertMamba project
//! A fast implementation of mamba for inference only.
//! Based on Laurent Mazare's rust implementation: [mamba.rs](https://github.com/LaurentMazare/mamba.rs)
use crate::models::with_tracing::{linear, linear_no_bias, Linear};
use candle::{DType, Device, IndexOp, Module, Result, Tensor, D};
use candle_nn::{RmsNorm, VarBuilder};
const D_CONV: usize = 4;
const D_STATE: usize = 16;
#[derive(Debug, Clone, serde::Deserialize)]
pub struct Config {
pub d_model: usize,
pub n_layer: usize,
pub vocab_size: usize,
pub pad_vocab_size_multiple: usize,
}
impl Config {
fn vocab_size(&self) -> usize {
let pad = self.pad_vocab_size_multiple;
self.vocab_size.div_ceil(pad) * pad
}
fn dt_rank(&self) -> usize {
self.d_model.div_ceil(16)
}
fn d_inner(&self) -> usize {
self.d_model * 2
}
}
pub struct State {
pub hs: Vec<Tensor>,
pub prev_xs: Vec<[Tensor; D_CONV]>,
pub pos: usize,
}
impl State {
pub fn new(batch_size: usize, cfg: &Config, dtype: DType, device: &Device) -> Result<Self> {
let mut hs = Vec::with_capacity(cfg.n_layer);
let mut prev_xs = Vec::with_capacity(cfg.n_layer);
for _i in 0..cfg.n_layer {
let h = Tensor::zeros((batch_size, cfg.d_inner(), D_STATE), dtype, device)?;
let x = Tensor::zeros((batch_size, cfg.d_inner()), dtype, device)?;
hs.push(h);
prev_xs.push([x.clone(), x.clone(), x.clone(), x.clone()]);
}
Ok(Self {
hs,
prev_xs,
pos: 0,
})
}
}
#[derive(Clone, Debug)]
pub struct MambaBlock {
in_proj: Linear,
conv1d_bias: Tensor,
conv1d_weights: [Tensor; D_CONV],
x_proj: Linear,
dt_proj: Linear,
a_log: Tensor,
d: Tensor,
out_proj: Linear,
dt_rank: usize,
layer_index: usize,
d_inner: usize,
}
impl MambaBlock {
pub fn new(layer_index: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
let d_inner = cfg.d_inner();
let dt_rank = cfg.dt_rank();
let in_proj = linear_no_bias(cfg.d_model, d_inner * 2, vb.pp("in_proj"))?;
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"))?;
let conv1d_bias = vb.get(d_inner, "conv1d.bias")?;
let conv1d_weight = vb.get((d_inner, 1, D_CONV), "conv1d.weight")?;
let conv1d_weights = [
conv1d_weight.i((.., 0, 0))?,
conv1d_weight.i((.., 0, 1))?,
conv1d_weight.i((.., 0, 2))?,
conv1d_weight.i((.., 0, 3))?,
];
Ok(Self {
in_proj,
conv1d_bias,
conv1d_weights,
x_proj,
dt_proj,
a_log,
d,
out_proj,
dt_rank,
layer_index,
d_inner,
})
}
pub fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
let (b_sz, _dim) = xs.dims2()?;
let li = self.layer_index;
let mut xs = xs.apply(&self.in_proj)?.chunk(2, D::Minus1)?;
let proj_for_silu = xs.remove(1);
state.prev_xs[li][state.pos % D_CONV] = xs.remove(0);
let mut proj_for_conv = self.conv1d_bias.broadcast_as((b_sz, self.d_inner))?;
for d_c in 0..D_CONV {
proj_for_conv = (proj_for_conv
+ self.conv1d_weights[d_c]
.broadcast_mul(&state.prev_xs[li][(d_c + 1 + state.pos) % D_CONV])?)?;
}
let proj_for_conv = candle_nn::ops::silu(&proj_for_conv)?;
// SSM + Selection, we're doing inference here so only need the last step of
// the sequence.
// Algorithm 3.2 on page 6, https://arxiv.org/pdf/2312.00752.pdf
let x_proj = self.x_proj.forward(&proj_for_conv)?;
let delta = x_proj.narrow(D::Minus1, 0, self.dt_rank)?.contiguous()?;
let b = x_proj.narrow(D::Minus1, self.dt_rank, D_STATE)?;
let c = x_proj.narrow(D::Minus1, self.dt_rank + D_STATE, D_STATE)?;
let delta = delta.apply(&self.dt_proj)?;
// softplus
let delta = (delta.exp()? + 1.)?.log()?;
let a = self.a_log.to_dtype(delta.dtype())?.exp()?.neg()?;
let d = self.d.to_dtype(delta.dtype())?;
// Selective scan part
// Eqn (2a), page 3, h_t = Ab h_{t-1} + Bb x_t
let delta = delta
.unsqueeze(D::Minus1)?
.broadcast_as((b_sz, self.d_inner, D_STATE))?;
let a = a.broadcast_as((b_sz, self.d_inner, D_STATE))?;
let b = b.broadcast_as((b_sz, self.d_inner, D_STATE))?;
let proj_for_conv_b =
proj_for_conv
.unsqueeze(D::Minus1)?
.broadcast_as((b_sz, self.d_inner, D_STATE))?;
state.hs[li] = ((&state.hs[li] * (&delta * &a)?.exp()?)? + &delta * &b * &proj_for_conv_b)?;
let ss = (state.hs[li]
.matmul(&c.unsqueeze(D::Minus1)?)?
.squeeze(D::Minus1)?
+ proj_for_conv.broadcast_mul(&d)?)?;
let ys = (ss * candle_nn::ops::silu(&proj_for_silu))?;
ys.apply(&self.out_proj)
}
}
#[derive(Clone, Debug)]
pub struct ResidualBlock {
mixer: MambaBlock,
norm: RmsNorm,
}
impl ResidualBlock {
pub fn new(layer_index: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
let norm = candle_nn::rms_norm(cfg.d_model, 1e-5, vb.pp("norm"))?;
let mixer = MambaBlock::new(layer_index, cfg, vb.pp("mixer"))?;
Ok(Self { mixer, norm })
}
fn forward(&self, xs: &Tensor, state: &mut State) -> Result<Tensor> {
self.mixer.forward(&xs.apply(&self.norm)?, state)? + 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,
dtype: DType,
}
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(layer_idx, 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,
dtype: vb.dtype(),
})
}
pub fn forward(&self, input_ids: &Tensor, state: &mut State) -> Result<Tensor> {
let _b_size = input_ids.dims1()?;
let mut xs = self.embedding.forward(input_ids)?;
for layer in self.layers.iter() {
xs = layer.forward(&xs, state)?
}
state.pos += 1;
xs.apply(&self.norm_f)?.apply(&self.lm_head)
}
pub fn dtype(&self) -> DType {
self.dtype
}
}
| candle/candle-transformers/src/models/mamba.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mamba.rs",
"repo_id": "candle",
"token_count": 3925
} | 60 |
use candle::{Module, Result, Tensor};
use candle_nn as nn;
pub struct Qkv {
pub q: Tensor,
pub k: Tensor,
pub v: Tensor,
}
pub struct Mlp {
fc1: nn::Linear,
act: nn::Activation,
fc2: nn::Linear,
}
impl Mlp {
pub fn new(
in_features: usize,
hidden_features: usize,
vb: candle_nn::VarBuilder,
) -> Result<Self> {
let fc1 = nn::linear(in_features, hidden_features, vb.pp("fc1"))?;
let act = nn::Activation::GeluPytorchTanh;
let fc2 = nn::linear(hidden_features, in_features, vb.pp("fc2"))?;
Ok(Self { fc1, act, fc2 })
}
}
impl Module for Mlp {
fn forward(&self, x: &Tensor) -> Result<Tensor> {
let x = self.fc1.forward(x)?;
let x = self.act.forward(&x)?;
self.fc2.forward(&x)
}
}
pub struct QkvOnlyAttnProjections {
qkv: nn::Linear,
head_dim: usize,
}
impl QkvOnlyAttnProjections {
pub fn new(dim: usize, num_heads: usize, vb: nn::VarBuilder) -> Result<Self> {
let head_dim = dim / num_heads;
let qkv = nn::linear(dim, dim * 3, vb.pp("qkv"))?;
Ok(Self { qkv, head_dim })
}
pub fn pre_attention(&self, x: &Tensor) -> Result<Qkv> {
let qkv = self.qkv.forward(x)?;
split_qkv(&qkv, self.head_dim)
}
}
pub struct AttnProjections {
head_dim: usize,
qkv: nn::Linear,
ln_k: Option<candle_nn::RmsNorm>,
ln_q: Option<candle_nn::RmsNorm>,
proj: nn::Linear,
}
impl AttnProjections {
pub fn new(dim: usize, num_heads: usize, vb: nn::VarBuilder) -> Result<Self> {
let head_dim = dim / num_heads;
let qkv = nn::linear(dim, dim * 3, vb.pp("qkv"))?;
let proj = nn::linear(dim, dim, vb.pp("proj"))?;
let (ln_k, ln_q) = if vb.contains_tensor("ln_k.weight") {
let ln_k = candle_nn::rms_norm(head_dim, 1e-6, vb.pp("ln_k"))?;
let ln_q = candle_nn::rms_norm(head_dim, 1e-6, vb.pp("ln_q"))?;
(Some(ln_k), Some(ln_q))
} else {
(None, None)
};
Ok(Self {
head_dim,
qkv,
proj,
ln_k,
ln_q,
})
}
pub fn pre_attention(&self, x: &Tensor) -> Result<Qkv> {
let qkv = self.qkv.forward(x)?;
let Qkv { q, k, v } = split_qkv(&qkv, self.head_dim)?;
let q = match self.ln_q.as_ref() {
None => q,
Some(l) => {
let (b, t, h) = q.dims3()?;
l.forward(&q.reshape((b, t, (), self.head_dim))?)?
.reshape((b, t, h))?
}
};
let k = match self.ln_k.as_ref() {
None => k,
Some(l) => {
let (b, t, h) = k.dims3()?;
l.forward(&k.reshape((b, t, (), self.head_dim))?)?
.reshape((b, t, h))?
}
};
Ok(Qkv { q, k, v })
}
pub fn post_attention(&self, x: &Tensor) -> Result<Tensor> {
self.proj.forward(x)
}
}
fn split_qkv(qkv: &Tensor, head_dim: usize) -> Result<Qkv> {
let (batch_size, seq_len, _) = qkv.dims3()?;
let qkv = qkv.reshape((batch_size, seq_len, 3, (), head_dim))?;
let q = qkv.get_on_dim(2, 0)?;
let q = q.reshape((batch_size, seq_len, ()))?;
let k = qkv.get_on_dim(2, 1)?;
let k = k.reshape((batch_size, seq_len, ()))?;
let v = qkv.get_on_dim(2, 2)?;
Ok(Qkv { q, k, v })
}
| candle/candle-transformers/src/models/mmdit/projections.rs/0 | {
"file_path": "candle/candle-transformers/src/models/mmdit/projections.rs",
"repo_id": "candle",
"token_count": 1917
} | 61 |
//! Parler Model implementation for parler_tts text-to-speech synthesis
//!
//! Implements a transformer-based decoder architecture for generating audio tokens
//! from text using discrete tokens. The model converts text into audio segments
//! using multiple codebooks of quantized audio tokens.
//!
//! The model architecture includes:
//! - Multi-head attention layers for text and audio processing
//! - Feed-forward networks
//! - Layer normalization
//! - Positional embeddings
//! - Multiple codebook prediction heads
//!
//! The implementation follows the original parler_tts architecture while focusing
//! on audio token generation for text-to-speech synthesis.
//!
use crate::generation::LogitsProcessor;
use crate::models::t5;
use candle::{IndexOp, Result, Tensor};
use candle_nn::{layer_norm, linear_b as linear, Activation, LayerNorm, Linear, VarBuilder};
#[derive(serde::Deserialize, Debug, Clone)]
pub struct DecoderConfig {
pub vocab_size: usize,
pub max_position_embeddings: usize,
pub num_hidden_layers: usize,
pub ffn_dim: usize,
pub num_attention_heads: usize,
pub num_key_value_heads: Option<usize>,
pub num_cross_attention_key_value_heads: Option<usize>,
pub activation_function: Activation,
pub hidden_size: usize,
pub scale_embedding: bool,
pub num_codebooks: usize,
pub pad_token_id: usize,
pub bos_token_id: usize,
pub eos_token_id: usize,
pub tie_word_embeddings: bool,
pub rope_embeddings: bool,
pub rope_theta: f64,
}
#[derive(serde::Deserialize, Debug, Clone)]
pub struct Config {
pub decoder_start_token_id: u32,
pub pad_token_id: u32,
pub decoder: DecoderConfig,
pub text_encoder: t5::Config,
pub vocab_size: usize,
pub audio_encoder: crate::models::dac::Config,
}
#[derive(Debug, Clone)]
pub struct Attention {
k_proj: Linear,
v_proj: Linear,
q_proj: Linear,
out_proj: Linear,
is_causal: bool,
kv_cache: Option<(Tensor, Tensor)>,
scaling: f64,
num_heads: usize,
num_kv_heads: usize,
num_kv_groups: usize,
head_dim: usize,
}
impl Attention {
fn new(
num_kv_heads: usize,
is_causal: bool,
cfg: &DecoderConfig,
vb: VarBuilder,
) -> Result<Self> {
if cfg.rope_embeddings {
candle::bail!("rope embeddings are not supported");
}
let embed_dim = cfg.hidden_size;
let head_dim = embed_dim / cfg.num_attention_heads;
let kv_out_dim = num_kv_heads * head_dim;
let k_proj = linear(embed_dim, kv_out_dim, false, vb.pp("k_proj"))?;
let v_proj = linear(embed_dim, kv_out_dim, false, vb.pp("v_proj"))?;
let q_proj = linear(embed_dim, embed_dim, false, vb.pp("q_proj"))?;
let out_proj = linear(embed_dim, embed_dim, false, vb.pp("out_proj"))?;
Ok(Self {
k_proj,
v_proj,
q_proj,
out_proj,
is_causal,
kv_cache: None,
scaling: (head_dim as f64).powf(-0.5),
num_heads: cfg.num_attention_heads,
num_kv_heads,
num_kv_groups: cfg.num_attention_heads / num_kv_heads,
head_dim,
})
}
fn forward(
&mut self,
xs: &Tensor,
key_value_states: Option<&Tensor>,
attention_mask: Option<&Tensor>,
) -> Result<Tensor> {
let (b_sz, tgt_len, _) = xs.dims3()?;
let query_states = (xs.apply(&self.q_proj)? * self.scaling)?
.reshape((b_sz, tgt_len, self.num_heads, self.head_dim))?
.transpose(1, 2)?
.contiguous()?;
let key_states = match key_value_states {
Some(states) => states.apply(&self.k_proj)?,
None => xs.apply(&self.k_proj)?,
};
let key_states = key_states
.reshape((b_sz, (), self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?
.contiguous()?;
let value_states = match key_value_states {
Some(states) => states.apply(&self.v_proj)?,
None => xs.apply(&self.v_proj)?,
};
let value_states = value_states
.reshape((b_sz, (), self.num_kv_heads, self.head_dim))?
.transpose(1, 2)?
.contiguous()?;
let (key_states, value_states) = match &self.kv_cache {
None => (key_states, value_states),
Some((prev_k, prev_v)) => {
let key_states = Tensor::cat(&[prev_k, &key_states], 2)?;
let value_states = Tensor::cat(&[prev_v, &value_states], 2)?;
(key_states, value_states)
}
};
if self.is_causal {
self.kv_cache = Some((key_states.clone(), value_states.clone()));
}
let key_states = crate::utils::repeat_kv(key_states, self.num_kv_groups)?.contiguous()?;
let value_states =
crate::utils::repeat_kv(value_states, self.num_kv_groups)?.contiguous()?;
let attn_weights = query_states.matmul(&key_states.transpose(2, 3)?)?;
let attn_weights = match attention_mask {
None => attn_weights,
Some(mask) => attn_weights.broadcast_add(mask)?,
};
let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
let attn_output = attn_weights.matmul(&value_states)?;
attn_output
.transpose(1, 2)?
.reshape((b_sz, tgt_len, ()))?
.apply(&self.out_proj)
}
fn clear_kv_cache(&mut self) {
self.kv_cache = None
}
}
#[derive(Debug, Clone)]
pub struct DecoderLayer {
self_attn: Attention,
self_attn_layer_norm: LayerNorm,
encoder_attn: Attention,
encoder_attn_layer_norm: LayerNorm,
fc1: Linear,
fc2: Linear,
final_layer_norm: LayerNorm,
activation: Activation,
}
impl DecoderLayer {
fn new(cfg: &DecoderConfig, vb: VarBuilder) -> Result<Self> {
let kv_heads = cfg.num_key_value_heads.unwrap_or(cfg.num_attention_heads);
let kv_heads_cross = cfg.num_cross_attention_key_value_heads.unwrap_or(kv_heads);
let self_attn = Attention::new(kv_heads, true, cfg, vb.pp("self_attn"))?;
let encoder_attn = Attention::new(kv_heads_cross, false, cfg, vb.pp("encoder_attn"))?;
let self_attn_layer_norm =
layer_norm(cfg.hidden_size, 1e-5, vb.pp("self_attn_layer_norm"))?;
let encoder_attn_layer_norm =
layer_norm(cfg.hidden_size, 1e-5, vb.pp("encoder_attn_layer_norm"))?;
let fc1 = linear(cfg.hidden_size, cfg.ffn_dim, false, vb.pp("fc1"))?;
let fc2 = linear(cfg.ffn_dim, cfg.hidden_size, false, vb.pp("fc2"))?;
let final_layer_norm = layer_norm(cfg.hidden_size, 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: cfg.activation_function,
})
}
fn forward(
&mut self,
xs: &Tensor,
attention_mask: Option<&Tensor>,
encoder_xs: &Tensor,
encoder_attention_mask: Option<&Tensor>,
) -> Result<Tensor> {
// Self attention
let residual = xs;
let xs = xs.apply(&self.self_attn_layer_norm)?;
let xs = self.self_attn.forward(&xs, None, attention_mask)?;
let xs = (residual + xs)?;
// Cross attention
let residual = &xs;
let xs = xs.apply(&self.encoder_attn_layer_norm)?;
let xs = self
.encoder_attn
.forward(&xs, Some(encoder_xs), encoder_attention_mask)?;
let xs = (residual + xs)?;
// Fully connected
let residual = &xs;
let xs = xs
.apply(&self.final_layer_norm)?
.apply(&self.fc1)?
.apply(&self.activation)?
.apply(&self.fc2)?;
residual + xs
}
fn clear_kv_cache(&mut self) {
self.self_attn.clear_kv_cache();
self.encoder_attn.clear_kv_cache();
}
}
#[derive(Debug, Clone)]
pub struct Decoder {
embed_tokens: Vec<candle_nn::Embedding>,
embed_positions: Tensor,
layers: Vec<DecoderLayer>,
layer_norm: LayerNorm,
num_codebooks: usize,
hidden_size: usize,
lm_heads: Vec<Linear>,
dtype: candle::DType,
}
impl Decoder {
pub fn new(cfg: &DecoderConfig, vb: VarBuilder) -> Result<Self> {
let vb_d = vb.pp("model.decoder");
let mut embed_tokens = Vec::with_capacity(cfg.num_codebooks);
let vb_e = vb_d.pp("embed_tokens");
for embed_idx in 0..cfg.num_codebooks {
let e = candle_nn::embedding(cfg.vocab_size + 1, cfg.hidden_size, vb_e.pp(embed_idx))?;
embed_tokens.push(e)
}
let embed_positions = vb_d.get(
(cfg.max_position_embeddings, cfg.hidden_size),
"embed_positions.weights",
)?;
let mut layers = Vec::with_capacity(cfg.num_hidden_layers);
let vb_l = vb_d.pp("layers");
for layer_idx in 0..cfg.num_hidden_layers {
let layer = DecoderLayer::new(cfg, vb_l.pp(layer_idx))?;
layers.push(layer)
}
let layer_norm = layer_norm(cfg.hidden_size, 1e-5, vb_d.pp("layer_norm"))?;
let mut lm_heads = Vec::with_capacity(cfg.num_codebooks);
let vb_l = vb.pp("lm_heads");
for lm_idx in 0..cfg.num_codebooks {
let lm_head = linear(cfg.hidden_size, cfg.vocab_size, false, vb_l.pp(lm_idx))?;
lm_heads.push(lm_head)
}
Ok(Self {
embed_tokens,
embed_positions,
layers,
layer_norm,
num_codebooks: cfg.num_codebooks,
lm_heads,
hidden_size: cfg.hidden_size,
dtype: vb.dtype(),
})
}
pub fn forward(
&mut self,
input_ids: &Tensor,
prompt_hidden_states: Option<&Tensor>,
attention_mask: Option<&Tensor>,
encoder_xs: &Tensor,
encoder_attention_mask: Option<&Tensor>,
seqlen_offset: usize,
) -> Result<Vec<Tensor>> {
let (b_sz, num_codebooks, seq_len) = input_ids.dims3()?;
if num_codebooks != self.num_codebooks {
candle::bail!("unexpected num codebooks in input {:?}", input_ids.shape())
}
let mut inputs_embeds = Tensor::zeros(
(b_sz, seq_len, self.hidden_size),
self.dtype,
input_ids.device(),
)?;
for (idx, embs) in self.embed_tokens.iter().enumerate() {
let e = input_ids.i((.., idx))?.apply(embs)?;
inputs_embeds = (inputs_embeds + e)?
}
let inputs_embeds = match prompt_hidden_states {
None => inputs_embeds,
Some(pis) => Tensor::cat(&[pis, &inputs_embeds], 1)?,
};
let embed_positions = self
.embed_positions
.i(seqlen_offset..seqlen_offset + inputs_embeds.dim(1)?)?;
let mut xs = (inputs_embeds + embed_positions.unsqueeze(0))?;
for layer in self.layers.iter_mut() {
xs = layer.forward(&xs, attention_mask, encoder_xs, encoder_attention_mask)?;
}
let xs = xs.apply(&self.layer_norm)?;
let mut lm_logits = Vec::with_capacity(self.num_codebooks);
for lm_head in self.lm_heads.iter() {
let logits = xs.apply(lm_head)?;
lm_logits.push(logits)
}
Ok(lm_logits)
}
pub fn clear_kv_cache(&mut self) {
for layer in self.layers.iter_mut() {
layer.clear_kv_cache()
}
}
}
#[derive(Debug, Clone)]
pub struct Model {
pub embed_prompts: candle_nn::Embedding,
pub enc_to_dec_proj: Option<Linear>,
pub decoder: Decoder,
pub text_encoder: t5::T5EncoderModel,
pub decoder_start_token_id: u32,
pub pad_token_id: u32,
pub audio_encoder: crate::models::dac::Model,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let text_encoder = t5::T5EncoderModel::load(vb.pp("text_encoder"), &cfg.text_encoder)?;
let decoder = Decoder::new(&cfg.decoder, vb.pp("decoder"))?;
let embed_prompts = candle_nn::embedding(
cfg.vocab_size,
cfg.decoder.hidden_size,
vb.pp("embed_prompts"),
)?;
let enc_to_dec_proj = if cfg.text_encoder.d_model != cfg.decoder.hidden_size {
let proj = linear(
cfg.text_encoder.d_model,
cfg.decoder.hidden_size,
true,
vb.pp("enc_to_dec_proj"),
)?;
Some(proj)
} else {
None
};
let audio_encoder =
crate::models::dac::Model::new(&cfg.audio_encoder, vb.pp("audio_encoder.model"))?;
Ok(Self {
decoder,
text_encoder,
embed_prompts,
enc_to_dec_proj,
decoder_start_token_id: cfg.decoder_start_token_id,
pad_token_id: cfg.pad_token_id,
audio_encoder,
})
}
/// Note that the returned tensor uses the CPU device.
pub fn generate(
&mut self,
prompt_tokens: &Tensor,
description_tokens: &Tensor,
mut lp: LogitsProcessor,
max_steps: usize,
) -> Result<Tensor> {
self.decoder.clear_kv_cache();
self.text_encoder.clear_kv_cache();
let encoded = self.text_encoder.forward(description_tokens)?;
let encoded = match self.enc_to_dec_proj.as_ref() {
None => encoded,
Some(proj) => encoded.apply(proj)?,
};
let prompt_hidden_states = prompt_tokens.apply(&self.embed_prompts)?;
let num_codebooks = self.decoder.num_codebooks;
let mut audio_tokens = vec![self.decoder_start_token_id; num_codebooks];
let mut all_audio_tokens = vec![vec![]; num_codebooks];
let prompt_len = prompt_hidden_states.dim(1)?;
for step in 0..max_steps {
let input_ids = Tensor::from_slice(
audio_tokens.as_slice(),
(1, num_codebooks, 1),
prompt_tokens.device(),
)?;
let (prompt_hidden_states, pos) = if step == 0 {
(Some(&prompt_hidden_states), 0)
} else {
(None, step + prompt_len)
};
let causal_mask = if pos == 0 {
self.prepare_causal_mask(prompt_len + 1, prompt_len + 1, input_ids.device())?
} else {
self.prepare_causal_mask(1, pos + 1, input_ids.device())?
};
let logits = self.decoder.forward(
&input_ids,
prompt_hidden_states,
Some(&causal_mask),
&encoded,
None,
pos,
)?;
for (logit_idx, logit) in logits.iter().enumerate() {
if logit_idx > step {
break;
}
if audio_tokens[logit_idx] != self.pad_token_id {
let logit = logit.i((0, logit.dim(1)? - 1))?;
let token = lp.sample(&logit)?;
audio_tokens[logit_idx] = token
}
}
if audio_tokens.iter().all(|v| v == &self.pad_token_id) {
break;
}
for (cb_idx, &token) in audio_tokens.iter().enumerate() {
if token != self.decoder_start_token_id && token != self.pad_token_id {
all_audio_tokens[cb_idx].push(token)
}
}
}
let min_len = all_audio_tokens.iter().map(|v| v.len()).min().unwrap_or(0);
all_audio_tokens.iter_mut().for_each(|v| {
v.resize(min_len, 0);
});
let all_audio_tokens = Tensor::new(all_audio_tokens, &candle::Device::Cpu)?;
Ok(all_audio_tokens)
}
fn prepare_causal_mask(
&self,
q_len: usize,
kv_len: usize,
device: &candle::Device,
) -> Result<Tensor> {
let mask: Vec<_> = (0..q_len)
.flat_map(|i| {
(0..kv_len).map(move |j| {
if i + kv_len < j + q_len {
f32::NEG_INFINITY
} else {
0.
}
})
})
.collect();
Tensor::from_slice(&mask, (q_len, kv_len), device)
}
}
| candle/candle-transformers/src/models/parler_tts.rs/0 | {
"file_path": "candle/candle-transformers/src/models/parler_tts.rs",
"repo_id": "candle",
"token_count": 8563
} | 62 |
//! Quantized MPT model implementation.
//!
//! MPT (MPT-7B) is a causal transformer model series optimized for code generation.
//! This implementation provides quantization for reduced memory and compute.
//!
//! Key characteristics:
//! - Multi-Query Grouped Attention (MQA)
//! - Support for KV-caching
//! - Pre-computed ALiBi attention biases
//! - Support for 8-bit quantization
//!
//! References:
//! - [Replit Code Models](https://huggingface.co/replit/replit-code-v1_5-3b)
//! - [MPT-7B Implementation](https://github.com/mosaicml/llm-foundry)
//!
/// MPT model used by replit-code-v1_5-3b
/// https://huggingface.co/replit/replit-code-v1_5-3b/blob/main/modeling_mpt.py
///
use crate::quantized_nn::{layer_norm_no_bias, linear_no_bias, Embedding, Linear};
pub use crate::quantized_var_builder::VarBuilder;
/// MPT model used by replit-code-v1_5-3b
/// https://huggingface.co/replit/replit-code-v1_5-3b/blob/main/modeling_mpt.py
use candle::{IndexOp, Module, Result, Tensor, D};
use candle_nn::LayerNorm;
pub use super::mpt::Config;
#[derive(Debug, Clone)]
struct GroupedQueryAttention {
wqkv: Linear,
out_proj: Linear,
kv_cache: Option<(Tensor, Tensor)>,
softmax_scale: f64,
head_dim: usize,
d_model: usize,
n_heads: usize,
kv_n_heads: usize,
attn_bias: Tensor,
span: tracing::Span,
}
impl GroupedQueryAttention {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let head_dim = cfg.d_model / cfg.n_heads;
let wqkv_size = cfg.d_model + 2 * cfg.kv_n_heads * head_dim;
let wqkv = linear_no_bias(cfg.d_model, wqkv_size, vb.pp("Wqkv"))?;
let softmax_scale = 1f64 / (head_dim as f64).sqrt();
let out_proj = linear_no_bias(cfg.d_model, cfg.d_model, vb.pp("out_proj"))?;
let attn_bias = super::mpt::build_alibi_bias(cfg)?.to_device(vb.device())?;
Ok(Self {
wqkv,
out_proj,
kv_cache: None,
softmax_scale,
head_dim,
d_model: cfg.d_model,
n_heads: cfg.n_heads,
kv_n_heads: cfg.kv_n_heads,
attn_bias,
span: tracing::span!(tracing::Level::TRACE, "gqa"),
})
}
fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
let _enter = self.span.enter();
let (b_size, seq_len, _n_embd) = xs.dims3()?;
let qkv = self.wqkv.forward(xs)?;
let query = qkv.narrow(2, 0, self.d_model)?;
let kv_size = self.kv_n_heads * self.head_dim;
let key = qkv.narrow(2, self.d_model, kv_size)?;
let value = qkv.narrow(2, self.d_model + kv_size, kv_size)?;
// scaled_multihead_dot_product_attention
let query = query
.reshape((b_size, seq_len, self.n_heads, ()))?
.transpose(1, 2)?; // b,h,s,d
let key = key
.reshape((b_size, seq_len, self.kv_n_heads, ()))?
.permute((0, 2, 3, 1))?; // b,h,d,s
let value = value
.reshape((b_size, seq_len, self.kv_n_heads, ()))?
.transpose(1, 2)?; // b,h,s,d
let (key, value) = match &self.kv_cache {
None => (key, value),
Some((prev_k, prev_v)) => {
let k = Tensor::cat(&[prev_k, &key], 3)?;
let v = Tensor::cat(&[prev_v, &value], 2)?;
(k, v)
}
};
self.kv_cache = Some((key.clone(), value.clone()));
let query = query.contiguous()?;
let key = crate::utils::repeat_kv(key, self.n_heads / self.kv_n_heads)?.contiguous()?;
let value = crate::utils::repeat_kv(value, self.n_heads / self.kv_n_heads)?.contiguous()?;
let attn_weights = (query.matmul(&key)? * self.softmax_scale)?;
let attn_bias = {
let s_q = query.dim(D::Minus2)?;
let s_k = key.dim(D::Minus1)?;
let (_, _, a_q, a_k) = self.attn_bias.dims4()?;
let start_q = a_q.saturating_sub(s_q);
let start_k = a_k.saturating_sub(s_k);
self.attn_bias.i((.., .., start_q.., start_k..))?
};
let attn_weights = attn_weights.broadcast_add(&attn_bias)?;
let attn_weights = match mask {
None => attn_weights,
Some(mask) => super::mpt::masked_fill(
&attn_weights,
&mask.broadcast_as(attn_weights.shape())?,
f32::NEG_INFINITY,
)?,
};
let attn_weights = candle_nn::ops::softmax_last_dim(&attn_weights)?;
let attn_output = attn_weights
.matmul(&value)?
.transpose(1, 2)?
.flatten_from(D::Minus2)?;
let out = attn_output.apply(&self.out_proj)?;
Ok(out)
}
}
#[derive(Debug, Clone)]
struct Ffn {
up_proj: Linear,
down_proj: Linear,
}
impl Ffn {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let hidden = cfg.d_model * cfg.expansion_ratio;
let up_proj = linear_no_bias(cfg.d_model, hidden, vb.pp("up_proj"))?;
let down_proj = linear_no_bias(hidden, cfg.d_model, vb.pp("down_proj"))?;
Ok(Self { up_proj, down_proj })
}
}
impl Module for Ffn {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
xs.apply(&self.up_proj)?.gelu_erf()?.apply(&self.down_proj)
}
}
#[derive(Debug, Clone)]
struct MPTBlock {
norm1: LayerNorm, // Do we need the low-precision variant?
attn: GroupedQueryAttention,
norm2: LayerNorm,
ffn: Ffn,
}
impl MPTBlock {
fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let norm1 = layer_norm_no_bias(cfg.d_model, 1e-5, vb.pp("norm_1"))?;
let norm2 = layer_norm_no_bias(cfg.d_model, 1e-5, vb.pp("norm_2"))?;
let attn = GroupedQueryAttention::new(cfg, vb.pp("attn"))?;
let ffn = Ffn::new(cfg, vb.pp("ffn"))?;
Ok(Self {
norm1,
attn,
norm2,
ffn,
})
}
fn forward(&mut self, xs: &Tensor, mask: Option<&Tensor>) -> Result<Tensor> {
let residual = xs;
let xs = xs.apply(&self.norm1)?;
let xs = self.attn.forward(&xs, mask)?;
let xs = (xs + residual)?;
let residual = &xs;
let xs = xs.apply(&self.norm2)?.apply(&self.ffn)?;
xs + residual
}
}
#[derive(Debug, Clone)]
pub struct Model {
wte: Embedding,
blocks: Vec<MPTBlock>,
norm_f: LayerNorm,
}
impl Model {
pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
let wte = Embedding::new(cfg.vocab_size, cfg.d_model, vb.pp("wte"))?;
let vb_b = vb.pp("blocks");
let mut blocks = Vec::with_capacity(cfg.n_layers);
for i in 0..cfg.n_layers {
let block = MPTBlock::new(cfg, vb_b.pp(i))?;
blocks.push(block)
}
let norm_f = layer_norm_no_bias(cfg.d_model, 1e-5, vb.pp("norm_f"))?;
Ok(Self {
wte,
blocks,
norm_f,
})
}
pub fn forward(&mut self, xs: &Tensor) -> Result<Tensor> {
let (_b_size, seq_len) = xs.dims2()?;
let mut xs = xs.apply(&self.wte)?;
let mask = if seq_len <= 1 {
None
} else {
Some(super::mpt::get_mask(seq_len, xs.device())?)
};
for block in self.blocks.iter_mut() {
xs = block.forward(&xs, mask.as_ref())?;
}
let xs = xs.apply(&self.norm_f)?;
let logits = xs
.narrow(1, seq_len - 1, 1)?
.squeeze(1)?
.matmul(&self.wte.embeddings().t()?)?
.squeeze(1)?;
Ok(logits)
}
}
| candle/candle-transformers/src/models/quantized_mpt.rs/0 | {
"file_path": "candle/candle-transformers/src/models/quantized_mpt.rs",
"repo_id": "candle",
"token_count": 3969
} | 63 |
//! # ResNet Implementation
//!
//! Implementation of ResNet architectures as described in the paper:
//!
//! ## Reference
//!
//! [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
//! He et al. (2015)
//!
//! This paper introduced ResNet, a deep neural network architecture that utilizes
//! skip connections ("residual connections") to enable training of very deep networks.
use candle::{Result, D};
use candle_nn::{batch_norm, Conv2d, Func, VarBuilder};
fn conv2d(
c_in: usize,
c_out: usize,
ksize: usize,
padding: usize,
stride: usize,
vb: VarBuilder,
) -> Result<Conv2d> {
let conv2d_cfg = candle_nn::Conv2dConfig {
stride,
padding,
..Default::default()
};
candle_nn::conv2d_no_bias(c_in, c_out, ksize, conv2d_cfg, vb)
}
fn downsample(c_in: usize, c_out: usize, stride: usize, vb: VarBuilder) -> Result<Func> {
if stride != 1 || c_in != c_out {
let conv = conv2d(c_in, c_out, 1, 0, stride, vb.pp(0))?;
let bn = batch_norm(c_out, 1e-5, vb.pp(1))?;
Ok(Func::new(move |xs| xs.apply(&conv)?.apply_t(&bn, false)))
} else {
Ok(Func::new(|xs| Ok(xs.clone())))
}
}
fn basic_block(c_in: usize, c_out: usize, stride: usize, vb: VarBuilder) -> Result<Func> {
let conv1 = conv2d(c_in, c_out, 3, 1, stride, vb.pp("conv1"))?;
let bn1 = batch_norm(c_out, 1e-5, vb.pp("bn1"))?;
let conv2 = conv2d(c_out, c_out, 3, 1, 1, vb.pp("conv2"))?;
let bn2 = batch_norm(c_out, 1e-5, vb.pp("bn2"))?;
let downsample = downsample(c_in, c_out, stride, vb.pp("downsample"))?;
Ok(Func::new(move |xs| {
let ys = xs
.apply(&conv1)?
.apply_t(&bn1, false)?
.relu()?
.apply(&conv2)?
.apply_t(&bn2, false)?;
(xs.apply(&downsample)? + ys)?.relu()
}))
}
fn basic_layer(
c_in: usize,
c_out: usize,
stride: usize,
cnt: usize,
vb: VarBuilder,
) -> Result<Func> {
let mut layers = Vec::with_capacity(cnt);
for index in 0..cnt {
let l_in = if index == 0 { c_in } else { c_out };
let stride = if index == 0 { stride } else { 1 };
layers.push(basic_block(l_in, c_out, stride, vb.pp(index))?)
}
Ok(Func::new(move |xs| {
let mut xs = xs.clone();
for layer in layers.iter() {
xs = xs.apply(layer)?
}
Ok(xs)
}))
}
fn resnet(
nclasses: Option<usize>,
c1: usize,
c2: usize,
c3: usize,
c4: usize,
vb: VarBuilder,
) -> Result<Func> {
let conv1 = conv2d(3, 64, 7, 3, 2, vb.pp("conv1"))?;
let bn1 = batch_norm(64, 1e-5, vb.pp("bn1"))?;
let layer1 = basic_layer(64, 64, 1, c1, vb.pp("layer1"))?;
let layer2 = basic_layer(64, 128, 2, c2, vb.pp("layer2"))?;
let layer3 = basic_layer(128, 256, 2, c3, vb.pp("layer3"))?;
let layer4 = basic_layer(256, 512, 2, c4, vb.pp("layer4"))?;
let fc = match nclasses {
None => None,
Some(nclasses) => {
let linear = candle_nn::linear(512, nclasses, vb.pp("fc"))?;
Some(linear)
}
};
Ok(Func::new(move |xs| {
let xs = xs
.apply(&conv1)?
.apply_t(&bn1, false)?
.relu()?
.pad_with_same(D::Minus1, 1, 1)?
.pad_with_same(D::Minus2, 1, 1)?
.max_pool2d_with_stride(3, 2)?
.apply(&layer1)?
.apply(&layer2)?
.apply(&layer3)?
.apply(&layer4)?
.mean(D::Minus1)?
.mean(D::Minus1)?;
match &fc {
None => Ok(xs),
Some(fc) => xs.apply(fc),
}
}))
}
/// Creates a ResNet-18 model.
pub fn resnet18(num_classes: usize, vb: VarBuilder) -> Result<Func> {
resnet(Some(num_classes), 2, 2, 2, 2, vb)
}
pub fn resnet18_no_final_layer(vb: VarBuilder) -> Result<Func> {
resnet(None, 2, 2, 2, 2, vb)
}
/// Creates a ResNet-34 model.
pub fn resnet34(num_classes: usize, vb: VarBuilder) -> Result<Func> {
resnet(Some(num_classes), 3, 4, 6, 3, vb)
}
pub fn resnet34_no_final_layer(vb: VarBuilder) -> Result<Func> {
resnet(None, 3, 4, 6, 3, vb)
}
// Bottleneck versions for ResNet 50, 101, and 152.
fn bottleneck_block(
c_in: usize,
c_out: usize,
stride: usize,
e: usize,
vb: VarBuilder,
) -> Result<Func> {
let e_dim = e * c_out;
let conv1 = conv2d(c_in, c_out, 1, 0, 1, vb.pp("conv1"))?;
let bn1 = batch_norm(c_out, 1e-5, vb.pp("bn1"))?;
let conv2 = conv2d(c_out, c_out, 3, 1, stride, vb.pp("conv2"))?;
let bn2 = batch_norm(c_out, 1e-5, vb.pp("bn2"))?;
let conv3 = conv2d(c_out, e_dim, 1, 0, 1, vb.pp("conv3"))?;
let bn3 = batch_norm(e_dim, 1e-5, vb.pp("bn3"))?;
let downsample = downsample(c_in, e_dim, stride, vb.pp("downsample"))?;
Ok(Func::new(move |xs| {
let ys = xs
.apply(&conv1)?
.apply_t(&bn1, false)?
.relu()?
.apply(&conv2)?
.apply_t(&bn2, false)?
.relu()?
.apply(&conv3)?
.apply_t(&bn3, false)?;
(xs.apply(&downsample)? + ys)?.relu()
}))
}
fn bottleneck_layer(
c_in: usize,
c_out: usize,
stride: usize,
cnt: usize,
vb: VarBuilder,
) -> Result<Func> {
let mut layers = Vec::with_capacity(cnt);
for index in 0..cnt {
let l_in = if index == 0 { c_in } else { 4 * c_out };
let stride = if index == 0 { stride } else { 1 };
layers.push(bottleneck_block(l_in, c_out, stride, 4, vb.pp(index))?)
}
Ok(Func::new(move |xs| {
let mut xs = xs.clone();
for layer in layers.iter() {
xs = xs.apply(layer)?
}
Ok(xs)
}))
}
fn bottleneck_resnet(
nclasses: Option<usize>,
c1: usize,
c2: usize,
c3: usize,
c4: usize,
vb: VarBuilder,
) -> Result<Func> {
let conv1 = conv2d(3, 64, 7, 3, 2, vb.pp("conv1"))?;
let bn1 = batch_norm(64, 1e-5, vb.pp("bn1"))?;
let layer1 = bottleneck_layer(64, 64, 1, c1, vb.pp("layer1"))?;
let layer2 = bottleneck_layer(4 * 64, 128, 2, c2, vb.pp("layer2"))?;
let layer3 = bottleneck_layer(4 * 128, 256, 2, c3, vb.pp("layer3"))?;
let layer4 = bottleneck_layer(4 * 256, 512, 2, c4, vb.pp("layer4"))?;
let fc = match nclasses {
None => None,
Some(nclasses) => {
let linear = candle_nn::linear(4 * 512, nclasses, vb.pp("fc"))?;
Some(linear)
}
};
Ok(Func::new(move |xs| {
let xs = xs
.apply(&conv1)?
.apply_t(&bn1, false)?
.relu()?
.pad_with_same(D::Minus1, 1, 1)?
.pad_with_same(D::Minus2, 1, 1)?
.max_pool2d_with_stride(3, 2)?
.apply(&layer1)?
.apply(&layer2)?
.apply(&layer3)?
.apply(&layer4)?
.mean(D::Minus1)?
.mean(D::Minus1)?;
match &fc {
None => Ok(xs),
Some(fc) => xs.apply(fc),
}
}))
}
pub fn resnet50(num_classes: usize, vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(Some(num_classes), 3, 4, 6, 3, vb)
}
pub fn resnet50_no_final_layer(vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(None, 3, 4, 6, 3, vb)
}
pub fn resnet101(num_classes: usize, vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(Some(num_classes), 3, 4, 23, 3, vb)
}
pub fn resnet101_no_final_layer(vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(None, 3, 4, 23, 3, vb)
}
pub fn resnet152(num_classes: usize, vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(Some(num_classes), 3, 8, 36, 3, vb)
}
pub fn resnet152_no_final_layer(vb: VarBuilder) -> Result<Func> {
bottleneck_resnet(None, 3, 8, 36, 3, vb)
}
| candle/candle-transformers/src/models/resnet.rs/0 | {
"file_path": "candle/candle-transformers/src/models/resnet.rs",
"repo_id": "candle",
"token_count": 4023
} | 64 |
use super::schedulers::{betas_for_alpha_bar, BetaSchedule, PredictionType};
use candle::{Result, Tensor};
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum DDPMVarianceType {
FixedSmall,
FixedSmallLog,
FixedLarge,
FixedLargeLog,
Learned,
}
impl Default for DDPMVarianceType {
fn default() -> Self {
Self::FixedSmall
}
}
#[derive(Debug, Clone)]
pub struct DDPMSchedulerConfig {
/// The value of beta at the beginning of training.
pub beta_start: f64,
/// The value of beta at the end of training.
pub beta_end: f64,
/// How beta evolved during training.
pub beta_schedule: BetaSchedule,
/// Option to predicted sample between -1 and 1 for numerical stability.
pub clip_sample: bool,
/// Option to clip the variance used when adding noise to the denoised sample.
pub variance_type: DDPMVarianceType,
/// prediction type of the scheduler function
pub prediction_type: PredictionType,
/// number of diffusion steps used to train the model.
pub train_timesteps: usize,
}
impl Default for DDPMSchedulerConfig {
fn default() -> Self {
Self {
beta_start: 0.00085,
beta_end: 0.012,
beta_schedule: BetaSchedule::ScaledLinear,
clip_sample: false,
variance_type: DDPMVarianceType::FixedSmall,
prediction_type: PredictionType::Epsilon,
train_timesteps: 1000,
}
}
}
pub struct DDPMScheduler {
alphas_cumprod: Vec<f64>,
init_noise_sigma: f64,
timesteps: Vec<usize>,
step_ratio: usize,
pub config: DDPMSchedulerConfig,
}
impl DDPMScheduler {
pub fn new(inference_steps: usize, config: DDPMSchedulerConfig) -> Result<Self> {
let betas = match config.beta_schedule {
BetaSchedule::ScaledLinear => super::utils::linspace(
config.beta_start.sqrt(),
config.beta_end.sqrt(),
config.train_timesteps,
)?
.sqr()?,
BetaSchedule::Linear => {
super::utils::linspace(config.beta_start, config.beta_end, config.train_timesteps)?
}
BetaSchedule::SquaredcosCapV2 => betas_for_alpha_bar(config.train_timesteps, 0.999)?,
};
let betas = betas.to_vec1::<f64>()?;
let mut alphas_cumprod = Vec::with_capacity(betas.len());
for &beta in betas.iter() {
let alpha = 1.0 - beta;
alphas_cumprod.push(alpha * *alphas_cumprod.last().unwrap_or(&1f64))
}
// min(train_timesteps, inference_steps)
// https://github.com/huggingface/diffusers/blob/8331da46837be40f96fbd24de6a6fb2da28acd11/src/diffusers/schedulers/scheduling_ddpm.py#L187
let inference_steps = inference_steps.min(config.train_timesteps);
// arange the number of the scheduler's timesteps
let step_ratio = config.train_timesteps / inference_steps;
let timesteps: Vec<usize> = (0..inference_steps).map(|s| s * step_ratio).rev().collect();
Ok(Self {
alphas_cumprod,
init_noise_sigma: 1.0,
timesteps,
step_ratio,
config,
})
}
fn get_variance(&self, timestep: usize) -> f64 {
let prev_t = timestep as isize - self.step_ratio as isize;
let alpha_prod_t = self.alphas_cumprod[timestep];
let alpha_prod_t_prev = if prev_t >= 0 {
self.alphas_cumprod[prev_t as usize]
} else {
1.0
};
let current_beta_t = 1. - alpha_prod_t / alpha_prod_t_prev;
// For t > 0, compute predicted variance βt (see formula (6) and (7) from [the pdf](https://arxiv.org/pdf/2006.11239.pdf))
// and sample from it to get previous sample
// x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
let variance = (1. - alpha_prod_t_prev) / (1. - alpha_prod_t) * current_beta_t;
// retrieve variance
match self.config.variance_type {
DDPMVarianceType::FixedSmall => variance.max(1e-20),
// for rl-diffuser https://arxiv.org/abs/2205.09991
DDPMVarianceType::FixedSmallLog => {
let variance = variance.max(1e-20).ln();
(variance * 0.5).exp()
}
DDPMVarianceType::FixedLarge => current_beta_t,
DDPMVarianceType::FixedLargeLog => current_beta_t.ln(),
DDPMVarianceType::Learned => variance,
}
}
pub fn timesteps(&self) -> &[usize] {
self.timesteps.as_slice()
}
/// Ensures interchangeability with schedulers that need to scale the denoising model input
/// depending on the current timestep.
pub fn scale_model_input(&self, sample: Tensor, _timestep: usize) -> Tensor {
sample
}
pub fn step(&self, model_output: &Tensor, timestep: usize, sample: &Tensor) -> Result<Tensor> {
let prev_t = timestep as isize - self.step_ratio as isize;
// https://github.com/huggingface/diffusers/blob/df2b548e893ccb8a888467c2508756680df22821/src/diffusers/schedulers/scheduling_ddpm.py#L272
// 1. compute alphas, betas
let alpha_prod_t = self.alphas_cumprod[timestep];
let alpha_prod_t_prev = if prev_t >= 0 {
self.alphas_cumprod[prev_t as usize]
} else {
1.0
};
let beta_prod_t = 1. - alpha_prod_t;
let beta_prod_t_prev = 1. - alpha_prod_t_prev;
let current_alpha_t = alpha_prod_t / alpha_prod_t_prev;
let current_beta_t = 1. - current_alpha_t;
// 2. compute predicted original sample from predicted noise also called "predicted x_0" of formula (15)
let mut pred_original_sample = match self.config.prediction_type {
PredictionType::Epsilon => {
((sample - model_output * beta_prod_t.sqrt())? / alpha_prod_t.sqrt())?
}
PredictionType::Sample => model_output.clone(),
PredictionType::VPrediction => {
((sample * alpha_prod_t.sqrt())? - model_output * beta_prod_t.sqrt())?
}
};
// 3. clip predicted x_0
if self.config.clip_sample {
pred_original_sample = pred_original_sample.clamp(-1f32, 1f32)?;
}
// 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
// See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
let pred_original_sample_coeff = (alpha_prod_t_prev.sqrt() * current_beta_t) / beta_prod_t;
let current_sample_coeff = current_alpha_t.sqrt() * beta_prod_t_prev / beta_prod_t;
// 5. Compute predicted previous sample µ_t
// See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
let pred_prev_sample = ((&pred_original_sample * pred_original_sample_coeff)?
+ sample * current_sample_coeff)?;
// https://github.com/huggingface/diffusers/blob/df2b548e893ccb8a888467c2508756680df22821/src/diffusers/schedulers/scheduling_ddpm.py#L305
// 6. Add noise
let mut variance = model_output.zeros_like()?;
if timestep > 0 {
let variance_noise = model_output.randn_like(0., 1.)?;
if self.config.variance_type == DDPMVarianceType::FixedSmallLog {
variance = (variance_noise * self.get_variance(timestep))?;
} else {
variance = (variance_noise * self.get_variance(timestep).sqrt())?;
}
}
&pred_prev_sample + variance
}
pub fn add_noise(
&self,
original_samples: &Tensor,
noise: Tensor,
timestep: usize,
) -> Result<Tensor> {
(original_samples * self.alphas_cumprod[timestep].sqrt())?
+ noise * (1. - self.alphas_cumprod[timestep]).sqrt()
}
pub fn init_noise_sigma(&self) -> f64 {
self.init_noise_sigma
}
}
| candle/candle-transformers/src/models/stable_diffusion/ddpm.rs/0 | {
"file_path": "candle/candle-transformers/src/models/stable_diffusion/ddpm.rs",
"repo_id": "candle",
"token_count": 3666
} | 65 |
//! VGG-16 model implementation.
//!
//! VGG-16 is a convolutional neural network architecture. It consists of 13
//! convolutional layers followed by 3 fully connected layers.
//!
//! Key characteristics:
//! - Conv layers with 3x3 filters
//! - Max pooling after every 2-3 conv layers
//! - Three fully connected layers of 4096, 4096, 1000 units
//! - ReLU activation and dropout
//!
//! References:
//! - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
//!
use candle::{ModuleT, Result, Tensor};
use candle_nn::{FuncT, VarBuilder};
// Enum representing the different VGG models
pub enum Models {
Vgg13,
Vgg16,
Vgg19,
}
// Struct representing a VGG model
#[derive(Debug)]
pub struct Vgg<'a> {
blocks: Vec<FuncT<'a>>,
}
// Struct representing the configuration for the pre-logit layer
struct PreLogitConfig {
in_dim: (usize, usize, usize, usize),
target_in: usize,
target_out: usize,
}
// Implementation of the VGG model
impl<'a> Vgg<'a> {
// Function to create a new VGG model
pub fn new(vb: VarBuilder<'a>, model: Models) -> Result<Self> {
let blocks = match model {
Models::Vgg13 => vgg13_blocks(vb)?,
Models::Vgg16 => vgg16_blocks(vb)?,
Models::Vgg19 => vgg19_blocks(vb)?,
};
Ok(Self { blocks })
}
}
// Implementation of the forward pass for the VGG model
impl ModuleT for Vgg<'_> {
fn forward_t(&self, xs: &Tensor, train: bool) -> Result<Tensor> {
let mut xs = xs.unsqueeze(0)?;
for block in self.blocks.iter() {
xs = xs.apply_t(block, train)?;
}
Ok(xs)
}
}
// Function to create a conv2d block
// The block is composed of two conv2d layers followed by a max pool layer
fn conv2d_block(convs: &[(usize, usize, &str)], vb: &VarBuilder) -> Result<FuncT<'static>> {
let layers = convs
.iter()
.map(|&(in_c, out_c, name)| {
candle_nn::conv2d(
in_c,
out_c,
3,
candle_nn::Conv2dConfig {
stride: 1,
padding: 1,
..Default::default()
},
vb.pp(name),
)
})
.collect::<Result<Vec<_>>>()?;
Ok(FuncT::new(move |xs, _train| {
let mut xs = xs.clone();
for layer in layers.iter() {
xs = xs.apply(layer)?.relu()?
}
xs = xs.max_pool2d_with_stride(2, 2)?;
Ok(xs)
}))
}
// Function to create a fully connected layer
// The layer is composed of two linear layers followed by a dropout layer
fn fully_connected(
num_classes: usize,
pre_logit_1: PreLogitConfig,
pre_logit_2: PreLogitConfig,
vb: VarBuilder,
) -> Result<FuncT> {
let lin = get_weights_and_biases(
&vb.pp("pre_logits.fc1"),
pre_logit_1.in_dim,
pre_logit_1.target_in,
pre_logit_1.target_out,
)?;
let lin2 = get_weights_and_biases(
&vb.pp("pre_logits.fc2"),
pre_logit_2.in_dim,
pre_logit_2.target_in,
pre_logit_2.target_out,
)?;
let dropout1 = candle_nn::Dropout::new(0.5);
let dropout2 = candle_nn::Dropout::new(0.5);
let dropout3 = candle_nn::Dropout::new(0.5);
Ok(FuncT::new(move |xs, train| {
let xs = xs.reshape((1, pre_logit_1.target_out))?;
let xs = xs.apply_t(&dropout1, train)?.apply(&lin)?.relu()?;
let xs = xs.apply_t(&dropout2, train)?.apply(&lin2)?.relu()?;
let lin3 = candle_nn::linear(4096, num_classes, vb.pp("head.fc"))?;
let xs = xs.apply_t(&dropout3, train)?.apply(&lin3)?.relu()?;
Ok(xs)
}))
}
// Function to get the weights and biases for a layer
// This is required because the weights and biases are stored in different format than our linear layer expects
fn get_weights_and_biases(
vs: &VarBuilder,
in_dim: (usize, usize, usize, usize),
target_in: usize,
target_out: usize,
) -> Result<candle_nn::Linear> {
let init_ws = candle_nn::init::DEFAULT_KAIMING_NORMAL;
let ws = vs.get_with_hints(in_dim, "weight", init_ws)?;
let ws = ws.reshape((target_in, target_out))?;
let bound = 1. / (target_out as f64).sqrt();
let init_bs = candle_nn::Init::Uniform {
lo: -bound,
up: bound,
};
let bs = vs.get_with_hints(target_in, "bias", init_bs)?;
Ok(candle_nn::Linear::new(ws, Some(bs)))
}
fn vgg13_blocks(vb: VarBuilder) -> Result<Vec<FuncT>> {
let num_classes = 1000;
let blocks = vec![
conv2d_block(&[(3, 64, "features.0"), (64, 64, "features.2")], &vb)?,
conv2d_block(&[(64, 128, "features.5"), (128, 128, "features.7")], &vb)?,
conv2d_block(&[(128, 256, "features.10"), (256, 256, "features.12")], &vb)?,
conv2d_block(&[(256, 512, "features.15"), (512, 512, "features.17")], &vb)?,
conv2d_block(&[(512, 512, "features.20"), (512, 512, "features.22")], &vb)?,
fully_connected(
num_classes,
PreLogitConfig {
in_dim: (4096, 512, 7, 7),
target_in: 4096,
target_out: 512 * 7 * 7,
},
PreLogitConfig {
in_dim: (4096, 4096, 1, 1),
target_in: 4096,
target_out: 4096,
},
vb.clone(),
)?,
];
Ok(blocks)
}
fn vgg16_blocks(vb: VarBuilder) -> Result<Vec<FuncT>> {
let num_classes = 1000;
let blocks = vec![
conv2d_block(&[(3, 64, "features.0"), (64, 64, "features.2")], &vb)?,
conv2d_block(&[(64, 128, "features.5"), (128, 128, "features.7")], &vb)?,
conv2d_block(
&[
(128, 256, "features.10"),
(256, 256, "features.12"),
(256, 256, "features.14"),
],
&vb,
)?,
conv2d_block(
&[
(256, 512, "features.17"),
(512, 512, "features.19"),
(512, 512, "features.21"),
],
&vb,
)?,
conv2d_block(
&[
(512, 512, "features.24"),
(512, 512, "features.26"),
(512, 512, "features.28"),
],
&vb,
)?,
fully_connected(
num_classes,
PreLogitConfig {
in_dim: (4096, 512, 7, 7),
target_in: 4096,
target_out: 512 * 7 * 7,
},
PreLogitConfig {
in_dim: (4096, 4096, 1, 1),
target_in: 4096,
target_out: 4096,
},
vb.clone(),
)?,
];
Ok(blocks)
}
fn vgg19_blocks(vb: VarBuilder) -> Result<Vec<FuncT>> {
let num_classes = 1000;
let blocks = vec![
conv2d_block(&[(3, 64, "features.0"), (64, 64, "features.2")], &vb)?,
conv2d_block(&[(64, 128, "features.5"), (128, 128, "features.7")], &vb)?,
conv2d_block(
&[
(128, 256, "features.10"),
(256, 256, "features.12"),
(256, 256, "features.14"),
(256, 256, "features.16"),
],
&vb,
)?,
conv2d_block(
&[
(256, 512, "features.19"),
(512, 512, "features.21"),
(512, 512, "features.23"),
(512, 512, "features.25"),
],
&vb,
)?,
conv2d_block(
&[
(512, 512, "features.28"),
(512, 512, "features.30"),
(512, 512, "features.32"),
(512, 512, "features.34"),
],
&vb,
)?,
fully_connected(
num_classes,
PreLogitConfig {
in_dim: (4096, 512, 7, 7),
target_in: 4096,
target_out: 512 * 7 * 7,
},
PreLogitConfig {
in_dim: (4096, 4096, 1, 1),
target_in: 4096,
target_out: 4096,
},
vb.clone(),
)?,
];
Ok(blocks)
}
| candle/candle-transformers/src/models/vgg.rs/0 | {
"file_path": "candle/candle-transformers/src/models/vgg.rs",
"repo_id": "candle",
"token_count": 4390
} | 66 |
use super::common::LayerNormNoWeights;
use candle::{Module, Result, Tensor};
use candle_nn::VarBuilder;
#[derive(Debug)]
pub struct MixingResidualBlock {
norm1: LayerNormNoWeights,
depthwise_conv: candle_nn::Conv2d,
norm2: LayerNormNoWeights,
channelwise_lin1: candle_nn::Linear,
channelwise_lin2: candle_nn::Linear,
gammas: Vec<f32>,
}
impl MixingResidualBlock {
pub fn new(inp: usize, embed_dim: usize, vb: VarBuilder) -> Result<Self> {
let norm1 = LayerNormNoWeights::new(inp)?;
let norm2 = LayerNormNoWeights::new(inp)?;
let cfg = candle_nn::Conv2dConfig {
groups: inp,
..Default::default()
};
let depthwise_conv = candle_nn::conv2d(inp, inp, 3, cfg, vb.pp("depthwise.1"))?;
let channelwise_lin1 = candle_nn::linear(inp, embed_dim, vb.pp("channelwise.0"))?;
let channelwise_lin2 = candle_nn::linear(embed_dim, inp, vb.pp("channelwise.2"))?;
let gammas = vb.get(6, "gammas")?.to_vec1::<f32>()?;
Ok(Self {
norm1,
depthwise_conv,
norm2,
channelwise_lin1,
channelwise_lin2,
gammas,
})
}
}
impl Module for MixingResidualBlock {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
let mods = &self.gammas;
let x_temp = xs
.permute((0, 2, 3, 1))?
.apply(&self.norm1)?
.permute((0, 3, 1, 2))?
.affine(1. + mods[0] as f64, mods[1] as f64)?;
let x_temp = candle_nn::ops::replication_pad2d(&x_temp, 1)?;
let xs = (xs + x_temp.apply(&self.depthwise_conv)? * mods[2] as f64)?;
let x_temp = xs
.permute((0, 2, 3, 1))?
.apply(&self.norm2)?
.permute((0, 3, 1, 2))?
.affine(1. + mods[3] as f64, mods[4] as f64)?;
let x_temp = x_temp
.permute((0, 2, 3, 1))?
.contiguous()?
.apply(&self.channelwise_lin1)?
.gelu()?
.apply(&self.channelwise_lin2)?
.permute((0, 3, 1, 2))?;
xs + x_temp * mods[5] as f64
}
}
#[derive(Debug)]
pub struct PaellaVQ {
in_block_conv: candle_nn::Conv2d,
out_block_conv: candle_nn::Conv2d,
down_blocks: Vec<(Option<candle_nn::Conv2d>, MixingResidualBlock)>,
down_blocks_conv: candle_nn::Conv2d,
down_blocks_bn: candle_nn::BatchNorm,
up_blocks_conv: candle_nn::Conv2d,
up_blocks: Vec<(Vec<MixingResidualBlock>, Option<candle_nn::ConvTranspose2d>)>,
}
impl PaellaVQ {
pub fn new(vb: VarBuilder) -> Result<Self> {
const IN_CHANNELS: usize = 3;
const OUT_CHANNELS: usize = 3;
const LATENT_CHANNELS: usize = 4;
const EMBED_DIM: usize = 384;
const BOTTLENECK_BLOCKS: usize = 12;
const C_LEVELS: [usize; 2] = [EMBED_DIM / 2, EMBED_DIM];
let in_block_conv = candle_nn::conv2d(
IN_CHANNELS * 4,
C_LEVELS[0],
1,
Default::default(),
vb.pp("in_block.1"),
)?;
let out_block_conv = candle_nn::conv2d(
C_LEVELS[0],
OUT_CHANNELS * 4,
1,
Default::default(),
vb.pp("out_block.0"),
)?;
let mut down_blocks = Vec::new();
let vb_d = vb.pp("down_blocks");
let mut d_idx = 0;
for (i, &c_level) in C_LEVELS.iter().enumerate() {
let conv_block = if i > 0 {
let cfg = candle_nn::Conv2dConfig {
padding: 1,
stride: 2,
..Default::default()
};
let block = candle_nn::conv2d(C_LEVELS[i - 1], c_level, 4, cfg, vb_d.pp(d_idx))?;
d_idx += 1;
Some(block)
} else {
None
};
let res_block = MixingResidualBlock::new(c_level, c_level * 4, vb_d.pp(d_idx))?;
d_idx += 1;
down_blocks.push((conv_block, res_block))
}
let vb_d = vb_d.pp(d_idx);
let down_blocks_conv = candle_nn::conv2d_no_bias(
C_LEVELS[1],
LATENT_CHANNELS,
1,
Default::default(),
vb_d.pp(0),
)?;
let down_blocks_bn = candle_nn::batch_norm(LATENT_CHANNELS, 1e-5, vb_d.pp(1))?;
let mut up_blocks = Vec::new();
let vb_u = vb.pp("up_blocks");
let mut u_idx = 0;
let up_blocks_conv = candle_nn::conv2d(
LATENT_CHANNELS,
C_LEVELS[1],
1,
Default::default(),
vb_u.pp(u_idx).pp(0),
)?;
u_idx += 1;
for (i, &c_level) in C_LEVELS.iter().rev().enumerate() {
let mut res_blocks = Vec::new();
let n_bottleneck_blocks = if i == 0 { BOTTLENECK_BLOCKS } else { 1 };
for _j in 0..n_bottleneck_blocks {
let res_block = MixingResidualBlock::new(c_level, c_level * 4, vb_u.pp(u_idx))?;
u_idx += 1;
res_blocks.push(res_block)
}
let conv_block = if i < C_LEVELS.len() - 1 {
let cfg = candle_nn::ConvTranspose2dConfig {
padding: 1,
stride: 2,
..Default::default()
};
let block = candle_nn::conv_transpose2d(
c_level,
C_LEVELS[C_LEVELS.len() - i - 2],
4,
cfg,
vb_u.pp(u_idx),
)?;
u_idx += 1;
Some(block)
} else {
None
};
up_blocks.push((res_blocks, conv_block))
}
Ok(Self {
in_block_conv,
down_blocks,
down_blocks_conv,
down_blocks_bn,
up_blocks,
up_blocks_conv,
out_block_conv,
})
}
pub fn encode(&self, xs: &Tensor) -> Result<Tensor> {
let mut xs = candle_nn::ops::pixel_unshuffle(xs, 2)?.apply(&self.in_block_conv)?;
for down_block in self.down_blocks.iter() {
if let Some(conv) = &down_block.0 {
xs = xs.apply(conv)?
}
xs = xs.apply(&down_block.1)?
}
xs.apply(&self.down_blocks_conv)?
.apply_t(&self.down_blocks_bn, false)
}
pub fn decode(&self, xs: &Tensor) -> Result<Tensor> {
// TODO: quantizer if we want to support `force_not_quantize=False`.
let mut xs = xs.apply(&self.up_blocks_conv)?;
for up_block in self.up_blocks.iter() {
for b in up_block.0.iter() {
xs = xs.apply(b)?;
}
if let Some(conv) = &up_block.1 {
xs = xs.apply(conv)?
}
}
xs.apply(&self.out_block_conv)?
.apply(&|xs: &_| candle_nn::ops::pixel_shuffle(xs, 2))
}
}
impl Module for PaellaVQ {
fn forward(&self, xs: &Tensor) -> Result<Tensor> {
self.decode(&self.encode(xs)?)
}
}
| candle/candle-transformers/src/models/wuerstchen/paella_vq.rs/0 | {
"file_path": "candle/candle-transformers/src/models/wuerstchen/paella_vq.rs",
"repo_id": "candle",
"token_count": 4078
} | 67 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Bert</title>
</head>
<body></body>
</html>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<style>
@import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
html,
body {
font-family: "Source Sans 3", sans-serif;
}
</style>
<script src="https://cdn.tailwindcss.com"></script>
<script type="module" src="./code.js"></script>
<script type="module">
import { hcl } from "https://cdn.skypack.dev/d3-color@3";
import { interpolateReds } from "https://cdn.skypack.dev/d3-scale-chromatic@3";
import { scaleLinear } from "https://cdn.skypack.dev/d3-scale@4";
import {
getModelInfo,
getEmbeddings,
getWikiText,
cosineSimilarity,
} from "./utils.js";
const bertWorker = new Worker("./bertWorker.js", {
type: "module",
});
const inputContainerEL = document.querySelector("#input-container");
const textAreaEl = document.querySelector("#input-area");
const outputAreaEl = document.querySelector("#output-area");
const formEl = document.querySelector("#form");
const searchInputEl = document.querySelector("#search-input");
const formWikiEl = document.querySelector("#form-wiki");
const searchWikiEl = document.querySelector("#search-wiki");
const outputStatusEl = document.querySelector("#output-status");
const modelSelectEl = document.querySelector("#model");
const sentencesRegex =
/(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<![A-Z]\.)(?<=\.|\?)\s/gm;
let sentenceEmbeddings = [];
let currInputText = "";
let isCalculating = false;
function toggleTextArea(state) {
if (state) {
textAreaEl.hidden = false;
textAreaEl.focus();
} else {
textAreaEl.hidden = true;
}
}
inputContainerEL.addEventListener("focus", (e) => {
toggleTextArea(true);
});
textAreaEl.addEventListener("blur", (e) => {
toggleTextArea(false);
});
textAreaEl.addEventListener("focusout", (e) => {
toggleTextArea(false);
if (currInputText === textAreaEl.value || isCalculating) return;
populateOutputArea(textAreaEl.value);
calculateEmbeddings(textAreaEl.value);
});
modelSelectEl.addEventListener("change", (e) => {
if (currInputText === "" || isCalculating) return;
populateOutputArea(textAreaEl.value);
calculateEmbeddings(textAreaEl.value);
});
function populateOutputArea(text) {
currInputText = text;
const sentences = text.split(sentencesRegex);
outputAreaEl.innerHTML = "";
for (const [id, sentence] of sentences.entries()) {
const sentenceEl = document.createElement("span");
sentenceEl.id = `sentence-${id}`;
sentenceEl.innerText = sentence + " ";
outputAreaEl.appendChild(sentenceEl);
}
}
formEl.addEventListener("submit", async (e) => {
e.preventDefault();
if (isCalculating || currInputText === "") return;
toggleInputs(true);
const modelID = modelSelectEl.value;
const { modelURL, tokenizerURL, configURL, search_prefix } =
getModelInfo(modelID);
const text = searchInputEl.value;
const query = search_prefix + searchInputEl.value;
outputStatusEl.classList.remove("invisible");
outputStatusEl.innerText = "Calculating embeddings for query...";
isCalculating = true;
const out = await getEmbeddings(
bertWorker,
modelURL,
tokenizerURL,
configURL,
modelID,
[query]
);
outputStatusEl.classList.add("invisible");
const queryEmbeddings = out.output[0];
// calculate cosine similarity with all sentences given the query
const distances = sentenceEmbeddings
.map((embedding, id) => ({
id,
similarity: cosineSimilarity(queryEmbeddings, embedding),
}))
.sort((a, b) => b.similarity - a.similarity)
// getting top 10 most similar sentences
.slice(0, 10);
const colorScale = scaleLinear()
.domain([
distances[distances.length - 1].similarity,
distances[0].similarity,
])
.range([0, 1])
.interpolate(() => interpolateReds);
outputAreaEl.querySelectorAll("span").forEach((el) => {
el.style.color = "unset";
el.style.backgroundColor = "unset";
});
distances.forEach((d) => {
const el = outputAreaEl.querySelector(`#sentence-${d.id}`);
const color = colorScale(d.similarity);
const fontColor = hcl(color).l < 70 ? "white" : "black";
el.style.color = fontColor;
el.style.backgroundColor = color;
});
outputAreaEl
.querySelector(`#sentence-${distances[0].id}`)
.scrollIntoView({
behavior: "smooth",
block: "center",
inline: "nearest",
});
isCalculating = false;
toggleInputs(false);
});
async function calculateEmbeddings(text) {
isCalculating = true;
toggleInputs(true);
const modelID = modelSelectEl.value;
const { modelURL, tokenizerURL, configURL, document_prefix } =
getModelInfo(modelID);
const sentences = text.split(sentencesRegex);
const allEmbeddings = [];
outputStatusEl.classList.remove("invisible");
for (const [id, sentence] of sentences.entries()) {
const query = document_prefix + sentence;
outputStatusEl.innerText = `Calculating embeddings: sentence ${
id + 1
} of ${sentences.length}`;
const embeddings = await getEmbeddings(
bertWorker,
modelURL,
tokenizerURL,
configURL,
modelID,
[query],
updateStatus
);
allEmbeddings.push(embeddings);
}
outputStatusEl.classList.add("invisible");
sentenceEmbeddings = allEmbeddings.map((e) => e.output[0]);
isCalculating = false;
toggleInputs(false);
}
function updateStatus(data) {
if ("status" in data) {
if (data.status === "loading") {
outputStatusEl.innerText = data.message;
outputStatusEl.classList.remove("invisible");
}
}
}
function toggleInputs(state) {
const interactive = document.querySelectorAll(".interactive");
interactive.forEach((el) => {
if (state) {
el.disabled = true;
} else {
el.disabled = false;
}
});
}
searchWikiEl.addEventListener("input", () => {
searchWikiEl.setCustomValidity("");
});
formWikiEl.addEventListener("submit", async (e) => {
e.preventDefault();
if ("example" in e.submitter.dataset) {
searchWikiEl.value = e.submitter.innerText;
}
const text = searchWikiEl.value;
if (isCalculating || text === "") return;
try {
const wikiText = await getWikiText(text);
searchWikiEl.setCustomValidity("");
textAreaEl.innerHTML = wikiText;
populateOutputArea(wikiText);
calculateEmbeddings(wikiText);
searchWikiEl.value = "";
} catch {
searchWikiEl.setCustomValidity("Invalid Wikipedia article name");
searchWikiEl.reportValidity();
}
});
</script>
</head>
<body class="container max-w-4xl mx-auto p-4">
<main class="grid grid-cols-1 gap-5 relative">
<span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
<div>
<h1 class="text-5xl font-bold">Candle BERT</h1>
<h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
<p class="max-w-lg">
Running sentence embeddings and similarity search in the browser using
the Bert Model written with
<a
href="https://github.com/huggingface/candle/"
target="_blank"
class="underline hover:text-blue-500 hover:no-underline"
>Candle
</a>
and compiled to Wasm. Embeddings models from are from
<a
href="https://huggingface.co/sentence-transformers/"
target="_blank"
class="underline hover:text-blue-500 hover:no-underline"
>
Sentence Transformers
</a>
and
<a
href="https://huggingface.co/intfloat/"
target="_blank"
class="underline hover:text-blue-500 hover:no-underline"
>
Liang Wang - e5 Models
</a>
</p>
</div>
<div>
<label for="model" class="font-medium block">Models Options: </label>
<select
id="model"
class="border-2 border-gray-500 rounded-md font-light interactive disabled:cursor-not-allowed w-full max-w-max"
>
<option value="intfloat_e5_small_v2" selected>
intfloat/e5-small-v2 (133 MB)
</option>
<option value="intfloat_e5_base_v2">
intfloat/e5-base-v2 (438 MB)
</option>
<option value="intfloat_multilingual_e5_small">
intfloat/multilingual-e5-small (471 MB)
</option>
<option value="sentence_transformers_all_MiniLM_L6_v2">
sentence-transformers/all-MiniLM-L6-v2 (90.9 MB)
</option>
<option value="sentence_transformers_all_MiniLM_L12_v2">
sentence-transformers/all-MiniLM-L12-v2 (133 MB)
</option>
</select>
</div>
<div>
<h3 class="font-medium">Examples:</h3>
<form
id="form-wiki"
class="flex text-xs rounded-md justify-between w-min gap-3"
>
<input type="submit" hidden />
<button data-example class="disabled:cursor-not-allowed interactive">
Pizza
</button>
<button data-example class="disabled:cursor-not-allowed interactive">
Paris
</button>
<button data-example class="disabled:cursor-not-allowed interactive">
Physics
</button>
<input
type="text"
id="search-wiki"
title="Search Wikipedia article by title"
class="font-light py-0 mx-1 resize-none outline-none w-32 disabled:cursor-not-allowed interactive"
placeholder="Load Wikipedia article..."
/>
<button
title="Search Wikipedia article and load into input"
class="bg-gray-700 hover:bg-gray-800 text-white font-normal px-2 py-1 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive"
>
Load
</button>
</form>
</div>
<form
id="form"
class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center"
>
<input type="submit" hidden />
<input
type="text"
id="search-input"
class="font-light w-full px-3 py-2 mx-1 resize-none outline-none interactive disabled:cursor-not-allowed"
placeholder="Search query here..."
/>
<button
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed interactive"
>
Search
</button>
</form>
<div>
<h3 class="font-medium">Input text:</h3>
<div class="flex justify-between items-center">
<div class="rounded-md inline text-xs">
<span id="output-status" class="m-auto font-light invisible"
>C</span
>
</div>
</div>
<div
id="input-container"
tabindex="0"
class="min-h-[250px] bg-slate-100 text-gray-500 rounded-md p-4 flex flex-col gap-2 relative"
>
<textarea
id="input-area"
hidden
value=""
placeholder="Input text to perform semantic similarity search..."
class="flex-1 resize-none outline-none left-0 right-0 top-0 bottom-0 m-4 absolute interactive disabled:invisible"
></textarea>
<p id="output-area" class="grid-rows-2">
Input text to perform semantic similarity search...
</p>
</div>
</div>
</main>
</body>
</html>
| candle/candle-wasm-examples/bert/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/bert/lib-example.html",
"repo_id": "candle",
"token_count": 6066
} | 68 |
<html>
<head>
<meta content="text/html;charset=utf-8" http-equiv="Content-Type" />
<title>Candle Llama.c Rust/WASM</title>
</head>
<body></body>
</html>
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<style>
@import url("https://fonts.googleapis.com/css2?family=Source+Code+Pro:wght@200;300;400&family=Source+Sans+3:wght@100;200;300;400;500;600;700;800;900&display=swap");
html,
body {
font-family: "Source Sans 3", sans-serif;
}
code,
output,
select,
pre {
font-family: "Source Code Pro", monospace;
}
</style>
<script src="https://cdn.tailwindcss.com"></script>
<script type="module">
// base url for audio examples
const MODELS_BASE_URL =
"https://huggingface.co/karpathy/tinyllamas/resolve/main";
// models base url
const MODELS = {
stories15M: {
url: "stories15M.bin",
seq_len: 256,
},
stories42M: {
url: "stories42M.bin",
seq_len: 1024,
},
stories110M: {
url: "stories110M.bin",
seq_len: 1024,
},
};
const llamaWorker = new Worker("./llama2cWorker.js", {
type: "module",
});
async function generateSequence(controller) {
const getValue = (id) => document.querySelector(`#${id}`).value;
const modelID = getValue("model");
const model = MODELS[modelID];
const weightsURL = `${MODELS_BASE_URL}/${model.url}`;
const prompt = getValue("prompt");
const temperature = getValue("temperature");
const topP = getValue("top-p");
const repeatPenalty = getValue("repeat_penalty");
const seed = getValue("seed");
const maxSeqLen = getValue("max-seq");
function updateStatus(data) {
const outStatus = document.querySelector("#output-status");
const outGen = document.querySelector("#output-generation");
const outCounter = document.querySelector("#output-counter");
switch (data.status) {
case "loading":
outStatus.hidden = false;
outStatus.textContent = data.message;
outGen.hidden = true;
outCounter.hidden = true;
break;
case "generating":
const { message, prompt, sentence, tokensSec, totalTime } = data;
outStatus.hidden = true;
outCounter.hidden = false;
outGen.hidden = false;
outGen.innerHTML = `<span class="font-semibold">${prompt}</span>${sentence.replace(
/\<s\>|\<\/s\>/g,
""
)}`;
outCounter.innerHTML = `${(totalTime / 1000).toFixed(
2
)}s (${tokensSec.toFixed(2)} tok/s)`;
break;
case "complete":
outStatus.hidden = true;
outGen.hidden = false;
break;
}
}
return new Promise((resolve, reject) => {
llamaWorker.postMessage({
weightsURL,
modelID,
tokenizerURL: "tokenizer.json",
prompt,
temp: temperature,
top_p: topP,
repeatPenalty,
seed: BigInt(seed),
maxSeqLen,
command: "start",
});
const handleAbort = () => {
llamaWorker.postMessage({ command: "abort" });
};
const handleMessage = (event) => {
const { status, error, message, prompt, sentence } = event.data;
if (status) updateStatus(event.data);
if (error) {
llamaWorker.removeEventListener("message", handleMessage);
reject(new Error(error));
}
if (status === "aborted") {
llamaWorker.removeEventListener("message", handleMessage);
resolve(event.data);
}
if (status === "complete") {
llamaWorker.removeEventListener("message", handleMessage);
resolve(event.data);
}
};
controller.signal.addEventListener("abort", handleAbort);
llamaWorker.addEventListener("message", handleMessage);
});
}
const form = document.querySelector("#form");
const prompt = document.querySelector("#prompt");
const clearBtn = document.querySelector("#clear-btn");
const runBtn = document.querySelector("#run");
const modelSelect = document.querySelector("#model");
let runController = new AbortController();
let isRunning = false;
modelSelect.addEventListener("change", (e) => {
const model = MODELS[e.target.value];
document.querySelector("#max-seq").max = model.seq_len;
document.querySelector("#max-seq").nextElementSibling.value =
model.seq_len;
});
form.addEventListener("submit", async (e) => {
e.preventDefault();
if (isRunning) {
stopRunning();
} else {
startRunning();
await generateSequence(runController);
stopRunning();
}
});
function startRunning() {
isRunning = true;
runBtn.textContent = "Stop";
}
function stopRunning() {
runController.abort();
runController = new AbortController();
runBtn.textContent = "Run";
isRunning = false;
}
clearBtn.addEventListener("click", (e) => {
e.preventDefault();
prompt.value = "";
clearBtn.classList.add("invisible");
runBtn.disabled = true;
stopRunning();
});
prompt.addEventListener("input", (e) => {
runBtn.disabled = false;
if (e.target.value.length > 0) {
clearBtn.classList.remove("invisible");
} else {
clearBtn.classList.add("invisible");
}
});
</script>
</head>
<body class="container max-w-4xl mx-auto p-4 text-gray-800">
<main class="grid grid-cols-1 gap-8 relative">
<span class="absolute text-5xl -ml-[1em]"> 🕯️ </span>
<div>
<h1 class="text-5xl font-bold">Candle Llama2.c</h1>
<h2 class="text-2xl font-bold">Rust/WASM Demo</h2>
<p class="max-w-lg">
<a
href="https://github.com/karpathy/llama2.c"
target="_blank"
class="underline hover:text-blue-500 hover:no-underline"
target="_blank"
>Llama2.c</a
>
is Andrey Karpathy's C implementation of the Llama 2 LLM model in C.
This demo uses
<a
href="https://github.com/huggingface/candle/"
target="_blank"
class="underline hover:text-blue-500 hover:no-underline"
>Candle
</a>
to run Llama2.c in the browser using rust/wasm.
</p>
</div>
<div>
<label for="model" class="font-medium">Models Options: </label>
<select
id="model"
class="border-2 border-gray-500 rounded-md font-light">
<option value="stories15M" selected>stories 15M (60.8 MB)</option>
<option value="stories42M">stories 42M (167 MB)</option>
<option value="stories110M">stories 110M (438 MB)</option>
</select>
</div>
<form
id="form"
class="flex text-normal px-1 py-1 border border-gray-700 rounded-md items-center">
<input type="submit" hidden />
<input
type="text"
id="prompt"
class="font-light w-full px-3 py-2 mx-1 resize-none outline-none"
placeholder="Add your prompt here..."
value="Once upon a time" />
<button id="clear-btn">
<svg
fill="none"
xmlns="http://www.w3.org/2000/svg"
width="40"
viewBox="0 0 70 40">
<path opacity=".5" d="M39 .2v40.2" stroke="#1F2937" />
<path
d="M1.5 11.5 19 29.1m0-17.6L1.5 29.1"
opacity=".5"
stroke="#1F2937"
stroke-width="2" />
</svg>
</button>
<button
id="run"
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-2 w-16 rounded disabled:bg-gray-300 disabled:cursor-not-allowed">
Run
</button>
</form>
<details>
<summary class="font-medium cursor-pointer">Advanced Options</summary>
<div class="grid grid-cols-3 max-w-md items-center gap-3 py-3">
<label class="text-sm font-medium" for="max-seq"
>Maximum length
</label>
<input
type="range"
id="max-seq"
name="max-seq"
min="1"
max="256"
step="1"
value="200"
oninput="this.nextElementSibling.value = Number(this.value)" />
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
200</output
>
<label class="text-sm font-medium" for="temperature"
>Temperature</label
>
<input
type="range"
id="temperature"
name="temperature"
min="0"
max="2"
step="0.01"
value="0.40"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)" />
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
0.40</output
>
<label class="text-sm font-medium" for="top-p">Top-p</label>
<input
type="range"
id="top-p"
name="top-p"
min="0"
max="1"
step="0.01"
value="1.00"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)" />
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md">
1.00</output
>
<label class="text-sm font-medium" for="repeat_penalty"
>Repeat Penalty</label
>
<input
type="range"
id="repeat_penalty"
name="repeat_penalty"
min="1"
max="2"
step="0.01"
value="1.10"
oninput="this.nextElementSibling.value = Number(this.value).toFixed(2)" />
<output
class="text-xs w-[50px] text-center font-light px-1 py-1 border border-gray-700 rounded-md"
>1.10</output
>
<label class="text-sm font-medium" for="seed">Seed</label>
<input
type="number"
id="seed"
name="seed"
value="299792458"
class="font-light border border-gray-700 text-right rounded-md p-2" />
<button
id="run"
onclick="document.querySelector('#seed').value = BigInt(Math.floor(Math.random() * 2**64-1))"
class="bg-gray-700 hover:bg-gray-800 text-white font-normal py-1 w-[50px] rounded disabled:bg-gray-300 disabled:cursor-not-allowed text-sm">
Rand
</button>
</div>
</details>
<div>
<h3 class="font-medium">Generation:</h3>
<div
class="min-h-[250px] bg-slate-100 text-gray-500 p-4 rounded-md flex flex-col gap-2">
<div
id="output-counter"
hidden
class="ml-auto font-semibold grid-rows-1 text-sm"></div>
<p hidden id="output-generation" class="grid-rows-2"></p>
<span id="output-status" class="m-auto font-light"
>No output yet</span
>
</div>
</div>
</main>
</body>
</html>
| candle/candle-wasm-examples/llama2-c/lib-example.html/0 | {
"file_path": "candle/candle-wasm-examples/llama2-c/lib-example.html",
"repo_id": "candle",
"token_count": 6089
} | 69 |
## Running T5 with Candle and WASM
Here, we provide two examples of how to run Bert using a Candle-compiled WASM binary and runtime.
### Vanilla JS and WebWorkers
To build and test the UI made in Vanilla JS and WebWorkers, first we need to build the WASM library:
```bash
sh build-lib.sh
```
This will bundle the library under `./build` and we can import it inside our WebWorker like a normal JS module:
```js
import init, { ModelConditionalGeneration, ModelEncoder } from "./build/m.js";
```
For the quantized version, we need to import the quantized module:
```js
import init, { ModelConditionalGeneration, ModelEncoder } from "./build/m-quantized.js";
```
The full example can be found under `./index.html`. All needed assets are fetched from the web, so no need to download anything.
Finally, you can preview the example by running a local HTTP server. For example:
```bash
python -m http.server
```
Then open `http://localhost:8000/index.html` in your browser.
| candle/candle-wasm-examples/t5/README.md/0 | {
"file_path": "candle/candle-wasm-examples/t5/README.md",
"repo_id": "candle",
"token_count": 282
} | 70 |
// Audio processing code, adapted from whisper.cpp
// https://github.com/ggerganov/whisper.cpp
use super::worker;
pub trait Float: num_traits::Float + num_traits::FloatConst + num_traits::NumAssign {}
impl Float for f32 {}
impl Float for f64 {}
// https://github.com/ggerganov/whisper.cpp/blob/4774d2feb01a772a15de81ffc34b34a1f294f020/whisper.cpp#L2357
fn fft<T: Float>(inp: &[T]) -> Vec<T> {
let n = inp.len();
let zero = T::zero();
if n == 1 {
return vec![inp[0], zero];
}
if n % 2 == 1 {
return dft(inp);
}
let mut out = vec![zero; n * 2];
let mut even = Vec::with_capacity(n / 2);
let mut odd = Vec::with_capacity(n / 2);
for (i, &inp) in inp.iter().enumerate() {
if i % 2 == 0 {
even.push(inp)
} else {
odd.push(inp);
}
}
let even_fft = fft(&even);
let odd_fft = fft(&odd);
let two_pi = T::PI() + T::PI();
let n_t = T::from(n).unwrap();
for k in 0..n / 2 {
let k_t = T::from(k).unwrap();
let theta = two_pi * k_t / n_t;
let re = theta.cos();
let im = -theta.sin();
let re_odd = odd_fft[2 * k];
let im_odd = odd_fft[2 * k + 1];
out[2 * k] = even_fft[2 * k] + re * re_odd - im * im_odd;
out[2 * k + 1] = even_fft[2 * k + 1] + re * im_odd + im * re_odd;
out[2 * (k + n / 2)] = even_fft[2 * k] - re * re_odd + im * im_odd;
out[2 * (k + n / 2) + 1] = even_fft[2 * k + 1] - re * im_odd - im * re_odd;
}
out
}
// https://github.com/ggerganov/whisper.cpp/blob/4774d2feb01a772a15de81ffc34b34a1f294f020/whisper.cpp#L2337
fn dft<T: Float>(inp: &[T]) -> Vec<T> {
let zero = T::zero();
let n = inp.len();
let two_pi = T::PI() + T::PI();
let mut out = Vec::with_capacity(2 * n);
let n_t = T::from(n).unwrap();
for k in 0..n {
let k_t = T::from(k).unwrap();
let mut re = zero;
let mut im = zero;
for (j, &inp) in inp.iter().enumerate() {
let j_t = T::from(j).unwrap();
let angle = two_pi * k_t * j_t / n_t;
re += inp * angle.cos();
im -= inp * angle.sin();
}
out.push(re);
out.push(im);
}
out
}
#[allow(clippy::too_many_arguments)]
// https://github.com/ggerganov/whisper.cpp/blob/4774d2feb01a772a15de81ffc34b34a1f294f020/whisper.cpp#L2414
fn log_mel_spectrogram_w<T: Float>(
ith: usize,
hann: &[T],
samples: &[T],
filters: &[T],
fft_size: usize,
fft_step: usize,
speed_up: bool,
n_len: usize,
n_mel: usize,
n_threads: usize,
) -> Vec<T> {
let n_fft = if speed_up {
1 + fft_size / 4
} else {
1 + fft_size / 2
};
let zero = T::zero();
let half = T::from(0.5).unwrap();
let mut fft_in = vec![zero; fft_size];
let mut mel = vec![zero; n_len * n_mel];
for i in (ith..n_len).step_by(n_threads) {
let offset = i * fft_step;
// apply Hanning window
for j in 0..fft_size {
fft_in[j] = if offset + j < samples.len() {
hann[j] * samples[offset + j]
} else {
zero
}
}
// FFT -> mag^2
let mut fft_out: Vec<T> = fft(&fft_in);
for j in 0..fft_size {
fft_out[j] = fft_out[2 * j] * fft_out[2 * j] + fft_out[2 * j + 1] * fft_out[2 * j + 1];
}
for j in 1..fft_size / 2 {
let v = fft_out[fft_size - j];
fft_out[j] += v;
}
if speed_up {
// scale down in the frequency domain results in a speed up in the time domain
for j in 0..n_fft {
fft_out[j] = half * (fft_out[2 * j] + fft_out[2 * j + 1]);
}
}
// mel spectrogram
for j in 0..n_mel {
let mut sum = zero;
for k in 0..n_fft {
sum += fft_out[k] * filters[j * n_fft + k];
}
mel[j * n_len + i] = T::max(sum, T::from(1e-10).unwrap()).log10();
}
}
mel
}
fn log_mel_spectrogram_<T: Float + std::fmt::Display>(
samples: &[T],
filters: &[T],
fft_size: usize,
fft_step: usize,
n_mel: usize,
speed_up: bool,
) -> Vec<T> {
let zero = T::zero();
let two_pi = T::PI() + T::PI();
let half = T::from(0.5).unwrap();
let one = T::from(1.0).unwrap();
let four = T::from(4.0).unwrap();
let fft_size_t = T::from(fft_size).unwrap();
let hann: Vec<T> = (0..fft_size)
.map(|i| half * (one - ((two_pi * T::from(i).unwrap()) / fft_size_t).cos()))
.collect();
let n_len = samples.len() / fft_step;
// pad audio with at least one extra chunk of zeros
let pad = 100 * worker::m::CHUNK_LENGTH / 2;
let n_len = if n_len % pad != 0 {
(n_len / pad + 1) * pad
} else {
n_len
};
let n_len = n_len + pad;
let samples = {
let mut samples_padded = samples.to_vec();
let to_add = n_len * fft_step - samples.len();
samples_padded.extend(std::iter::repeat_n(zero, to_add));
samples_padded
};
// Use a single thread for now.
let mut mel = log_mel_spectrogram_w(
0, &hann, &samples, filters, fft_size, fft_step, speed_up, n_len, n_mel, 1,
);
let mmax = mel
.iter()
.max_by(|&u, &v| u.partial_cmp(v).unwrap_or(std::cmp::Ordering::Greater))
.copied()
.unwrap_or(zero)
- T::from(8).unwrap();
for m in mel.iter_mut() {
let v = T::max(*m, mmax);
*m = v / four + one
}
mel
}
pub fn pcm_to_mel<T: Float + std::fmt::Display>(
cfg: &worker::m::Config,
samples: &[T],
filters: &[T],
) -> anyhow::Result<Vec<T>> {
let mel = log_mel_spectrogram_(
samples,
filters,
worker::m::N_FFT,
worker::m::HOP_LENGTH,
cfg.num_mel_bins,
false,
);
Ok(mel)
}
| candle/candle-wasm-examples/whisper/src/audio.rs/0 | {
"file_path": "candle/candle-wasm-examples/whisper/src/audio.rs",
"repo_id": "candle",
"token_count": 3162
} | 71 |
use yew_agent::PublicWorker;
fn main() {
console_error_panic_hook::set_once();
candle_wasm_example_yolo::Worker::register();
}
| candle/candle-wasm-examples/yolo/src/bin/worker.rs/0 | {
"file_path": "candle/candle-wasm-examples/yolo/src/bin/worker.rs",
"repo_id": "candle",
"token_count": 53
} | 72 |
MONGODB_URL=mongodb://localhost:27017/ | chat-ui/.env.ci/0 | {
"file_path": "chat-ui/.env.ci",
"repo_id": "chat-ui",
"token_count": 16
} | 73 |
{
"useTabs": true,
"trailingComma": "es5",
"printWidth": 100,
"plugins": ["prettier-plugin-svelte", "prettier-plugin-tailwindcss"],
"overrides": [{ "files": "*.svelte", "options": { "parser": "svelte" } }]
}
| chat-ui/.prettierrc/0 | {
"file_path": "chat-ui/.prettierrc",
"repo_id": "chat-ui",
"token_count": 93
} | 74 |
{{- if $.Values.ingress.enabled }}
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
annotations: {{ toYaml .Values.ingress.annotations | nindent 4 }}
labels: {{ include "labels.standard" . | nindent 4 }}
name: {{ include "name" . }}
namespace: {{ .Release.Namespace }}
spec:
{{ if $.Values.ingress.className }}
ingressClassName: {{ .Values.ingress.className }}
{{ end }}
{{- with .Values.ingress.tls }}
tls:
- hosts:
- {{ $.Values.domain | quote }}
{{- with .secretName }}
secretName: {{ . }}
{{- end }}
{{- end }}
rules:
- host: {{ .Values.domain }}
http:
paths:
- backend:
service:
name: {{ include "name" . }}
port:
name: http
path: {{ $.Values.ingress.path | default "/" }}
pathType: Prefix
{{- end }}
| chat-ui/chart/templates/ingress.yaml/0 | {
"file_path": "chat-ui/chart/templates/ingress.yaml",
"repo_id": "chat-ui",
"token_count": 400
} | 75 |
# Google
| Feature | Available |
| --------------------------- | --------- |
| [Tools](../tools) | No |
| [Multimodal](../multimodal) | No |
Chat UI can connect to the google Vertex API endpoints ([List of supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models)).
To enable:
1. [Select](https://console.cloud.google.com/project) or [create](https://cloud.google.com/resource-manager/docs/creating-managing-projects#creating_a_project) a Google Cloud project.
1. [Enable billing for your project](https://cloud.google.com/billing/docs/how-to/modify-project).
1. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).
1. [Set up authentication with a service account](https://cloud.google.com/docs/authentication/getting-started)
so you can access the API from your local workstation.
The service account credentials file can be imported as an environmental variable:
```ini
GOOGLE_APPLICATION_CREDENTIALS = clientid.json
```
Make sure your docker container has access to the file and the variable is correctly set.
Afterwards Google Vertex endpoints can be configured as following:
```ini
MODELS=`[
{
"name": "gemini-1.5-pro",
"displayName": "Vertex Gemini Pro 1.5",
"endpoints" : [{
"type": "vertex",
"project": "abc-xyz",
"location": "europe-west3",
"extraBody": {
"model_version": "gemini-1.5-pro-002",
},
// Optional
"safetyThreshold": "BLOCK_MEDIUM_AND_ABOVE",
"apiEndpoint": "", // alternative api endpoint url,
"tools": [{
"googleSearchRetrieval": {
"disableAttribution": true
}
}]
}]
}
]`
```
## GenAI
Or use the Gemini API API provider [from](https://github.com/google-gemini/generative-ai-js#readme):
Make sure that you have an API key from Google Cloud Platform. To get an API key, follow the instructions [here](https://ai.google.dev/gemini-api/docs/api-key).
You can either specify them directly in your `.env.local` using the `GOOGLE_GENAI_API_KEY` variables, or you can set them directly in the endpoint config.
You can find the list of models available [here](https://ai.google.dev/gemini-api/docs/models/gemini), and experimental models available [here](https://ai.google.dev/gemini-api/docs/models/experimental-models).
```ini
MODELS=`[
{
"name": "gemini-1.5-flash",
"displayName": "Gemini Flash 1.5",
"multimodal": true,
"endpoints": [
{
"type": "genai",
// Optional
"apiKey": "abc...xyz"
"safetyThreshold": "BLOCK_MEDIUM_AND_ABOVE",
}
]
},
{
"name": "gemini-1.5-pro",
"displayName": "Gemini Pro 1.5",
"multimodal": false,
"endpoints": [
{
"type": "genai",
// Optional
"apiKey": "abc...xyz"
}
]
}
]`
```
| chat-ui/docs/source/configuration/models/providers/google.md/0 | {
"file_path": "chat-ui/docs/source/configuration/models/providers/google.md",
"repo_id": "chat-ui",
"token_count": 1138
} | 76 |
# Running Locally
You may start an instance locally for non-production use cases. For production use cases, please see the other installation options.
## Configuration
The default config for Chat UI is stored in the `.env` file. You will need to override some values to get Chat UI to run locally. Start by creating a `.env.local` file in the root of the repository as per the [configuration section](../configuration/overview). The bare minimum config you need to get Chat UI to run locally is the following:
```ini
MONGODB_URL=<the URL to your MongoDB instance>
HF_TOKEN=<your access token> # find your token at hf.co/settings/token
```
## Database
The chat history is stored in a MongoDB instance, and having a DB instance available is needed for Chat UI to work.
You can use a local MongoDB instance. The easiest way is to spin one up using docker with persistence:
```bash
docker run -d -p 27017:27017 -v mongo-chat-ui:/data --name mongo-chat-ui mongo:latest
```
In which case the url of your DB will be `MONGODB_URL=mongodb://localhost:27017`.
Alternatively, you can use a [free MongoDB Atlas](https://www.mongodb.com/pricing) instance for this, Chat UI should fit comfortably within their free tier. After which you can set the `MONGODB_URL` variable in `.env.local` to match your instance.
## Starting the server
```bash
npm ci # install dependencies
npm run build # build the project
npm run preview -- --open # start the server with & open your instance at http://localhost:4173
```
| chat-ui/docs/source/installation/local.md/0 | {
"file_path": "chat-ui/docs/source/installation/local.md",
"repo_id": "chat-ui",
"token_count": 416
} | 77 |
import { config, ready } from "$lib/server/config";
import type { Handle, HandleServerError, ServerInit, HandleFetch } from "@sveltejs/kit";
import { collections } from "$lib/server/database";
import { base } from "$app/paths";
import { authenticateRequest, refreshSessionCookie, requiresUser } from "$lib/server/auth";
import { ERROR_MESSAGES } from "$lib/stores/errors";
import { addWeeks } from "date-fns";
import { checkAndRunMigrations } from "$lib/migrations/migrations";
import { building, dev } from "$app/environment";
import { logger } from "$lib/server/logger";
import { AbortedGenerations } from "$lib/server/abortedGenerations";
import { MetricsServer } from "$lib/server/metrics";
import { initExitHandler } from "$lib/server/exitHandler";
import { refreshAssistantsCounts } from "$lib/jobs/refresh-assistants-counts";
import { refreshConversationStats } from "$lib/jobs/refresh-conversation-stats";
import { adminTokenManager } from "$lib/server/adminToken";
import { isHostLocalhost } from "$lib/server/isURLLocal";
export const init: ServerInit = async () => {
// Wait for config to be fully loaded
await ready;
// TODO: move this code on a started server hook, instead of using a "building" flag
if (!building) {
// Set HF_TOKEN as a process variable for Transformers.JS to see it
process.env.HF_TOKEN ??= config.HF_TOKEN;
logger.info("Starting server...");
initExitHandler();
checkAndRunMigrations();
if (config.ENABLE_ASSISTANTS) {
refreshAssistantsCounts();
}
refreshConversationStats();
// Init metrics server
MetricsServer.getInstance();
// Init AbortedGenerations refresh process
AbortedGenerations.getInstance();
adminTokenManager.displayToken();
if (config.EXPOSE_API) {
logger.warn(
"The EXPOSE_API flag has been deprecated. The API is now required for chat-ui to work."
);
}
}
};
export const handleError: HandleServerError = async ({ error, event, status, message }) => {
// handle 404
if (building) {
throw error;
}
if (event.route.id === null) {
return {
message: `Page ${event.url.pathname} not found`,
};
}
const errorId = crypto.randomUUID();
logger.error({
locals: event.locals,
url: event.request.url,
params: event.params,
request: event.request,
message,
error,
errorId,
status,
stack: error instanceof Error ? error.stack : undefined,
});
return {
message: "An error occurred",
errorId,
};
};
export const handle: Handle = async ({ event, resolve }) => {
await ready.then(() => {
config.checkForUpdates();
});
logger.debug({
locals: event.locals,
url: event.url.pathname,
params: event.params,
request: event.request,
});
function errorResponse(status: number, message: string) {
const sendJson =
event.request.headers.get("accept")?.includes("application/json") ||
event.request.headers.get("content-type")?.includes("application/json");
return new Response(sendJson ? JSON.stringify({ error: message }) : message, {
status,
headers: {
"content-type": sendJson ? "application/json" : "text/plain",
},
});
}
if (event.url.pathname.startsWith(`${base}/admin/`) || event.url.pathname === `${base}/admin`) {
const ADMIN_SECRET = config.ADMIN_API_SECRET || config.PARQUET_EXPORT_SECRET;
if (!ADMIN_SECRET) {
return errorResponse(500, "Admin API is not configured");
}
if (event.request.headers.get("Authorization") !== `Bearer ${ADMIN_SECRET}`) {
return errorResponse(401, "Unauthorized");
}
}
const auth = await authenticateRequest(
{ type: "svelte", value: event.request.headers },
{ type: "svelte", value: event.cookies }
);
event.locals.user = auth.user || undefined;
event.locals.sessionId = auth.sessionId;
event.locals.isAdmin =
event.locals.user?.isAdmin || adminTokenManager.isAdmin(event.locals.sessionId);
// CSRF protection
const requestContentType = event.request.headers.get("content-type")?.split(";")[0] ?? "";
/** https://developer.mozilla.org/en-US/docs/Web/HTML/Element/form#attr-enctype */
const nativeFormContentTypes = [
"multipart/form-data",
"application/x-www-form-urlencoded",
"text/plain",
];
if (event.request.method === "POST") {
if (nativeFormContentTypes.includes(requestContentType)) {
const origin = event.request.headers.get("origin");
if (!origin) {
return errorResponse(403, "Non-JSON form requests need to have an origin");
}
const validOrigins = [
new URL(event.request.url).host,
...(config.PUBLIC_ORIGIN ? [new URL(config.PUBLIC_ORIGIN).host] : []),
];
if (!validOrigins.includes(new URL(origin).host)) {
return errorResponse(403, "Invalid referer for POST request");
}
}
}
if (
event.request.method === "POST" ||
event.url.pathname.startsWith(`${base}/login`) ||
event.url.pathname.startsWith(`${base}/login/callback`)
) {
// if the request is a POST request or login-related we refresh the cookie
refreshSessionCookie(event.cookies, auth.secretSessionId);
await collections.sessions.updateOne(
{ sessionId: auth.sessionId },
{ $set: { updatedAt: new Date(), expiresAt: addWeeks(new Date(), 2) } }
);
}
if (
!event.url.pathname.startsWith(`${base}/login`) &&
!event.url.pathname.startsWith(`${base}/admin`) &&
!event.url.pathname.startsWith(`${base}/settings`) &&
!["GET", "OPTIONS", "HEAD"].includes(event.request.method)
) {
if (
!event.locals.user &&
requiresUser &&
!((config.MESSAGES_BEFORE_LOGIN ? parseInt(config.MESSAGES_BEFORE_LOGIN) : 0) > 0)
) {
return errorResponse(401, ERROR_MESSAGES.authOnly);
}
// if login is not required and the call is not from /settings and we display the ethics modal with PUBLIC_APP_DISCLAIMER
// we check if the user has accepted the ethics modal first.
// If login is required, `ethicsModalAcceptedAt` is already true at this point, so do not pass this condition. This saves a DB call.
if (
!requiresUser &&
!event.url.pathname.startsWith(`${base}/settings`) &&
config.PUBLIC_APP_DISCLAIMER === "1"
) {
const hasAcceptedEthicsModal = await collections.settings.countDocuments({
sessionId: event.locals.sessionId,
ethicsModalAcceptedAt: { $exists: true },
});
if (!hasAcceptedEthicsModal) {
return errorResponse(405, "You need to accept the welcome modal first");
}
}
}
let replaced = false;
const response = await resolve(event, {
transformPageChunk: (chunk) => {
// For some reason, Sveltekit doesn't let us load env variables from .env in the app.html template
if (replaced || !chunk.html.includes("%gaId%")) {
return chunk.html;
}
replaced = true;
return chunk.html.replace("%gaId%", config.PUBLIC_GOOGLE_ANALYTICS_ID);
},
filterSerializedResponseHeaders: (header) => {
return header.includes("content-type");
},
});
// Add CSP header to disallow framing if ALLOW_IFRAME is not "true"
if (config.ALLOW_IFRAME !== "true") {
response.headers.append("Content-Security-Policy", "frame-ancestors 'none';");
}
if (
event.url.pathname.startsWith(`${base}/login/callback`) ||
event.url.pathname.startsWith(`${base}/login`)
) {
response.headers.append("Cache-Control", "no-store");
}
if (event.url.pathname.startsWith(`${base}/api/`)) {
// get origin from the request
const requestOrigin = event.request.headers.get("origin");
// get origin from the config if its defined
let allowedOrigin = config.PUBLIC_ORIGIN ? new URL(config.PUBLIC_ORIGIN).origin : undefined;
if (
dev || // if we're in dev mode
!requestOrigin || // or the origin is null (SSR)
isHostLocalhost(new URL(requestOrigin).hostname) // or the origin is localhost
) {
allowedOrigin = "*"; // allow all origins
} else if (allowedOrigin === requestOrigin) {
allowedOrigin = requestOrigin; // echo back the caller
}
if (allowedOrigin) {
response.headers.set("Access-Control-Allow-Origin", allowedOrigin);
response.headers.set(
"Access-Control-Allow-Methods",
"GET, POST, PUT, PATCH, DELETE, OPTIONS"
);
response.headers.set("Access-Control-Allow-Headers", "Content-Type, Authorization");
}
}
return response;
};
export const handleFetch: HandleFetch = async ({ event, request, fetch }) => {
if (isHostLocalhost(new URL(request.url).hostname)) {
const cookieHeader = event.request.headers.get("cookie");
if (cookieHeader) {
const headers = new Headers(request.headers);
headers.set("cookie", cookieHeader);
return fetch(new Request(request, { headers }));
}
}
return fetch(request);
};
| chat-ui/src/hooks.server.ts/0 | {
"file_path": "chat-ui/src/hooks.server.ts",
"repo_id": "chat-ui",
"token_count": 3045
} | 78 |
<script lang="ts">
import { base } from "$app/paths";
import { page } from "$app/state";
import LogoHuggingFaceBorderless from "$lib/components/icons/LogoHuggingFaceBorderless.svelte";
import Modal from "$lib/components/Modal.svelte";
import { useSettingsStore } from "$lib/stores/settings";
import { cookiesAreEnabled } from "$lib/utils/cookiesAreEnabled";
import Logo from "./icons/Logo.svelte";
import { usePublicConfig } from "$lib/utils/PublicConfig.svelte";
const publicConfig = usePublicConfig();
const settings = useSettingsStore();
</script>
<Modal on:close width="!max-w-[400px] !m-4">
<div
class="from-primary-500/40 via-primary-500/10 to-primary-500/0 flex w-full flex-col items-center gap-6 bg-gradient-to-b px-5 pb-8 pt-9 text-center"
>
<h2 class="flex items-center text-2xl font-semibold text-gray-800">
<Logo classNames="mr-1" />
{publicConfig.PUBLIC_APP_NAME}
</h2>
<p class="text-balance text-lg font-semibold leading-snug text-gray-800">
{publicConfig.PUBLIC_APP_DESCRIPTION}
</p>
<p class="text-balance rounded-xl border bg-white/80 p-2 text-base text-gray-800">
{publicConfig.PUBLIC_APP_GUEST_MESSAGE}
</p>
<div class="flex w-full flex-col items-center gap-2">
{#if page.data.loginRequired}
<a
href="{base}/login"
class="flex w-full flex-wrap items-center justify-center whitespace-nowrap rounded-full bg-black px-5 py-2 text-center text-lg font-semibold text-gray-100 transition-colors hover:bg-gray-900"
>
Sign in
{#if publicConfig.isHuggingChat}
<span class="flex items-center">
with <LogoHuggingFaceBorderless classNames="text-xl mr-1 ml-1.5" /> Hugging Face
</span>
{/if}
</a>
{:else}
<button
class="flex w-full items-center justify-center whitespace-nowrap rounded-full border-2 border-black bg-black px-5 py-2 text-lg font-semibold text-gray-100 transition-colors hover:bg-gray-900"
onclick={(e) => {
if (!cookiesAreEnabled()) {
e.preventDefault();
window.open(window.location.href, "_blank");
}
$settings.ethicsModalAccepted = true;
}}
>
Start chatting
</button>
{/if}
</div>
</div>
</Modal>
| chat-ui/src/lib/components/LoginModal.svelte/0 | {
"file_path": "chat-ui/src/lib/components/LoginModal.svelte",
"repo_id": "chat-ui",
"token_count": 914
} | 79 |
<script lang="ts">
import Modal from "./Modal.svelte";
import CarbonClose from "~icons/carbon/close";
import CarbonBlockchain from "~icons/carbon/blockchain";
interface Props {
preprompt: string;
}
let { preprompt }: Props = $props();
let isOpen = $state(false);
</script>
<button
type="button"
class="mx-auto flex items-center gap-1.5 rounded-full border border-gray-100 bg-gray-50 px-3 py-1 text-xs text-gray-500 hover:bg-gray-100 dark:border-gray-800 dark:bg-gray-800 dark:text-gray-400 dark:hover:bg-gray-700"
onclick={() => (isOpen = !isOpen)}
onkeydown={(e) => e.key === "Enter" && (isOpen = !isOpen)}
>
<CarbonBlockchain class="text-xxs" /> Using Custom System Prompt
</button>
{#if isOpen}
<Modal on:close={() => (isOpen = false)} width="w-full !max-w-xl">
<div class="flex w-full flex-col gap-5 p-6">
<div class="flex items-start justify-between text-xl font-semibold text-gray-800">
<h2>System Prompt</h2>
<button type="button" class="group" onclick={() => (isOpen = false)}>
<CarbonClose class="mt-auto text-gray-900 group-hover:text-gray-500" />
</button>
</div>
<textarea
disabled
value={preprompt}
class="min-h-[420px] w-full resize-none rounded-lg border bg-gray-50 p-2.5 text-gray-600 max-sm:text-sm"
></textarea>
</div>
</Modal>
{/if}
| chat-ui/src/lib/components/SystemPromptModal.svelte/0 | {
"file_path": "chat-ui/src/lib/components/SystemPromptModal.svelte",
"repo_id": "chat-ui",
"token_count": 535
} | 80 |
<script lang="ts">
import type { WebSearchSource } from "$lib/types/WebSearch";
import { processTokens, processTokensSync, type Token } from "$lib/utils/marked";
// import MarkdownWorker from "$lib/workers/markdownWorker?worker";
import CodeBlock from "../CodeBlock.svelte";
import type { IncomingMessage, OutgoingMessage } from "$lib/workers/markdownWorker";
import { browser } from "$app/environment";
import DOMPurify from "isomorphic-dompurify";
import { onMount } from "svelte";
import { updateDebouncer } from "$lib/utils/updates";
interface Props {
content: string;
sources?: WebSearchSource[];
}
let worker: Worker | null = null;
let { content, sources = [] }: Props = $props();
let tokens: Token[] = $state(processTokensSync(content, sources));
async function processContent(content: string, sources: WebSearchSource[]): Promise<Token[]> {
if (worker) {
return new Promise((resolve) => {
if (!worker) {
throw new Error("Worker not initialized");
}
worker.onmessage = (event: MessageEvent<OutgoingMessage>) => {
if (event.data.type !== "processed") {
throw new Error("Invalid message type");
}
resolve(event.data.tokens);
};
worker.postMessage(
JSON.parse(JSON.stringify({ content, sources, type: "process" })) as IncomingMessage
);
});
} else {
return processTokens(content, sources);
}
}
$effect(() => {
if (!browser) {
tokens = processTokensSync(content, sources);
} else {
(async () => {
updateDebouncer.startRender();
tokens = await processContent(content, sources).then(
async (tokens) =>
await Promise.all(
tokens.map(async (token) => {
if (token.type === "text") {
token.html = DOMPurify.sanitize(await token.html);
}
return token;
})
)
);
updateDebouncer.endRender();
})();
}
});
onMount(() => {
// todo: fix worker, seems to be transmitting a lot of data
// worker = browser && window.Worker ? new MarkdownWorker() : null;
DOMPurify.addHook("afterSanitizeAttributes", (node) => {
if (node.tagName === "A") {
node.setAttribute("target", "_blank");
node.setAttribute("rel", "noreferrer");
}
});
});
</script>
{#each tokens as token}
{#if token.type === "text"}
<!-- eslint-disable-next-line svelte/no-at-html-tags -->
{@html token.html}
{:else if token.type === "code"}
<CodeBlock code={token.code} rawCode={token.rawCode} />
{/if}
{/each}
| chat-ui/src/lib/components/chat/MarkdownRenderer.svelte/0 | {
"file_path": "chat-ui/src/lib/components/chat/MarkdownRenderer.svelte",
"repo_id": "chat-ui",
"token_count": 964
} | 81 |
<script lang="ts">
interface Props {
classNames?: string;
}
let { classNames = "" }: Props = $props();
</script>
<svg
class={classNames}
xmlns="http://www.w3.org/2000/svg"
aria-hidden="true"
focusable="false"
role="img"
width="1em"
height="1em"
fill="currentColor"
viewBox="0 0 32 32"
><path
fill-rule="evenodd"
clip-rule="evenodd"
d="M5.98 6.01h20.04v16.1H5.98V6.02Zm-2.1 0c0-1.16.94-2.1 2.1-2.1h20.04c1.16 0 2.1.94 2.1 2.1v16.1a2.1 2.1 0 0 1-2.1 2.11H5.98a2.1 2.1 0 0 1-2.1-2.1V6.02Zm5.7 1.65a2.1 2.1 0 0 0-2.1 2.1v2.61a1.05 1.05 0 0 0 2.1 0v-2.6h2.96a1.05 1.05 0 1 0 0-2.11H9.58ZM24.41 18.4a2.1 2.1 0 0 1-2.1 2.1h-2.95a1.05 1.05 0 1 1 0-2.1h2.95v-2.61a1.05 1.05 0 0 1 2.1 0v2.6ZM10.1 25.9a1.05 1.05 0 1 0 0 2.1H22.3a1.05 1.05 0 1 0 0-2.1H10.1Z"
/>
</svg>
| chat-ui/src/lib/components/icons/IconScreenshot.svelte/0 | {
"file_path": "chat-ui/src/lib/components/icons/IconScreenshot.svelte",
"repo_id": "chat-ui",
"token_count": 471
} | 82 |
import { ObjectId, type WithId } from "mongodb";
import { collections } from "$lib/server/database";
import type { Migration } from ".";
import type { Conversation } from "$lib/types/Conversation";
import type { MessageFile } from "$lib/types/Message";
const updateMessageFiles: Migration = {
_id: new ObjectId("5f9f5f5f5f5f5f5f5f5f5f5f"),
name: "Convert message files to the new schema",
up: async () => {
const allConversations = collections.conversations.find({}, { projection: { messages: 1 } });
let conversation: WithId<Pick<Conversation, "messages">> | null = null;
while ((conversation = await allConversations.tryNext())) {
const messages = conversation.messages.map((message) => {
const files = (message.files as string[] | undefined)?.map<MessageFile>((file) => {
// File is already in the new format
if (typeof file !== "string") return file;
// File was a hash pointing to a file in the bucket
if (file.length === 64) {
return {
type: "hash",
name: "unknown.jpg",
value: file,
mime: "image/jpeg",
};
}
// File was a base64 string
else {
return {
type: "base64",
name: "unknown.jpg",
value: file,
mime: "image/jpeg",
};
}
});
return {
...message,
files,
};
});
// Set the new messages array
await collections.conversations.updateOne({ _id: conversation._id }, { $set: { messages } });
}
return true;
},
runEveryTime: false,
};
export default updateMessageFiles;
| chat-ui/src/lib/migrations/routines/05-update-message-files.ts/0 | {
"file_path": "chat-ui/src/lib/migrations/routines/05-update-message-files.ts",
"repo_id": "chat-ui",
"token_count": 618
} | 83 |
import { Elysia, t } from "elysia";
import { authPlugin } from "$api/authPlugin";
import { ReviewStatus } from "$lib/types/Review";
import { toolFromConfigs } from "$lib/server/tools";
import { collections } from "$lib/server/database";
import { ObjectId, type Filter } from "mongodb";
import type { CommunityToolDB, ToolFront, ToolInputFile } from "$lib/types/Tool";
import { MetricsServer } from "$lib/server/metrics";
import { authCondition } from "$lib/server/auth";
import { SortKey } from "$lib/types/Assistant";
import type { User } from "$lib/types/User";
import { generateQueryTokens, generateSearchTokens } from "$lib/utils/searchTokens";
import { config } from "$lib/server/config";
const NUM_PER_PAGE = 16;
export const toolGroup = new Elysia().use(authPlugin).group("/tools", (app) => {
return app
.get("/active", async ({ locals }) => {
const settings = await collections.settings.findOne(authCondition(locals));
if (!settings) {
return [];
}
const toolUseDuration = (await MetricsServer.getMetrics().tool.toolUseDuration.get()).values;
const activeCommunityToolIds = settings.tools ?? [];
const communityTools = await collections.tools
.find({ _id: { $in: activeCommunityToolIds.map((el) => new ObjectId(el)) } })
.toArray()
.then((tools) =>
tools.map((tool) => ({
...tool,
isHidden: false,
isOnByDefault: true,
isLocked: true,
}))
);
const fullTools = [...communityTools, ...toolFromConfigs];
return fullTools
.filter((tool) => !tool.isHidden)
.map(
(tool) =>
({
_id: tool._id.toString(),
type: tool.type,
displayName: tool.displayName,
name: tool.name,
description: tool.description,
mimeTypes: (tool.inputs ?? [])
.filter((input): input is ToolInputFile => input.type === "file")
.map((input) => (input as ToolInputFile).mimeTypes)
.flat(),
isOnByDefault: tool.isOnByDefault ?? true,
isLocked: tool.isLocked ?? true,
timeToUseMS:
toolUseDuration.find(
(el) => el.labels.tool === tool._id.toString() && el.labels.quantile === 0.9
)?.value ?? 15_000,
color: tool.color,
icon: tool.icon,
}) satisfies ToolFront
);
})
.get(
"/search",
async ({ query, locals, error }) => {
if (config.COMMUNITY_TOOLS !== "true") {
error(403, "Community tools are not enabled");
}
const username = query.user;
const search = query.q?.trim() ?? null;
const pageIndex = query.p ?? 0;
const sort = query.sort ?? SortKey.TRENDING;
const createdByCurrentUser = locals.user?.username && locals.user.username === username;
const activeOnly = query.active ?? false;
const showUnfeatured = query.showUnfeatured ?? false;
let user: Pick<User, "_id"> | null = null;
if (username) {
user = await collections.users.findOne<Pick<User, "_id">>(
{ username },
{ projection: { _id: 1 } }
);
if (!user) {
error(404, `User "${username}" doesn't exist`);
}
}
const settings = await collections.settings.findOne(authCondition(locals));
if (!settings && activeOnly) {
error(404, "No user settings found");
}
const queryTokens = !!search && generateQueryTokens(search);
const filter: Filter<CommunityToolDB> = {
...(!createdByCurrentUser &&
!activeOnly &&
!(locals.isAdmin && showUnfeatured) && { review: ReviewStatus.APPROVED }),
...(user && { createdById: user._id }),
...(queryTokens && { searchTokens: { $all: queryTokens } }),
...(activeOnly && {
_id: {
$in: (settings?.tools ?? []).map((key) => {
return new ObjectId(key);
}),
},
}),
};
const communityTools = await collections.tools
.find(filter)
.skip(NUM_PER_PAGE * pageIndex)
.sort({
...(sort === SortKey.TRENDING && { last24HoursUseCount: -1 }),
useCount: -1,
})
.limit(NUM_PER_PAGE)
.toArray();
const configTools = toolFromConfigs
.filter((tool) => !tool?.isHidden)
.filter((tool) => {
if (queryTokens) {
return generateSearchTokens(tool.displayName).some((token) =>
queryTokens.some((queryToken) => queryToken.test(token))
);
}
return true;
});
const tools = [...(pageIndex == 0 && !username ? configTools : []), ...communityTools];
const numTotalItems =
(await collections.tools.countDocuments(filter)) + toolFromConfigs.length;
return {
tools,
numTotalItems,
numItemsPerPage: NUM_PER_PAGE,
query: search,
sort,
showUnfeatured,
};
},
{
query: t.Object({
user: t.Optional(t.String()),
q: t.Optional(t.String()),
sort: t.Optional(t.Enum(SortKey)),
p: t.Optional(t.Numeric()),
showUnfeatured: t.Optional(t.Boolean()),
active: t.Optional(t.Boolean()),
}),
}
)
.get("/count", () => {
// return community tool count
return collections.tools.countDocuments({ type: "community", review: ReviewStatus.APPROVED });
})
.group("/:id", (app) => {
return app
.derive(async ({ params, error, locals }) => {
const tool = await collections.tools.findOne({ _id: new ObjectId(params.id) });
if (!tool) {
const tool = toolFromConfigs.find((el) => el._id.toString() === params.id);
if (!tool) {
throw error(404, "Tool not found");
} else {
return {
tool: {
...tool,
_id: tool._id.toString(),
call: undefined,
createdById: null,
createdByName: null,
createdByMe: false,
reported: false,
review: ReviewStatus.APPROVED,
},
};
}
} else {
const reported = await collections.reports.findOne({
contentId: tool._id,
object: "tool",
});
return {
tool: {
...tool,
_id: tool._id.toString(),
call: undefined,
createdById: tool.createdById.toString(),
createdByMe:
tool.createdById.toString() === (locals.user?._id ?? locals.sessionId).toString(),
reported: !!reported,
},
};
}
})
.get("", ({ tool }) => {
return tool;
})
.post("/", () => {
// todo: post new tool
throw new Error("Not implemented");
})
.group("/:toolId", (app) => {
return app
.get("/", () => {
// todo: get tool
throw new Error("Not implemented");
})
.patch("/", () => {
// todo: patch tool
throw new Error("Not implemented");
})
.delete("/", () => {
// todo: delete tool
throw new Error("Not implemented");
})
.post("/report", () => {
// todo: report tool
throw new Error("Not implemented");
})
.patch("/review", () => {
// todo: review tool
throw new Error("Not implemented");
});
});
});
});
| chat-ui/src/lib/server/api/routes/groups/tools.ts/0 | {
"file_path": "chat-ui/src/lib/server/api/routes/groups/tools.ts",
"repo_id": "chat-ui",
"token_count": 3068
} | 84 |
import { z } from "zod";
import type { Endpoint } from "../endpoints";
import type { TextGenerationStreamOutput } from "@huggingface/inference";
import { config } from "$lib/server/config";
import { logger } from "$lib/server/logger";
export const endpointCloudflareParametersSchema = z.object({
weight: z.number().int().positive().default(1),
model: z.any(),
type: z.literal("cloudflare"),
accountId: z.string().default(config.CLOUDFLARE_ACCOUNT_ID),
apiToken: z.string().default(config.CLOUDFLARE_API_TOKEN),
});
export async function endpointCloudflare(
input: z.input<typeof endpointCloudflareParametersSchema>
): Promise<Endpoint> {
const { accountId, apiToken, model } = endpointCloudflareParametersSchema.parse(input);
if (!model.id.startsWith("@")) {
model.id = "@hf/" + model.id;
}
const apiURL = `https://api.cloudflare.com/client/v4/accounts/${accountId}/ai/run/${model.id}`;
return async ({ messages, preprompt, generateSettings }) => {
let messagesFormatted = messages.map((message) => ({
role: message.from,
content: message.content,
}));
if (messagesFormatted?.[0]?.role !== "system") {
messagesFormatted = [{ role: "system", content: preprompt ?? "" }, ...messagesFormatted];
}
const parameters = { ...model.parameters, ...generateSettings };
const payload = JSON.stringify({
messages: messagesFormatted,
stream: true,
max_tokens: parameters?.max_new_tokens,
temperature: parameters?.temperature,
top_p: parameters?.top_p,
top_k: parameters?.top_k,
repetition_penalty: parameters?.repetition_penalty,
});
const res = await fetch(apiURL, {
method: "POST",
headers: {
Authorization: `Bearer ${apiToken}`,
"Content-Type": "application/json",
},
body: payload,
});
if (!res.ok) {
throw new Error(`Failed to generate text: ${await res.text()}`);
}
const encoder = new TextDecoderStream();
const reader = res.body?.pipeThrough(encoder).getReader();
return (async function* () {
let stop = false;
let generatedText = "";
let tokenId = 0;
let accumulatedData = ""; // Buffer to accumulate data chunks
while (!stop) {
const out = await reader?.read();
// If it's done, we cancel
if (out?.done) {
reader?.cancel();
return;
}
if (!out?.value) {
return;
}
// Accumulate the data chunk
accumulatedData += out.value;
// Process each complete JSON object in the accumulated data
while (accumulatedData.includes("\n")) {
// Assuming each JSON object ends with a newline
const endIndex = accumulatedData.indexOf("\n");
let jsonString = accumulatedData.substring(0, endIndex).trim();
// Remove the processed part from the buffer
accumulatedData = accumulatedData.substring(endIndex + 1);
if (jsonString.startsWith("data: ")) {
jsonString = jsonString.slice(6);
let data = null;
if (jsonString === "[DONE]") {
stop = true;
yield {
token: {
id: tokenId++,
text: "",
logprob: 0,
special: true,
},
generated_text: generatedText,
details: null,
} satisfies TextGenerationStreamOutput;
reader?.cancel();
continue;
}
try {
data = JSON.parse(jsonString);
} catch (e) {
logger.error(e, "Failed to parse JSON");
logger.error(jsonString, "Problematic JSON string:");
continue; // Skip this iteration and try the next chunk
}
// Handle the parsed data
if (data.response) {
generatedText += data.response ?? "";
const output: TextGenerationStreamOutput = {
token: {
id: tokenId++,
text: data.response ?? "",
logprob: 0,
special: false,
},
generated_text: null,
details: null,
};
yield output;
}
}
}
}
})();
};
}
export default endpointCloudflare;
| chat-ui/src/lib/server/endpoints/cloudflare/endpointCloudflare.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/cloudflare/endpointCloudflare.ts",
"repo_id": "chat-ui",
"token_count": 1620
} | 85 |
import { config } from "$lib/server/config";
import { buildPrompt } from "$lib/buildPrompt";
import { textGenerationStream } from "@huggingface/inference";
import type { Endpoint, EndpointMessage } from "../endpoints";
import { z } from "zod";
import {
createImageProcessorOptionsValidator,
makeImageProcessor,
type ImageProcessor,
} from "../images";
export const endpointTgiParametersSchema = z.object({
weight: z.number().int().positive().default(1),
model: z.any(),
type: z.literal("tgi"),
url: z.string().url(),
accessToken: z.string().default(config.HF_TOKEN ?? config.HF_ACCESS_TOKEN),
authorization: z.string().optional(),
multimodal: z
.object({
// Assumes IDEFICS
image: createImageProcessorOptionsValidator({
supportedMimeTypes: ["image/jpeg", "image/webp"],
preferredMimeType: "image/webp",
maxSizeInMB: 5,
maxWidth: 378,
maxHeight: 980,
}),
})
.default({}),
});
export function endpointTgi(input: z.input<typeof endpointTgiParametersSchema>): Endpoint {
const { url, accessToken, model, authorization, multimodal } =
endpointTgiParametersSchema.parse(input);
const imageProcessor = makeImageProcessor(multimodal.image);
return async ({
messages,
preprompt,
continueMessage,
generateSettings,
tools,
toolResults,
isMultimodal,
conversationId,
}) => {
const messagesWithResizedFiles = await Promise.all(
messages.map((message) => prepareMessage(Boolean(isMultimodal), message, imageProcessor))
);
const prompt = await buildPrompt({
messages: messagesWithResizedFiles,
preprompt,
model,
continueMessage,
tools,
toolResults,
});
return textGenerationStream(
{
parameters: { ...model.parameters, ...generateSettings, return_full_text: false },
model: url,
inputs: prompt,
accessToken,
},
{
fetch: async (endpointUrl, info) => {
if (info && authorization && !accessToken) {
// Set authorization header if it is defined and HF_TOKEN is empty
info.headers = {
...info.headers,
Authorization: authorization,
"ChatUI-Conversation-ID": conversationId?.toString() ?? "",
"X-Use-Cache": "false",
};
}
return fetch(endpointUrl, info);
},
}
);
};
}
async function prepareMessage(
isMultimodal: boolean,
message: EndpointMessage,
imageProcessor: ImageProcessor
): Promise<EndpointMessage> {
if (!isMultimodal) return message;
const files = await Promise.all(message.files?.map(imageProcessor) ?? []);
const markdowns = files.map(
(file) => `![](data:${file.mime};base64,${file.image.toString("base64")})`
);
const content = message.content + "\n" + markdowns.join("\n ");
return { ...message, content };
}
| chat-ui/src/lib/server/endpoints/tgi/endpointTgi.ts/0 | {
"file_path": "chat-ui/src/lib/server/endpoints/tgi/endpointTgi.ts",
"repo_id": "chat-ui",
"token_count": 1022
} | 86 |
import { Address6, Address4 } from "ip-address";
import dns from "node:dns";
import { isIP } from "node:net";
const dnsLookup = (hostname: string): Promise<{ address: string; family: number }> => {
return new Promise((resolve, reject) => {
dns.lookup(hostname, (err, address, family) => {
if (err) return reject(err);
resolve({ address, family });
});
});
};
export async function isURLLocal(URL: URL): Promise<boolean> {
const { address, family } = await dnsLookup(URL.hostname);
if (family === 4) {
const addr = new Address4(address);
const localSubnet = new Address4("127.0.0.0/8");
return addr.isInSubnet(localSubnet);
}
if (family === 6) {
const addr = new Address6(address);
return addr.isLoopback() || addr.isInSubnet(new Address6("::1/128")) || addr.isLinkLocal();
}
throw Error("Unknown IP family");
}
export function isURLStringLocal(url: string) {
try {
const urlObj = new URL(url);
return isURLLocal(urlObj);
} catch (e) {
// assume local if URL parsing fails
return true;
}
}
export function isHostLocalhost(host: string): boolean {
if (host === "localhost") return true;
if (host === "::1" || host === "[::1]") return true;
if (host.startsWith("127.") && isIP(host)) return true;
if (host.endsWith(".localhost")) return true;
return false;
}
| chat-ui/src/lib/server/isURLLocal.ts/0 | {
"file_path": "chat-ui/src/lib/server/isURLLocal.ts",
"repo_id": "chat-ui",
"token_count": 466
} | 87 |
import { MessageUpdateType } from "$lib/types/MessageUpdate";
import {
ToolColor,
ToolIcon,
ToolOutputComponents,
type BackendCall,
type BaseTool,
type ConfigTool,
type ToolInput,
} from "$lib/types/Tool";
import type { TextGenerationContext } from "../textGeneration/types";
import { z } from "zod";
import JSON5 from "json5";
import { config } from "$lib/server/config";
import jp from "jsonpath";
import calculator from "./calculator";
import directlyAnswer from "./directlyAnswer";
import fetchUrl from "./web/url";
import websearch from "./web/search";
import { callSpace, getIpToken } from "./utils";
import { uploadFile } from "../files/uploadFile";
import type { MessageFile } from "$lib/types/Message";
import { sha256 } from "$lib/utils/sha256";
import { ObjectId } from "mongodb";
import { isValidOutputComponent, ToolOutputPaths } from "./outputs";
import { downloadFile } from "../files/downloadFile";
import { fileTypeFromBlob } from "file-type";
export type BackendToolContext = Pick<
TextGenerationContext,
"conv" | "messages" | "assistant" | "ip" | "username"
> & { preprompt?: string };
const IOType = z.union([z.literal("str"), z.literal("int"), z.literal("float"), z.literal("bool")]);
const toolInputBaseSchema = z.union([
z.object({
name: z.string().min(1).max(80),
description: z.string().max(200).optional(),
paramType: z.literal("required"),
}),
z.object({
name: z.string().min(1).max(80),
description: z.string().max(200).optional(),
paramType: z.literal("optional"),
default: z
.union([z.string().max(300), z.number(), z.boolean(), z.undefined()])
.transform((val) => (val === undefined ? "" : val)),
}),
z.object({
name: z.string().min(1).max(80),
paramType: z.literal("fixed"),
value: z
.union([z.string().max(300), z.number(), z.boolean(), z.undefined()])
.transform((val) => (val === undefined ? "" : val)),
}),
]);
const toolInputSchema = toolInputBaseSchema.and(
z.object({ type: IOType }).or(
z.object({
type: z.literal("file"),
mimeTypes: z.string().min(1),
})
)
);
export const editableToolSchema = z
.object({
name: z
.string()
.regex(/^[a-zA-Z_][a-zA-Z0-9_]*$/) // only allow letters, numbers, and underscores, and start with a letter or underscore
.min(1)
.max(40),
// only allow huggingface spaces either through namespace or direct URLs
baseUrl: z.union([
z.string().regex(/^[^/]+\/[^/]+$/),
z
.string()
.regex(/^https:\/\/huggingface\.co\/spaces\/[a-zA-Z0-9-]+\/[a-zA-Z0-9-]+$/)
.transform((url) => url.split("/").slice(-2).join("/")),
]),
endpoint: z.string().min(1).max(100),
inputs: z.array(toolInputSchema),
outputComponent: z.string().min(1).max(100),
showOutput: z.boolean(),
displayName: z.string().min(1).max(40),
color: ToolColor,
icon: ToolIcon,
description: z.string().min(1).max(100),
})
.transform((tool) => ({
...tool,
outputComponentIdx: parseInt(tool.outputComponent.split(";")[0]),
outputComponent: ToolOutputComponents.parse(tool.outputComponent.split(";")[1]),
}));
export const configTools = z
.array(
z
.object({
name: z.string(),
description: z.string(),
endpoint: z.union([z.string(), z.null()]),
inputs: z.array(toolInputSchema),
outputComponent: ToolOutputComponents.or(z.null()),
outputComponentIdx: z.number().int().default(0),
showOutput: z.boolean(),
_id: z
.string()
.length(24)
.regex(/^[0-9a-fA-F]{24}$/)
.transform((val) => new ObjectId(val)),
baseUrl: z.string().optional(),
displayName: z.string(),
color: ToolColor,
icon: ToolIcon,
isOnByDefault: z.optional(z.literal(true)),
isLocked: z.optional(z.literal(true)),
isHidden: z.optional(z.literal(true)),
})
.transform((val) => ({
type: "config" as const,
...val,
call: getCallMethod(val),
}))
)
// add the extra hardcoded tools
.transform((val) => [...val, calculator, directlyAnswer, fetchUrl, websearch]);
export function getCallMethod(tool: Omit<BaseTool, "call">): BackendCall {
return async function* (params, ctx, uuid) {
if (
tool.endpoint === null ||
!tool.baseUrl ||
!tool.outputComponent ||
tool.outputComponentIdx === null
) {
throw new Error(`Tool function ${tool.name} has no endpoint`);
}
const ipToken = await getIpToken(ctx.ip, ctx.username);
function coerceInput(value: unknown, type: ToolInput["type"]) {
const valueStr = String(value);
switch (type) {
case "str":
return valueStr;
case "int":
return parseInt(valueStr);
case "float":
return parseFloat(valueStr);
case "bool":
return valueStr === "true";
default:
throw new Error(`Unsupported type ${type}`);
}
}
const inputs = tool.inputs.map(async (input) => {
if (input.type === "file" && input.paramType !== "required") {
throw new Error("File inputs are always required and cannot be optional or fixed");
}
if (input.paramType === "fixed") {
return coerceInput(input.value, input.type);
} else if (input.paramType === "optional") {
return coerceInput(params[input.name] ?? input.default, input.type);
} else if (input.paramType === "required") {
if (params[input.name] === undefined) {
throw new Error(`Missing required input ${input.name}`);
}
if (input.type === "file") {
// todo: parse file here !
// structure is {input|output}-{msgIdx}-{fileIdx}-{filename}
const filename = params[input.name];
if (!filename || typeof filename !== "string") {
throw new Error(`Filename is not a string`);
}
const messages = ctx.messages;
const msgIdx = parseInt(filename.split("_")[1]);
const fileIdx = parseInt(filename.split("_")[2]);
if (Number.isNaN(msgIdx) || Number.isNaN(fileIdx)) {
throw Error(`Message index or file index is missing`);
}
if (msgIdx >= messages.length) {
throw Error(`Message index ${msgIdx} is out of bounds`);
}
const file = messages[msgIdx].files?.[fileIdx];
if (!file) {
throw Error(`File index ${fileIdx} is out of bounds`);
}
const blob = await downloadFile(file.value, ctx.conv._id)
.then((file) => fetch(`data:${file.mime};base64,${file.value}`))
.then((res) => res.blob())
.catch((err) => {
throw Error("Failed to download file", { cause: err });
});
return blob;
} else {
return coerceInput(params[input.name], input.type);
}
}
});
const outputs = yield* callSpace(
tool.baseUrl,
tool.endpoint,
await Promise.all(inputs),
ipToken,
uuid
);
if (!isValidOutputComponent(tool.outputComponent)) {
throw new Error(`Tool output component is not defined`);
}
const { type, path } = ToolOutputPaths[tool.outputComponent];
if (!path || !type) {
throw new Error(`Tool output type ${tool.outputComponent} is not supported`);
}
const files: MessageFile[] = [];
const toolOutputs: Array<Record<string, string>> = [];
if (outputs.length <= tool.outputComponentIdx) {
throw new Error(`Tool output component index is out of bounds`);
}
// if its not an object, return directly
if (
outputs[tool.outputComponentIdx] !== undefined &&
typeof outputs[tool.outputComponentIdx] !== "object"
) {
return {
outputs: [{ [tool.name + "-0"]: outputs[tool.outputComponentIdx] }],
display: tool.showOutput,
};
}
await Promise.all(
jp
.query(outputs[tool.outputComponentIdx], path)
.map(async (output: string | string[], idx) => {
const arrayedOutput = Array.isArray(output) ? output : [output];
if (type === "file") {
// output files are actually URLs
await Promise.all(
arrayedOutput.map(async (output, idx) => {
await fetch(output)
.then((res) => res.blob())
.then(async (blob) => {
const { ext, mime } = (await fileTypeFromBlob(blob)) ?? { ext: "octet-stream" };
return new File(
[blob],
`${idx}-${await sha256(JSON.stringify(params))}.${ext}`,
{
type: mime,
}
);
})
.then((file) => uploadFile(file, ctx.conv))
.then((file) => files.push(file));
})
);
toolOutputs.push({
[tool.name + "-" + idx.toString()]:
`Only and always answer: 'I used the tool ${tool.displayName}, here is the result.' Don't add anything else.`,
});
} else {
for (const output of arrayedOutput) {
toolOutputs.push({
[tool.name + "-" + idx.toString()]: output,
});
}
}
})
);
for (const file of files) {
yield {
type: MessageUpdateType.File,
name: file.name,
sha: file.value,
mime: file.mime,
};
}
return { outputs: toolOutputs, display: tool.showOutput };
};
}
export const toolFromConfigs = configTools.parse(JSON5.parse(config.TOOLS)) satisfies ConfigTool[];
| chat-ui/src/lib/server/tools/index.ts/0 | {
"file_path": "chat-ui/src/lib/server/tools/index.ts",
"repo_id": "chat-ui",
"token_count": 3695
} | 88 |
import type { SerializedHTMLElement } from "./types";
interface DBSCANOptions<T> {
dataset: T[];
epsilon?: number;
epsilonCompare?: (distance: number, epsilon: number) => boolean;
minimumPoints?: number;
distanceFunction: (a: T, b: T) => number;
}
export function spatialParser() {
/**
* Implementation for dbscan, inlined and migrated to typescript from https://github.com/cdxOo/dbscan (MIT License)
*/
const DBSCAN = <T>({
dataset,
epsilon = 1,
epsilonCompare = (dist, e) => dist < e,
minimumPoints = 2,
distanceFunction,
}: DBSCANOptions<T>) => {
const visitedIndices: Record<number, boolean> = {};
const isVisited = (i: number) => visitedIndices[i];
const markVisited = (i: number) => {
visitedIndices[i] = true;
};
const clusteredIndices: Record<number, boolean> = {};
const isClustered = (i: number) => clusteredIndices[i];
const markClustered = (i: number) => {
clusteredIndices[i] = true;
};
const uniqueMerge = <U>(targetArray: U[], sourceArray: U[]) => {
for (let i = 0; i < sourceArray.length; i += 1) {
const item = sourceArray[i];
if (targetArray.indexOf(item) < 0) {
targetArray.push(item);
}
}
};
const findNeighbors = (index: number) => {
const neighbors = [];
for (let other = 0; other < dataset.length; other += 1) {
const distance = distanceFunction(dataset[index], dataset[other]);
if (epsilonCompare(distance, epsilon)) {
neighbors.push(other);
}
}
return neighbors;
};
const noise: number[] = [];
const addNoise = (i: number) => noise.push(i);
const clusters: number[][] = [];
const createCluster = () => clusters.push([]) - 1;
const addIndexToCluster = (c: number, i: number) => {
clusters[c].push(i);
markClustered(i);
};
const expandCluster = (c: number, neighbors: number[]) => {
for (let i = 0; i < neighbors.length; i += 1) {
const neighborIndex = neighbors[i];
if (!isVisited(neighborIndex)) {
markVisited(neighborIndex);
const secondaryNeighbors = findNeighbors(neighborIndex);
if (secondaryNeighbors.length >= minimumPoints) {
uniqueMerge(neighbors, secondaryNeighbors);
}
}
if (!isClustered(neighborIndex)) {
addIndexToCluster(c, neighborIndex);
}
}
};
dataset.forEach((_, index) => {
if (!isVisited(index)) {
markVisited(index);
const neighbors = findNeighbors(index);
if (neighbors.length < minimumPoints) {
addNoise(index);
} else {
const clusterIndex = createCluster();
addIndexToCluster(clusterIndex, index);
expandCluster(clusterIndex, neighbors);
}
}
});
return { clusters, noise };
};
// -----------
// Scraping implementation
const IgnoredTagsList = [
"footer",
"nav",
"aside",
"script",
"style",
"noscript",
"form",
"button",
];
const InlineTags = [
"a",
"abbrv",
"span",
"address",
"time",
"acronym",
"strong",
"b",
"br",
"sub",
"sup",
"tt",
"var",
"em",
"i",
];
type ReadableNode = HTMLElement;
type NodeWithRect = {
node: ReadableNode;
rect: DOMRect;
};
const isOnlyChild = (node: Node) => {
if (!node.parentElement) return true;
if (node.parentElement.nodeName === "body") return false;
if (node.parentElement.childNodes.length === 1) return true;
return false;
};
const hasValidInlineParent = (node: Node) => {
return node.parentElement && !node.parentElement.matches("div, section, article, main, body ");
};
const hasValidParent = (node: Node) => {
return node.parentElement && !node.parentElement.isSameNode(document.body);
};
const possibleCodeParents = Array.from(document.querySelectorAll("pre, p"));
const possibleTableParents = Array.from(document.querySelectorAll("table"));
const possibleListParents = Array.from(document.querySelectorAll("ul, ol"));
/**
* We want to find the highest parent of text node in the cluster.
* For example in this case: <p><span>Text here</span></p>
* the P tag is highest parent.
*/
const findHighestDirectParentOfReadableNode = (node: Node): HTMLElement => {
// go up the tree until the parent is no longer an only child
let parent = node.parentElement;
// if the parent is an inline tag, then go up one more level
while (
parent &&
hasValidInlineParent(parent) &&
InlineTags.includes(parent?.tagName.toLowerCase())
) {
parent = parent.parentElement;
}
while (parent && isOnlyChild(parent)) {
if (!hasValidParent(parent)) break;
parent = parent.parentElement;
}
if (!parent) {
throw new Error(
"disconnected node found, this should not really be possible when traversing through the dom"
);
}
// if the parent is a span, code or div tag check if there is a pre tag or p tag above it
if (["span", "code", "div"].includes(parent.nodeName.toLowerCase())) {
const hasParent = possibleCodeParents.find((tag) => tag.contains(parent)) as HTMLElement;
if (hasParent) {
parent = hasParent;
}
}
// if the parent is a li tag check if there is a ul or ol tag above it
if (parent.nodeName.toLowerCase() === "li") {
const hasParent = possibleListParents.find((tag) => tag.contains(parent)) as HTMLElement;
if (hasParent) {
parent = hasParent;
}
}
// if the parent is a td, th, tr tag check if there is a table tag above it
if (["td", "th", "tr"].includes(parent.nodeName.toLowerCase())) {
const hasParent = possibleTableParents.find((tag) => tag.contains(parent)) as HTMLElement;
if (hasParent) {
parent = hasParent;
}
}
return parent;
};
const barredNodes = Array.from(document.querySelectorAll(IgnoredTagsList.join(",")));
const doesNodePassHeuristics = (node: Node) => {
if ((node.textContent ?? "").trim().length < 10) {
return false;
}
const parentNode = findHighestDirectParentOfReadableNode(node);
if (parentNode && parentNode instanceof Element) {
if (
!parentNode.checkVisibility({
checkOpacity: true,
checkVisibilityCSS: true,
})
)
return false;
const rect = parentNode.getBoundingClientRect();
// elements that are readable usually don't have really small height or width
if (rect.width < 4 || rect.height < 4) {
return false;
}
}
if (parentNode && parentNode instanceof Element) {
if (barredNodes.some((barredNode) => barredNode.contains(parentNode))) {
return false;
}
}
return true;
};
const getAllReadableNodes = (): NodeWithRect[] => {
if (!document.body) throw new Error("Page failed to load");
const treeWalker = document.createTreeWalker(document.body, NodeFilter.SHOW_TEXT, {
acceptNode(node) {
if (doesNodePassHeuristics(node)) {
return NodeFilter.FILTER_ACCEPT;
} else {
return NodeFilter.FILTER_SKIP;
}
},
});
const readableNodes = [];
while (treeWalker.nextNode()) {
readableNodes.push(treeWalker.currentNode as ReadableNode);
}
/*
* <table><p>hello</p><p>world</p></table>
* table is already included in the parent of the first p tag
*/
const parentsForReadableNodes = readableNodes.map(findHighestDirectParentOfReadableNode);
const listWithOnlyParents: HTMLElement[] = [];
// find unique nodes in the parent list, a unique node is a node that is not a child of any other node in the list
for (let i = 0; i < parentsForReadableNodes.length; i++) {
const node = parentsForReadableNodes[i];
const hasParentInList = parentsForReadableNodes.find((otherNode, idx) => {
if (i === idx) return false;
return otherNode.contains(node);
});
listWithOnlyParents.push(hasParentInList ? hasParentInList : node);
}
const uniqueParents = Array.from(new Set(listWithOnlyParents));
return uniqueParents.map((node) => {
return {
node,
rect: node.getBoundingClientRect(),
};
});
};
const distanceFunction = (a: NodeWithRect, b: NodeWithRect) => {
// we make two assumptions here which are fine to make for rects returned from getBoundingClientRect
// 1. rects are upright and not rotated
// 2. If two rects intersect, we assume distance to be 0
let dx = 0;
let dy = 0;
const rect1 = a.rect;
const rect2 = b.rect;
// Calculate the horizontal distance
if (rect1.x + rect1.width < rect2.x) {
dx = rect2.x - (rect1.x + rect1.width);
} else if (rect2.x + rect2.width < rect1.x) {
dx = rect1.x - (rect2.x + rect2.width);
}
// Calculate the vertical distance
if (rect1.y + rect1.height < rect2.y) {
dy = rect2.y - (rect1.y + rect1.height);
} else if (rect2.y + rect2.height < rect1.y) {
dy = rect1.y - (rect2.y + rect2.height);
}
const distance = Math.sqrt(dx * dx + dy * dy);
// Return the Euclidean distance
return distance;
};
/**
* Clusters nodes using dbscan
*/
const clusterReadableNodes = (nodes: NodeWithRect[]) => {
const { clusters } = DBSCAN({
dataset: nodes,
epsilon: 28,
minimumPoints: 1,
distanceFunction,
});
return clusters;
};
const totalTextLength = (cluster: number[]) => {
return cluster
.map((t) => readableNodes[t].node.innerText?.replaceAll(/ {2}|\r\n|\n|\r/gm, ""))
.join("").length;
};
const approximatelyEqual = (a: number, b: number, epsilon = 1) => {
return Math.abs(a - b) < epsilon;
};
const getClusterBounds = (cluster: number[]) => {
const leftMostPoint = Math.min(...cluster.map((c) => readableNodes[c].rect.x));
const topMostPoint = Math.min(...cluster.map((c) => readableNodes[c].rect.y));
const rightMostPoint = Math.max(
...cluster.map((c) => readableNodes[c].rect.x + readableNodes[c].rect.width)
);
const bottomMostPoint = Math.max(
...cluster.map((c) => readableNodes[c].rect.y + readableNodes[c].rect.height)
);
return {
// left most element
x: leftMostPoint,
y: topMostPoint,
width: rightMostPoint - leftMostPoint,
height: bottomMostPoint - topMostPoint,
};
};
const round = (num: number, decimalPlaces = 2) => {
const factor = Math.pow(10, decimalPlaces);
return Math.round(num * factor) / factor;
};
/** minimum distance to center of the screen */
const clusterCentrality = (cluster: number[]) => {
const bounds = getClusterBounds(cluster);
const centerOfScreen = window.innerWidth / 2;
// the cluster contains the center of the screen
if (bounds.x < centerOfScreen && bounds.x + bounds.width > centerOfScreen) {
return 0;
}
// the cluster is to the left of the screen
if (bounds.x + bounds.width < centerOfScreen) {
return centerOfScreen - (bounds.x + bounds.width);
}
// the cluster is to the right of the screen
return bounds.x - centerOfScreen;
};
/** measure of text share that belong to the cluster */
const percentageTextShare = (cluster: number[], totalLength: number) => {
// apply an exponentially increasing penalty for centrality per 100 pixels distance from center
return round((totalTextLength(cluster) / totalLength) * 100);
};
const shouldMergeClusters = (clusterA: number[], clusterB: number[]) => {
const clusterABounds = getClusterBounds(clusterA);
const clusterBBounds = getClusterBounds(clusterB);
// A cluster is horizontally aligned if the x and width are roughly equal
const isHorizontallyAligned =
approximatelyEqual(clusterABounds.x, clusterBBounds.x, 40) &&
approximatelyEqual(clusterABounds.width, clusterBBounds.width, 40);
if (!isHorizontallyAligned) return false;
// check the y gap between the clusters
const higherCluster = clusterABounds.y < clusterBBounds.y ? clusterABounds : clusterBBounds;
const lowerCluster = clusterABounds.y < clusterBBounds.y ? clusterBBounds : clusterABounds;
const yGap = lowerCluster.y - (higherCluster.y + higherCluster.height);
if (approximatelyEqual(yGap, 0, 100)) return true;
};
const findCriticalClusters = (clusters: number[][]) => {
// merge the clusters that have similar widths and x position
let i = 0;
while (i < clusters.length) {
const cluster = clusters[i];
for (let j = i + 1; j < clusters.length; j++) {
const otherCluster = clusters[j];
if (shouldMergeClusters(cluster, otherCluster)) {
cluster.push(...otherCluster);
clusters.splice(j, 1);
j -= 1;
}
}
i++;
}
const totalText = totalTextLength(clusters.flat());
// sort in descending order of text share
const clusterWithMetrics = clusters.map((cluster) => {
const centrality = clusterCentrality(cluster);
return {
cluster,
centrality,
percentageTextShare: percentageTextShare(cluster, totalText),
};
});
// if there is a dominant cluster with more than 60% text share, return that
const dominantCluster = clusterWithMetrics[0]?.percentageTextShare > 60;
if (dominantCluster) return [clusterWithMetrics[0].cluster];
// clusters are sorted by text share after applying a penalty for centrality
const sortedClusters = clusterWithMetrics.sort((a, b) => {
const penaltyForA = Math.pow(0.9, a.centrality / 100);
const penaltyForB = Math.pow(0.9, b.centrality / 100);
const adjustedTextShareA = a.percentageTextShare * penaltyForA;
const adjustedTextShareB = b.percentageTextShare * penaltyForB;
return adjustedTextShareB - adjustedTextShareA;
});
// find all clusters that are similar to the largest cluster in terms of text share
// and see if they are enough to cover at least 60% of the text share
const largeTextShareClusters = sortedClusters.filter((c) =>
approximatelyEqual(c.percentageTextShare, sortedClusters[0]?.percentageTextShare, 10)
);
const totalTextShareOfLargeClusters = largeTextShareClusters.reduce(
(acc, cluster) => acc + cluster.percentageTextShare,
0
);
if (totalTextShareOfLargeClusters > 60) {
return largeTextShareClusters.map((c) => c.cluster);
}
// choose clusters till the text share is greater than 60%
let totalTextShare = 0;
const criticalClusters = [];
for (const cluster of sortedClusters) {
/** Ignore clusters with less than 2%*/
if (cluster.percentageTextShare < 2) continue;
if (totalTextShare > 60) break;
criticalClusters.push(cluster.cluster);
totalTextShare += cluster.percentageTextShare;
}
// if the total text share is less than 60% then return an empty array
// as this website should not be particularly useful for the web search anyways
// this should almost never happen on structured website with a lot of text
if (totalTextShare < 60) {
return [];
}
return criticalClusters;
};
const allowListedAttributes = ["href", "src", "alt", "title", "class", "id"];
function serializeHTMLElement(node: Element): SerializedHTMLElement {
return {
tagName: node.tagName.toLowerCase(),
attributes: allowListedAttributes.reduce(
(acc, attr) => {
const value = node.getAttribute(attr);
if (value) {
acc[attr] = value;
}
return acc;
},
{} as Record<string, string>
),
content: Array.from(node.childNodes).map(serializeNode).filter(Boolean),
};
}
function serializeNode(node: Node): SerializedHTMLElement | string {
if (node.nodeType === 1) return serializeHTMLElement(node as Element);
else if (node.nodeType === 3) return node.textContent ?? "";
else return "";
}
function getPageMetadata(): {
title: string;
siteName?: string;
author?: string;
description?: string;
createdAt?: string;
updatedAt?: string;
} {
const title = document.title ?? "";
const siteName =
document.querySelector("meta[property='og:site_name']")?.getAttribute("content") ?? undefined;
const author =
document.querySelector("meta[name='author']")?.getAttribute("content") ?? undefined;
const description =
document.querySelector("meta[name='description']")?.getAttribute("content") ??
document.querySelector("meta[property='og:description']")?.getAttribute("content") ??
undefined;
const createdAt =
document.querySelector("meta[property='article:published_time']")?.getAttribute("content") ??
document.querySelector("meta[name='date']")?.getAttribute("content") ??
undefined;
const updatedAt =
document.querySelector("meta[property='article:modified_time']")?.getAttribute("content") ??
undefined;
return { title, siteName, author, description, createdAt, updatedAt };
}
const readableNodes = getAllReadableNodes();
const clusters = clusterReadableNodes(readableNodes);
const criticalClusters = findCriticalClusters(clusters);
// filter readable nodes using the above information as well as heuristics
const filteredNodes = readableNodes.filter((_, idx) => {
return criticalClusters.some((cluster) => {
return cluster.includes(idx);
});
});
const elements = filteredNodes
.filter(
(node, idx, nodes) => !nodes.slice(idx + 1).some((otherNode) => node.node === otherNode.node)
)
.map<SerializedHTMLElement>(({ node }) => serializeHTMLElement(node));
const metadata = getPageMetadata();
return { ...metadata, elements };
}
| chat-ui/src/lib/server/websearch/scrape/parser.ts/0 | {
"file_path": "chat-ui/src/lib/server/websearch/scrape/parser.ts",
"repo_id": "chat-ui",
"token_count": 6106
} | 89 |
import { base } from "$app/paths";
import { ERROR_MESSAGES, error } from "$lib/stores/errors";
import { share } from "./utils/share";
import { page } from "$app/state";
export async function shareConversation(id: string, title: string) {
try {
if (id.length === 7) {
const shareUrl = `${
page.data.publicConfig.PUBLIC_SHARE_PREFIX ||
`${page.data.publicConfig.PUBLIC_ORIGIN || page.url.origin}${base}`
}/r/${id}`;
await share(shareUrl, title, true);
} else {
const res = await fetch(`${base}/conversation/${id}/share`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
});
if (!res.ok) {
error.set("Error while sharing conversation, try again.");
console.error("Error while sharing conversation: " + (await res.text()));
return;
}
const { shareId } = await res.json();
const shareUrl = `${
page.data.publicConfig.PUBLIC_SHARE_PREFIX ||
`${page.data.publicConfig.PUBLIC_ORIGIN || page.url.origin}${base}`
}/r/${shareId}`;
await share(shareUrl, title, true);
}
} catch (err) {
error.set(ERROR_MESSAGES.default);
console.error(err);
}
}
| chat-ui/src/lib/shareConversation.ts/0 | {
"file_path": "chat-ui/src/lib/shareConversation.ts",
"repo_id": "chat-ui",
"token_count": 470
} | 90 |
import type { MessageUpdate } from "./MessageUpdate";
import type { Timestamps } from "./Timestamps";
import type { WebSearch } from "./WebSearch";
import type { v4 } from "uuid";
export type Message = Partial<Timestamps> & {
from: "user" | "assistant" | "system";
id: ReturnType<typeof v4>;
content: string;
updates?: MessageUpdate[];
webSearchId?: WebSearch["_id"]; // legacy version
webSearch?: WebSearch;
reasoning?: string;
score?: -1 | 0 | 1;
/**
* Either contains the base64 encoded image data
* or the hash of the file stored on the server
**/
files?: MessageFile[];
interrupted?: boolean;
// needed for conversation trees
ancestors?: Message["id"][];
// goes one level deep
children?: Message["id"][];
};
export type MessageFile = {
type: "hash" | "base64";
name: string;
value: string;
mime: string;
};
| chat-ui/src/lib/types/Message.ts/0 | {
"file_path": "chat-ui/src/lib/types/Message.ts",
"repo_id": "chat-ui",
"token_count": 277
} | 91 |
import type { ObjectId } from "mongodb";
import type { Timestamps } from "./Timestamps";
export interface User extends Timestamps {
_id: ObjectId;
username?: string;
name: string;
email?: string;
avatarUrl: string | undefined;
hfUserId: string;
isAdmin?: boolean;
isEarlyAccess?: boolean;
}
| chat-ui/src/lib/types/User.ts/0 | {
"file_path": "chat-ui/src/lib/types/User.ts",
"repo_id": "chat-ui",
"token_count": 100
} | 92 |
import { browser } from "$app/environment";
export function isVirtualKeyboard(): boolean {
if (!browser) return false;
// Check for touch capability
if (navigator.maxTouchPoints > 0 && screen.width <= 768) return true;
// Check for touch events
if ("ontouchstart" in window) return true;
// Fallback to user agent string check
const userAgent = navigator.userAgent.toLowerCase();
return /android|webos|iphone|ipad|ipod|blackberry|iemobile|opera mini/i.test(userAgent);
}
| chat-ui/src/lib/utils/isVirtualKeyboard.ts/0 | {
"file_path": "chat-ui/src/lib/utils/isVirtualKeyboard.ts",
"repo_id": "chat-ui",
"token_count": 144
} | 93 |
export const webSearchToolId = "00000000000000000000000a";
export const fetchUrlToolId = "00000000000000000000000b";
export const imageGenToolId = "000000000000000000000001";
export const documentParserToolId = "000000000000000000000002";
| chat-ui/src/lib/utils/toolIds.ts/0 | {
"file_path": "chat-ui/src/lib/utils/toolIds.ts",
"repo_id": "chat-ui",
"token_count": 52
} | 94 |
<script lang="ts">
import { page } from "$app/state";
</script>
<div
class="flex items-center justify-center bg-gradient-to-t from-gray-200 text-gray-800 dark:from-gray-700 dark:text-gray-300"
>
<div
class="align-center -mt-24 flex flex-col justify-center rounded-xl border bg-white px-8 pb-2 pt-4 text-center dark:border-gray-700 dark:bg-gray-800"
>
<h1 class="mb-2 text-5xl font-semibold">{page.status}</h1>
<div class="-mx-8 my-2 h-px bg-gray-200 dark:bg-gray-700"></div>
<h2 class="max-w-sm text-lg">{page.error?.message}</h2>
{#if page.error?.errorId}
<div class="-mx-8 my-2 h-px bg-gray-200 dark:bg-gray-700"></div>
<pre class="max-w-sm whitespace-pre-wrap text-left font-mono text-xs">{page.error
.errorId}</pre>
{/if}
</div>
</div>
| chat-ui/src/routes/+error.svelte/0 | {
"file_path": "chat-ui/src/routes/+error.svelte",
"repo_id": "chat-ui",
"token_count": 342
} | 95 |
import { models } from "$lib/server/models";
export async function GET() {
const res = models
.filter((m) => m.unlisted == false)
.map((model) => ({
id: model.id,
name: model.name,
websiteUrl: model.websiteUrl ?? "https://huggingface.co",
modelUrl: model.modelUrl ?? "https://huggingface.co",
tokenizer: model.tokenizer,
datasetName: model.datasetName,
datasetUrl: model.datasetUrl,
displayName: model.displayName,
description: model.description ?? "",
logoUrl: model.logoUrl,
promptExamples: model.promptExamples ?? [],
preprompt: model.preprompt ?? "",
multimodal: model.multimodal ?? false,
unlisted: model.unlisted ?? false,
tools: model.tools ?? false,
hasInferenceAPI: model.hasInferenceAPI ?? false,
}));
return Response.json(res);
}
| chat-ui/src/routes/api/models/+server.ts/0 | {
"file_path": "chat-ui/src/routes/api/models/+server.ts",
"repo_id": "chat-ui",
"token_count": 311
} | 96 |
import type { RequestHandler } from "./$types";
import { collections } from "$lib/server/database";
import { ObjectId } from "mongodb";
import { error, redirect } from "@sveltejs/kit";
import { base } from "$app/paths";
import { z } from "zod";
import type { Message } from "$lib/types/Message";
import { models, validateModel } from "$lib/server/models";
import { defaultEmbeddingModel } from "$lib/server/embeddingModels";
import { v4 } from "uuid";
import { authCondition } from "$lib/server/auth";
import { usageLimits } from "$lib/server/usageLimits";
import { MetricsServer } from "$lib/server/metrics";
export const POST: RequestHandler = async ({ locals, request }) => {
const body = await request.text();
let title = "";
const parsedBody = z
.object({
fromShare: z.string().optional(),
model: validateModel(models),
assistantId: z.string().optional(),
preprompt: z.string().optional(),
})
.safeParse(JSON.parse(body));
if (!parsedBody.success) {
error(400, "Invalid request");
}
const values = parsedBody.data;
const convCount = await collections.conversations.countDocuments(authCondition(locals));
if (usageLimits?.conversations && convCount > usageLimits?.conversations) {
error(429, "You have reached the maximum number of conversations. Delete some to continue.");
}
const model = models.find((m) => (m.id || m.name) === values.model);
if (!model) {
error(400, "Invalid model");
}
let messages: Message[] = [
{
id: v4(),
from: "system",
content: values.preprompt ?? "",
createdAt: new Date(),
updatedAt: new Date(),
children: [],
ancestors: [],
},
];
let rootMessageId: Message["id"] = messages[0].id;
let embeddingModel: string;
if (values.fromShare) {
const conversation = await collections.sharedConversations.findOne({
_id: values.fromShare,
});
if (!conversation) {
error(404, "Conversation not found");
}
title = conversation.title;
messages = conversation.messages;
rootMessageId = conversation.rootMessageId ?? rootMessageId;
values.model = conversation.model;
values.preprompt = conversation.preprompt;
values.assistantId = conversation.assistantId?.toString();
embeddingModel = conversation.embeddingModel;
}
embeddingModel ??= model.embeddingModel ?? defaultEmbeddingModel.name;
if (model.unlisted) {
error(400, "Can't start a conversation with an unlisted model");
}
// get preprompt from assistant if it exists
const assistant = await collections.assistants.findOne({
_id: new ObjectId(values.assistantId),
});
if (assistant) {
values.preprompt = assistant.preprompt;
} else {
values.preprompt ??= model?.preprompt ?? "";
}
if (messages && messages.length > 0 && messages[0].from === "system") {
messages[0].content = values.preprompt;
}
const res = await collections.conversations.insertOne({
_id: new ObjectId(),
title: title || "New Chat",
rootMessageId,
messages,
model: values.model,
preprompt: values.preprompt,
assistantId: values.assistantId ? new ObjectId(values.assistantId) : undefined,
createdAt: new Date(),
updatedAt: new Date(),
userAgent: request.headers.get("User-Agent") ?? undefined,
embeddingModel,
...(locals.user ? { userId: locals.user._id } : { sessionId: locals.sessionId }),
...(values.fromShare ? { meta: { fromShareId: values.fromShare } } : {}),
});
MetricsServer.getMetrics().model.conversationsTotal.inc({ model: values.model });
return new Response(
JSON.stringify({
conversationId: res.insertedId.toString(),
}),
{ headers: { "Content-Type": "application/json" } }
);
};
export const GET: RequestHandler = async () => {
redirect(302, `${base}/`);
};
| chat-ui/src/routes/conversation/+server.ts/0 | {
"file_path": "chat-ui/src/routes/conversation/+server.ts",
"repo_id": "chat-ui",
"token_count": 1252
} | 97 |
<script lang="ts">
import { page } from "$app/state";
import { base } from "$app/paths";
import { goto } from "$app/navigation";
import { onMount } from "svelte";
import { usePublicConfig } from "$lib/utils/PublicConfig.svelte";
import ChatWindow from "$lib/components/chat/ChatWindow.svelte";
import { findCurrentModel } from "$lib/utils/models";
import { useSettingsStore } from "$lib/stores/settings";
import { ERROR_MESSAGES, error } from "$lib/stores/errors";
import { pendingMessage } from "$lib/stores/pendingMessage";
let { data } = $props();
let loading = $state(false);
let files: File[] = $state([]);
const settings = useSettingsStore();
const modelId = page.params.model;
const publicConfig = usePublicConfig();
async function createConversation(message: string) {
try {
loading = true;
const res = await fetch(`${base}/conversation`, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({
model: modelId,
preprompt: $settings.customPrompts[modelId],
}),
});
if (!res.ok) {
error.set("Error while creating conversation, try again.");
console.error("Error while creating conversation: " + (await res.text()));
return;
}
const { conversationId } = await res.json();
// Ugly hack to use a store as temp storage, feel free to improve ^^
pendingMessage.set({
content: message,
files,
});
// invalidateAll to update list of conversations
await goto(`${base}/conversation/${conversationId}`, { invalidateAll: true });
} catch (err) {
error.set(ERROR_MESSAGES.default);
console.error(err);
} finally {
loading = false;
}
}
onMount(async () => {
const query = page.url.searchParams.get("q");
if (query) createConversation(query);
settings.instantSet({ activeModel: modelId });
});
</script>
<svelte:head>
<meta property="og:title" content={modelId + " - " + publicConfig.PUBLIC_APP_NAME} />
<meta property="og:type" content="link" />
<meta property="og:description" content={`Use ${modelId} with ${publicConfig.PUBLIC_APP_NAME}`} />
<meta
property="og:image"
content="{publicConfig.PUBLIC_ORIGIN || page.url.origin}{base}/models/{modelId}/thumbnail.png"
/>
<meta property="og:url" content={page.url.href} />
<meta name="twitter:card" content="summary_large_image" />
</svelte:head>
<ChatWindow
on:message={(ev) => createConversation(ev.detail)}
{loading}
currentModel={findCurrentModel([...data.models, ...data.oldModels], modelId)}
models={data.models}
bind:files
/>
| chat-ui/src/routes/models/[...model]/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/models/[...model]/+page.svelte",
"repo_id": "chat-ui",
"token_count": 931
} | 98 |
<script lang="ts">
import type { PageData } from "./$types";
import { page } from "$app/state";
import AssistantSettings from "$lib/components/AssistantSettings.svelte";
interface Props {
data: PageData;
}
let { data }: Props = $props();
let assistant = data.assistants.find((el) => el._id.toString() === page.params.assistantId);
</script>
<AssistantSettings {assistant} models={data.models} />
| chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/edit/+page.svelte/0 | {
"file_path": "chat-ui/src/routes/settings/(nav)/assistants/[assistantId]/edit/+page.svelte",
"repo_id": "chat-ui",
"token_count": 135
} | 99 |
import { sveltekit } from "@sveltejs/kit/vite";
import Icons from "unplugin-icons/vite";
import { promises } from "fs";
import { defineConfig } from "vitest/config";
import { resolve } from "path";
import fs from "fs-extra";
import { spawn } from "child_process";
import type { Plugin } from "vite";
// used to load fonts server side for thumbnail generation
function loadTTFAsArrayBuffer() {
return {
name: "load-ttf-as-array-buffer",
async transform(_src, id) {
if (id.endsWith(".ttf")) {
return `export default new Uint8Array([
${new Uint8Array(await promises.readFile(id))}
]).buffer`;
}
},
};
}
const isViteNode = process.argv.some((arg) => arg.includes("vite-node")) || !!process.env.VITE_NODE;
const skipLlamaCppBuild = process.env.SKIP_LLAMA_CPP_BUILD === "true";
const shouldCopyLlama =
process.env.npm_lifecycle_event === "build" && !isViteNode && !skipLlamaCppBuild; // Copy node-llama-cpp/llama files to build output
function copyLlamaFiles() {
return {
name: "copy-llama-files",
apply: "build" as const,
closeBundle: async () => {
try {
// Run npx command first and pipe IO
console.log("Running node-llama-cpp source download...");
await new Promise((resolve, reject) => {
const npxProcess = spawn("npx", ["--no", "node-llama-cpp", "source", "download"], {
stdio: "inherit", // Pipe all IO to parent process
shell: true,
});
npxProcess.on("close", (code) => {
if (code === 0) {
console.log("✓ Successfully downloaded llama source files");
resolve(code);
} else {
reject(new Error(`npx command failed with code ${code}`));
}
});
npxProcess.on("error", (err) => {
reject(err);
});
});
const sourcePath = resolve("node_modules/node-llama-cpp/llama");
const destPath = resolve("build/server/llama");
// Ensure destination directory exists
await fs.ensureDir(destPath);
// Copy files - using a filter to prevent copying files to subdirectories of themselves
await fs.copy(sourcePath, destPath, {
filter: (src, dest) => {
// Skip if source path is inside destination path or vice versa
if (src.includes(destPath) || dest.includes(sourcePath)) {
console.log(`Skipping problematic copy: ${src} -> ${dest}`);
return false;
}
return true;
},
overwrite: true,
dereference: true,
});
console.log("✓ Successfully copied llama files to build output");
} catch (error) {
console.error("Error in llama files process:", error);
}
},
} satisfies Plugin;
}
export default defineConfig({
plugins: [
sveltekit(),
Icons({
compiler: "svelte",
}),
loadTTFAsArrayBuffer(),
...(shouldCopyLlama ? [copyLlamaFiles()] : []),
],
optimizeDeps: {
include: ["uuid", "@huggingface/transformers", "sharp", "@gradio/client", "clsx"],
},
test: {
workspace: [
{
// Client-side tests (Svelte components)
extends: "./vite.config.ts",
test: {
name: "client",
environment: "browser",
browser: {
enabled: true,
provider: "playwright",
instances: [{ browser: "chromium", headless: true }],
},
include: ["src/**/*.svelte.{test,spec}.{js,ts}"],
exclude: ["src/lib/server/**", "src/**/*.ssr.{test,spec}.{js,ts}"],
setupFiles: ["./scripts/setups/vitest-setup-client.ts"],
},
},
{
// SSR tests (Server-side rendering)
extends: "./vite.config.ts",
test: {
name: "ssr",
environment: "node",
include: ["src/**/*.ssr.{test,spec}.{js,ts}"],
},
},
{
// Server-side tests (Node.js utilities)
extends: "./vite.config.ts",
test: {
name: "server",
environment: "node",
include: ["src/**/*.{test,spec}.{js,ts}"],
exclude: ["src/**/*.svelte.{test,spec}.{js,ts}", "src/**/*.ssr.{test,spec}.{js,ts}"],
setupFiles: ["./scripts/setups/vitest-setup-server.ts"],
},
},
],
},
});
| chat-ui/vite.config.ts/0 | {
"file_path": "chat-ui/vite.config.ts",
"repo_id": "chat-ui",
"token_count": 1683
} | 100 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
SPEED_TEST_N_EXAMPLES = 500_000
RESULTS_BASEPATH, RESULTS_FILENAME = os.path.split(__file__)
RESULTS_FILE_PATH = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def map(dataset: datasets.Dataset, **kwargs):
_ = dataset.map(**kwargs)
@get_duration
def filter(dataset: datasets.Dataset, **kwargs):
_ = dataset.filter(**kwargs)
def benchmark_map_filter():
times = {"num examples": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
features = datasets.Features({"text": datasets.Value("string"), "numbers": datasets.Value("float32")})
dataset = generate_example_dataset(
os.path.join(tmp_dir, "dataset.arrow"), features, num_examples=SPEED_TEST_N_EXAMPLES
)
tokenizer = transformers.AutoTokenizer.from_pretrained("bert-base-cased", use_fast=True)
def tokenize(examples):
return tokenizer(examples["text"])
times["map identity"] = map(dataset)
times["map identity batched"] = map(dataset, batched=True)
times["map no-op batched"] = map(dataset, function=lambda x: None, batched=True)
with dataset.formatted_as(type="numpy"):
times["map no-op batched numpy"] = map(dataset, function=lambda x: None, batched=True)
with dataset.formatted_as(type="pandas"):
times["map no-op batched pandas"] = map(dataset, function=lambda x: None, batched=True)
with dataset.formatted_as(type="torch", columns="numbers"):
times["map no-op batched pytorch"] = map(dataset, function=lambda x: None, batched=True)
with dataset.formatted_as(type="tensorflow", columns="numbers"):
times["map no-op batched tensorflow"] = map(dataset, function=lambda x: None, batched=True)
times["map fast-tokenizer batched"] = map(dataset, function=tokenize, batched=True)
times["filter"] = filter(dataset)
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(RESULTS_FILE_PATH, "wb") as f:
f.write(json.dumps(times).encode("utf-8"))
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| datasets/benchmarks/benchmark_map_filter.py/0 | {
"file_path": "datasets/benchmarks/benchmark_map_filter.py",
"repo_id": "datasets",
"token_count": 996
} | 101 |
# Build and load
Nearly every deep learning workflow begins with loading a dataset, which makes it one of the most important steps. With 🤗 Datasets, there are more than 900 datasets available to help you get started with your NLP task. All you have to do is call: [`load_dataset`] to take your first step. This function is a true workhorse in every sense because it builds and loads every dataset you use.
## ELI5: `load_dataset`
Let's begin with a basic Explain Like I'm Five.
A dataset is a directory that contains:
- Some data files in generic formats (JSON, CSV, Parquet, text, etc.)
- A dataset card named `README.md` that contains documentation about the dataset as well as a YAML header to define the datasets tags and configurations
The [`load_dataset`] function fetches the requested dataset locally or from the Hugging Face Hub.
The Hub is a central repository where all the Hugging Face datasets and models are stored.
If the dataset only contains data files, then [`load_dataset`] automatically infers how to load the data files from their extensions (json, csv, parquet, txt, etc.).
Under the hood, 🤗 Datasets will use an appropriate [`DatasetBuilder`] based on the data files format. There exist one builder per data file format in 🤗 Datasets:
* [`datasets.packaged_modules.text.Text`] for text
* [`datasets.packaged_modules.csv.Csv`] for CSV and TSV
* [`datasets.packaged_modules.json.Json`] for JSON and JSONL
* [`datasets.packaged_modules.parquet.Parquet`] for Parquet
* [`datasets.packaged_modules.arrow.Arrow`] for Arrow (streaming file format)
* [`datasets.packaged_modules.sql.Sql`] for SQL databases
* [`datasets.packaged_modules.imagefolder.ImageFolder`] for image folders
* [`datasets.packaged_modules.audiofolder.AudioFolder`] for audio folders
<Tip>
Read the [Share](./upload_dataset) section to learn more about how to share a dataset.
</Tip>
🤗 Datasets downloads the dataset files from the original URL, generates the dataset and caches it in an Arrow table on your drive.
If you've downloaded the dataset before, then 🤗 Datasets will reload it from the cache to save you the trouble of downloading it again.
Now that you have a high-level understanding about how datasets are built, let's take a closer look at the nuts and bolts of how all this works.
## Building a dataset
When you load a dataset for the first time, 🤗 Datasets takes the raw data file and builds it into a table of rows and typed columns. There are two main classes responsible for building a dataset: [`BuilderConfig`] and [`DatasetBuilder`].
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/builderconfig.png"/>
</div>
### BuilderConfig[[datasets-builderconfig]]
[`BuilderConfig`] is the configuration class of [`DatasetBuilder`]. The [`BuilderConfig`] contains the following basic attributes about a dataset:
| Attribute | Description |
|---------------|--------------------------------------------------------------|
| `name` | Short name of the dataset. |
| `version` | Dataset version identifier. |
| `data_dir` | Stores the path to a local folder containing the data files. |
| `data_files` | Stores paths to local data files. |
| `description` | Description of the dataset. |
If you want to add additional attributes to your dataset such as the class labels, you can subclass the base [`BuilderConfig`] class. There are two ways to populate the attributes of a [`BuilderConfig`] class or subclass:
- Provide a list of predefined [`BuilderConfig`] class (or subclass) instances in the datasets [`DatasetBuilder.BUILDER_CONFIGS`] attribute.
- When you call [`load_dataset`], any keyword arguments that are not specific to the method will be used to set the associated attributes of the [`BuilderConfig`] class. This will override the predefined attributes if a specific configuration was selected.
You can also set the [`DatasetBuilder.BUILDER_CONFIG_CLASS`] to any custom subclass of [`BuilderConfig`].
### DatasetBuilder[[datasets-datasetbuilder]]
[`DatasetBuilder`] accesses all the attributes inside [`BuilderConfig`] to build the actual dataset.
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets/datasetbuilder.png"/>
</div>
There are three main methods in [`DatasetBuilder`]:
1. [`DatasetBuilder._info`] is in charge of defining the dataset attributes. When you call `dataset.info`, 🤗 Datasets returns the information stored here. Likewise, the [`Features`] are also specified here. Remember, the [`Features`] are like the skeleton of the dataset. It provides the names and types of each column.
2. [`DatasetBuilder._split_generator`] downloads or retrieves the requested data files, organizes them into splits, and defines specific arguments for the generation process. This method has a [`DownloadManager`] that downloads files or fetches them from your local filesystem. Within the [`DownloadManager`], there is a [`DownloadManager.download_and_extract`] method that accepts a dictionary of URLs to the original data files, and downloads the requested files. Accepted inputs include: a single URL or path, or a list/dictionary of URLs or paths. Any compressed file types like TAR, GZIP and ZIP archives will be automatically extracted.
Once the files are downloaded, [`SplitGenerator`] organizes them into splits. The [`SplitGenerator`] contains the name of the split, and any keyword arguments that are provided to the [`DatasetBuilder._generate_examples`] method. The keyword arguments can be specific to each split, and typically comprise at least the local path to the data files for each split.
3. [`DatasetBuilder._generate_examples`] reads and parses the data files for a split. Then it yields dataset examples according to the format specified in the `features` from [`DatasetBuilder._info`]. The input of [`DatasetBuilder._generate_examples`] is actually the `filepath` provided in the keyword arguments of the last method.
The dataset is generated with a Python generator, which doesn't load all the data in memory. As a result, the generator can handle large datasets. However, before the generated samples are flushed to the dataset file on disk, they are stored in an `ArrowWriter` buffer. This means the generated samples are written by batch. If your dataset samples consumes a lot of memory (images or videos), then make sure to specify a low value for the `DEFAULT_WRITER_BATCH_SIZE` attribute in [`DatasetBuilder`]. We recommend not exceeding a size of 200 MB.
## Maintaining integrity
To ensure a dataset is complete, [`load_dataset`] will perform a series of tests on the downloaded files to make sure everything is there. This way, you don't encounter any surprises when your requested dataset doesn't get generated as expected. [`load_dataset`] verifies:
- The number of splits in the generated `DatasetDict`.
- The number of samples in each split of the generated `DatasetDict`.
- The list of downloaded files.
- The SHA256 checksums of the downloaded files (disabled by default).
If the dataset doesn't pass the verifications, it is likely that the dataset author made some changes in the data files.
In this case, an error is raised to alert that the dataset has changed.
To ignore the error, one needs to specify `verification_mode="no_checks"` in [`load_dataset`].
Anytime you see a verification error, feel free to open a discussion or pull request in the corresponding dataset "Community" tab, so that the integrity checks for that dataset are updated.
## Security
The dataset repositories on the Hub are scanned for malware, see more information [here](https://huggingface.co/docs/hub/security#malware-scanning).
| datasets/docs/source/about_dataset_load.mdx/0 | {
"file_path": "datasets/docs/source/about_dataset_load.mdx",
"repo_id": "datasets",
"token_count": 2241
} | 102 |
# Overview
The how-to guides offer a more comprehensive overview of all the tools 🤗 Datasets offers and how to use them. This will help you tackle messier real-world datasets where you may need to manipulate the dataset structure or content to get it ready for training.
The guides assume you are familiar and comfortable with the 🤗 Datasets basics. We recommend newer users check out our [tutorials](tutorial) first.
<Tip>
Interested in learning more? Take a look at [Chapter 5](https://huggingface.co/course/chapter5/1?fw=pt) of the Hugging Face course!
</Tip>
The guides are organized into six sections:
- <span class="underline decoration-sky-400 decoration-2 font-semibold">General usage</span>: Functions for general dataset loading and processing. The functions shown in this section are applicable across all dataset modalities.
- <span class="underline decoration-pink-400 decoration-2 font-semibold">Audio</span>: How to load, process, and share audio datasets.
- <span class="underline decoration-yellow-400 decoration-2 font-semibold">Vision</span>: How to load, process, and share image and video datasets.
- <span class="underline decoration-green-400 decoration-2 font-semibold">Text</span>: How to load, process, and share text datasets.
- <span class="underline decoration-orange-400 decoration-2 font-semibold">Tabular</span>: How to load, process, and share tabular datasets.
- <span class="underline decoration-indigo-400 decoration-2 font-semibold">Dataset repository</span>: How to share and upload a dataset to the <a href="https://huggingface.co/datasets">Hub</a>.
If you have any questions about 🤗 Datasets, feel free to join and ask the community on our [forum](https://discuss.huggingface.co/c/datasets/10).
| datasets/docs/source/how_to.md/0 | {
"file_path": "datasets/docs/source/how_to.md",
"repo_id": "datasets",
"token_count": 471
} | 103 |
# Main classes
## DatasetInfo
[[autodoc]] datasets.DatasetInfo
## Dataset
The base class [`Dataset`] implements a Dataset backed by an Apache Arrow table.
[[autodoc]] datasets.Dataset
- add_column
- add_item
- from_file
- from_buffer
- from_pandas
- from_dict
- from_generator
- data
- cache_files
- num_columns
- num_rows
- column_names
- shape
- unique
- flatten
- cast
- cast_column
- remove_columns
- rename_column
- rename_columns
- select_columns
- class_encode_column
- __len__
- __iter__
- iter
- formatted_as
- set_format
- set_transform
- reset_format
- with_format
- with_transform
- __getitem__
- cleanup_cache_files
- map
- filter
- select
- sort
- shuffle
- skip
- take
- train_test_split
- shard
- repeat
- to_tf_dataset
- push_to_hub
- save_to_disk
- load_from_disk
- flatten_indices
- to_csv
- to_pandas
- to_dict
- to_json
- to_parquet
- to_sql
- to_iterable_dataset
- add_faiss_index
- add_faiss_index_from_external_arrays
- save_faiss_index
- load_faiss_index
- add_elasticsearch_index
- load_elasticsearch_index
- list_indexes
- get_index
- drop_index
- search
- search_batch
- get_nearest_examples
- get_nearest_examples_batch
- info
- split
- builder_name
- citation
- config_name
- dataset_size
- description
- download_checksums
- download_size
- features
- homepage
- license
- size_in_bytes
- supervised_keys
- version
- from_csv
- from_json
- from_parquet
- from_text
- from_sql
- align_labels_with_mapping
[[autodoc]] datasets.concatenate_datasets
[[autodoc]] datasets.interleave_datasets
[[autodoc]] datasets.distributed.split_dataset_by_node
[[autodoc]] datasets.enable_caching
[[autodoc]] datasets.disable_caching
[[autodoc]] datasets.is_caching_enabled
[[autodoc]] datasets.Column
## DatasetDict
Dictionary with split names as keys ('train', 'test' for example), and `Dataset` objects as values.
It also has dataset transform methods like map or filter, to process all the splits at once.
[[autodoc]] datasets.DatasetDict
- data
- cache_files
- num_columns
- num_rows
- column_names
- shape
- unique
- cleanup_cache_files
- map
- filter
- sort
- shuffle
- set_format
- reset_format
- formatted_as
- with_format
- with_transform
- flatten
- cast
- cast_column
- remove_columns
- rename_column
- rename_columns
- select_columns
- class_encode_column
- push_to_hub
- save_to_disk
- load_from_disk
- from_csv
- from_json
- from_parquet
- from_text
<a id='package_reference_features'></a>
## IterableDataset
The base class [`IterableDataset`] implements an iterable Dataset backed by python generators.
[[autodoc]] datasets.IterableDataset
- from_generator
- remove_columns
- select_columns
- cast_column
- cast
- decode
- __iter__
- iter
- map
- rename_column
- filter
- shuffle
- batch
- skip
- take
- shard
- repeat
- to_csv
- to_pandas
- to_dict
- to_json
- to_parquet
- to_sql
- push_to_hub
- load_state_dict
- state_dict
- info
- split
- builder_name
- citation
- config_name
- dataset_size
- description
- download_checksums
- download_size
- features
- homepage
- license
- size_in_bytes
- supervised_keys
- version
[[autodoc]] datasets.IterableColumn
## IterableDatasetDict
Dictionary with split names as keys ('train', 'test' for example), and `IterableDataset` objects as values.
[[autodoc]] datasets.IterableDatasetDict
- map
- filter
- shuffle
- with_format
- cast
- cast_column
- remove_columns
- rename_column
- rename_columns
- select_columns
- push_to_hub
## Features
[[autodoc]] datasets.Features
### Scalar
[[autodoc]] datasets.Value
[[autodoc]] datasets.ClassLabel
### Composite
[[autodoc]] datasets.LargeList
[[autodoc]] datasets.List
[[autodoc]] datasets.Sequence
### Translation
[[autodoc]] datasets.Translation
[[autodoc]] datasets.TranslationVariableLanguages
### Arrays
[[autodoc]] datasets.Array2D
[[autodoc]] datasets.Array3D
[[autodoc]] datasets.Array4D
[[autodoc]] datasets.Array5D
### Audio
[[autodoc]] datasets.Audio
### Image
[[autodoc]] datasets.Image
### Video
[[autodoc]] datasets.Video
### Pdf
[[autodoc]] datasets.Pdf
## Filesystems
[[autodoc]] datasets.filesystems.is_remote_filesystem
## Fingerprint
[[autodoc]] datasets.fingerprint.Hasher
| datasets/docs/source/package_reference/main_classes.mdx/0 | {
"file_path": "datasets/docs/source/package_reference/main_classes.mdx",
"repo_id": "datasets",
"token_count": 2022
} | 104 |
# Use with Pandas
This document is a quick introduction to using `datasets` with Pandas, with a particular focus on how to process
datasets using Pandas functions, and how to convert a dataset to Pandas or from Pandas.
This is particularly useful as it allows fast operations, since `datasets` uses PyArrow under the hood and PyArrow is well integrated with Pandas.
## Dataset format
By default, datasets return regular Python objects: integers, floats, strings, lists, etc.
To get Pandas DataFrames or Series instead, you can set the format of the dataset to `pandas` using [`Dataset.with_format`]:
```py
>>> from datasets import Dataset
>>> data = {"col_0": ["a", "b", "c", "d"], "col_1": [0., 0., 1., 1.]}
>>> ds = Dataset.from_dict(data)
>>> ds = ds.with_format("pandas")
>>> ds[0] # pd.DataFrame
col_0 col_1
0 a 0.0
>>> ds[:2] # pd.DataFrame
col_0 col_1
0 a 0.0
1 b 0.0
>>> ds["data"] # pd.Series
0 a
1 b
2 c
3 d
Name: col_0, dtype: object
```
This also works for `IterableDataset` objects obtained e.g. using `load_dataset(..., streaming=True)`:
```py
>>> ds = ds.with_format("pandas")
>>> for df in ds.iter(batch_size=2):
... print(df)
... break
col_0 col_1
0 a 0.0
1 b 0.0
```
## Process data
Pandas functions are generally faster than regular hand-written python functions, and therefore they are a good option to optimize data processing. You can use Pandas functions to process a dataset in [`Dataset.map`] or [`Dataset.filter`]:
```python
>>> from datasets import Dataset
>>> data = {"col_0": ["a", "b", "c", "d"], "col_1": [0., 0., 1., 1.]}
>>> ds = Dataset.from_dict(data)
>>> ds = ds.with_format("pandas")
>>> ds = ds.map(lambda df: df.assign(col_2=df.col_1 + 1), batched=True)
>>> ds[:2]
col_0 col_1 col_2
0 a 0.0 1.0
1 b 0.0 1.0
>>> ds = ds.filter(lambda df: df.col_0 == "b", batched=True)
>>> ds[0]
col_0 col_1 col_2
0 b 0.0 1.0
```
We use `batched=True` because it is faster to process batches of data in Pandas rather than row by row. It's also possible to use `batch_size=` in `map()` to set the size of each `df`.
This also works for [`IterableDataset.map`] and [`IterableDataset.filter`].
## Import or Export from Pandas
To import data from Pandas, you can use [`Dataset.from_pandas`]:
```python
ds = Dataset.from_pandas(df)
```
And you can use [`Dataset.to_pandas`] to export a Dataset to a Pandas DataFrame:
```python
df = Dataset.to_pandas()
```
| datasets/docs/source/use_with_pandas.mdx/0 | {
"file_path": "datasets/docs/source/use_with_pandas.mdx",
"repo_id": "datasets",
"token_count": 1010
} | 105 |
from typing import Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal
logger = logging.get_logger(__name__)
DatasetType = TypeVar("DatasetType", Dataset, IterableDataset)
def interleave_datasets(
datasets: list[DatasetType],
probabilities: Optional[list[float]] = None,
seed: Optional[int] = None,
info: Optional[DatasetInfo] = None,
split: Optional[NamedSplit] = None,
stopping_strategy: Literal["first_exhausted", "all_exhausted"] = "first_exhausted",
) -> DatasetType:
"""
Interleave several datasets (sources) into a single dataset.
The new dataset is constructed by alternating between the sources to get the examples.
You can use this function on a list of [`Dataset`] objects, or on a list of [`IterableDataset`] objects.
- If `probabilities` is `None` (default) the new dataset is constructed by cycling between each source to get the examples.
- If `probabilities` is not `None`, the new dataset is constructed by getting examples from a random source at a time according to the provided probabilities.
The resulting dataset ends when one of the source datasets runs out of examples except when `oversampling` is `True`,
in which case, the resulting dataset ends when all datasets have ran out of examples at least one time.
Note for iterable datasets:
In a distributed setup or in PyTorch DataLoader workers, the stopping strategy is applied per process.
Therefore the "first_exhausted" strategy on an sharded iterable dataset can generate less samples in total (up to 1 missing sample per subdataset per worker).
Args:
datasets (`List[Dataset]` or `List[IterableDataset]`):
List of datasets to interleave.
probabilities (`List[float]`, *optional*, defaults to `None`):
If specified, the new dataset is constructed by sampling
examples from one source at a time according to these probabilities.
seed (`int`, *optional*, defaults to `None`):
The random seed used to choose a source for each example.
info ([`DatasetInfo`], *optional*):
Dataset information, like description, citation, etc.
<Added version="2.4.0"/>
split ([`NamedSplit`], *optional*):
Name of the dataset split.
<Added version="2.4.0"/>
stopping_strategy (`str`, defaults to `first_exhausted`):
Two strategies are proposed right now, `first_exhausted` and `all_exhausted`.
By default, `first_exhausted` is an undersampling strategy, i.e the dataset construction is stopped as soon as one dataset has ran out of samples.
If the strategy is `all_exhausted`, we use an oversampling strategy, i.e the dataset construction is stopped as soon as every samples of every dataset has been added at least once.
Note that if the strategy is `all_exhausted`, the interleaved dataset size can get enormous:
- with no probabilities, the resulting dataset will have `max_length_datasets*nb_dataset` samples.
- with given probabilities, the resulting dataset will have more samples if some datasets have really low probability of visiting.
Returns:
[`Dataset`] or [`IterableDataset`]: Return type depends on the input `datasets`
parameter. `Dataset` if the input is a list of `Dataset`, `IterableDataset` if the input is a list of
`IterableDataset`.
Example:
For regular datasets (map-style):
```python
>>> from datasets import Dataset, interleave_datasets
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22]})
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted")
>>> dataset["a"]
[10, 0, 11, 1, 2, 20, 12, 10, 0, 1, 2, 21, 0, 11, 1, 2, 0, 1, 12, 2, 10, 0, 22]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)
>>> dataset["a"]
[10, 0, 11, 1, 2]
>>> dataset = interleave_datasets([d1, d2, d3])
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> d1 = Dataset.from_dict({"a": [0, 1, 2]})
>>> d2 = Dataset.from_dict({"a": [10, 11, 12, 13]})
>>> d3 = Dataset.from_dict({"a": [20, 21, 22, 23, 24]})
>>> dataset = interleave_datasets([d1, d2, d3])
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22]
>>> dataset = interleave_datasets([d1, d2, d3], stopping_strategy="all_exhausted")
>>> dataset["a"]
[0, 10, 20, 1, 11, 21, 2, 12, 22, 0, 13, 23, 1, 10, 24]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42)
>>> dataset["a"]
[10, 0, 11, 1, 2]
>>> dataset = interleave_datasets([d1, d2, d3], probabilities=[0.7, 0.2, 0.1], seed=42, stopping_strategy="all_exhausted")
>>> dataset["a"]
[10, 0, 11, 1, 2, 20, 12, 13, ..., 0, 1, 2, 0, 24]
For datasets in streaming mode (iterable):
>>> from datasets import interleave_datasets
>>> d1 = load_dataset('allenai/c4', 'es', split='train', streaming=True)
>>> d2 = load_dataset('allenai/c4', 'fr', split='train', streaming=True)
>>> dataset = interleave_datasets([d1, d2])
>>> iterator = iter(dataset)
>>> next(iterator)
{'text': 'Comprar Zapatillas para niña en chancla con goma por...'}
>>> next(iterator)
{'text': 'Le sacre de philippe ier, 23 mai 1059 - Compte Rendu...'
```
"""
from .arrow_dataset import Dataset
from .iterable_dataset import IterableDataset
if not datasets:
raise ValueError("Unable to interleave an empty list of datasets.")
for i, dataset in enumerate(datasets):
if not isinstance(dataset, (Dataset, IterableDataset)):
if isinstance(dataset, (DatasetDict, IterableDatasetDict)):
if not dataset:
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
"is an empty dataset dictionary."
)
raise ValueError(
f"Dataset at position {i} has at least one split: {list(dataset)}\n"
f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(dataset))}']"
)
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(dataset).__name__}."
)
if i == 0:
dataset_type, other_type = (
(Dataset, IterableDataset) if isinstance(dataset, Dataset) else (IterableDataset, Dataset)
)
elif not isinstance(dataset, dataset_type):
raise ValueError(
f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."
)
if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
raise ValueError(f"{stopping_strategy} is not supported. Please enter a valid stopping_strategy.")
if dataset_type is Dataset:
return _interleave_map_style_datasets(
datasets, probabilities, seed, info=info, split=split, stopping_strategy=stopping_strategy
)
else:
return _interleave_iterable_datasets(
datasets, probabilities, seed, info=info, split=split, stopping_strategy=stopping_strategy
)
def concatenate_datasets(
dsets: list[DatasetType],
info: Optional[DatasetInfo] = None,
split: Optional[NamedSplit] = None,
axis: int = 0,
) -> DatasetType:
"""
Converts a list of [`Dataset`] with the same schema into a single [`Dataset`].
Args:
dsets (`List[datasets.Dataset]`):
List of Datasets to concatenate.
info (`DatasetInfo`, *optional*):
Dataset information, like description, citation, etc.
split (`NamedSplit`, *optional*):
Name of the dataset split.
axis (`{0, 1}`, defaults to `0`):
Axis to concatenate over, where `0` means over rows (vertically) and `1` means over columns
(horizontally).
<Added version="1.6.0"/>
Example:
```py
>>> ds3 = concatenate_datasets([ds1, ds2])
```
"""
if not dsets:
raise ValueError("Unable to concatenate an empty list of datasets.")
for i, dataset in enumerate(dsets):
if not isinstance(dataset, (Dataset, IterableDataset)):
if isinstance(dataset, (DatasetDict, IterableDatasetDict)):
if not dataset:
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} "
"is an empty dataset dictionary."
)
raise ValueError(
f"Dataset at position {i} has at least one split: {list(dataset)}\n"
f"Please pick one to interleave with the other datasets, for example: dataset['{next(iter(dataset))}']"
)
raise ValueError(
f"Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(dataset).__name__}."
)
if i == 0:
dataset_type, other_type = (
(Dataset, IterableDataset) if isinstance(dataset, Dataset) else (IterableDataset, Dataset)
)
elif not isinstance(dataset, dataset_type):
raise ValueError(
f"Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."
)
if dataset_type is Dataset:
return _concatenate_map_style_datasets(dsets, info=info, split=split, axis=axis)
else:
return _concatenate_iterable_datasets(dsets, info=info, split=split, axis=axis)
| datasets/src/datasets/combine.py/0 | {
"file_path": "datasets/src/datasets/combine.py",
"repo_id": "datasets",
"token_count": 4602
} | 106 |
import numpy as np
from torchcodec.decoders import AudioDecoder as _AudioDecoder
class AudioDecoder(_AudioDecoder):
def __getitem__(self, key: str):
if key == "array":
y = self.get_all_samples().data.cpu().numpy()
return np.mean(y, axis=tuple(range(y.ndim - 1))) if y.ndim > 1 else y
elif key == "sampling_rate":
return self.get_samples_played_in_range(0, 0).sample_rate
elif hasattr(super(), "__getitem__"):
return super().__getitem__(key)
else:
raise TypeError("'torchcodec.decoders.AudioDecoder' object is not subscriptable")
| datasets/src/datasets/features/_torchcodec.py/0 | {
"file_path": "datasets/src/datasets/features/_torchcodec.py",
"repo_id": "datasets",
"token_count": 278
} | 107 |
# Copyright 2020 The HuggingFace Authors.
#
# 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.
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class TorchFormatter(TensorFormatter[Mapping, "torch.Tensor", Mapping]):
def __init__(self, features=None, token_per_repo_id=None, **torch_tensor_kwargs):
super().__init__(features=features, token_per_repo_id=token_per_repo_id)
self.torch_tensor_kwargs = torch_tensor_kwargs
import torch # noqa import torch at initialization
def _consolidate(self, column):
import torch
if isinstance(column, list) and column:
if all(
isinstance(x, torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column
):
return torch.stack(column)
return column
def _tensorize(self, value):
import torch
if isinstance(value, (str, bytes, type(None))):
return value
elif isinstance(value, (np.character, np.ndarray)) and np.issubdtype(value.dtype, np.character):
return value.tolist()
default_dtype = {}
if isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.integer):
default_dtype = {"dtype": torch.int64}
# Convert dtype to np.int64 if it's either np.uint16 or np.uint32 to ensure compatibility.
# np.uint64 is excluded from this conversion as there is no compatible PyTorch dtype that can handle it without loss.
if value.dtype in [np.uint16, np.uint32]:
value = value.astype(np.int64)
elif isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.floating):
default_dtype = {"dtype": torch.float32}
if config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(value, PIL.Image.Image):
value = np.asarray(value)
if value.ndim == 2:
value = value[:, :, np.newaxis]
value = value.transpose((2, 0, 1))
if config.TORCHVISION_AVAILABLE and "torchvision" in sys.modules:
from torchvision.io import VideoReader
if isinstance(value, VideoReader):
return value # TODO(QL): set output to torch tensors ?
if config.TORCHCODEC_AVAILABLE and "torchcodec" in sys.modules:
from torchcodec.decoders import AudioDecoder, VideoDecoder
if isinstance(value, (VideoDecoder, AudioDecoder)):
return value # TODO(QL): set output to jax arrays ?
return torch.tensor(value, **{**default_dtype, **self.torch_tensor_kwargs})
def _recursive_tensorize(self, data_struct):
import torch
# support for torch, tf, jax etc.
if hasattr(data_struct, "__array__") and not isinstance(data_struct, torch.Tensor):
data_struct = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(data_struct, np.ndarray):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
elif isinstance(data_struct, (list, tuple)):
return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])
return self._tensorize(data_struct)
def recursive_tensorize(self, data_struct: dict):
return map_nested(self._recursive_tensorize, data_struct, map_list=False)
def format_row(self, pa_table: pa.Table) -> Mapping:
row = self.numpy_arrow_extractor().extract_row(pa_table)
row = self.python_features_decoder.decode_row(row)
return self.recursive_tensorize(row)
def format_column(self, pa_table: pa.Table) -> "torch.Tensor":
column = self.numpy_arrow_extractor().extract_column(pa_table)
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
column = self.recursive_tensorize(column)
column = self._consolidate(column)
return column
def format_batch(self, pa_table: pa.Table) -> Mapping:
batch = self.numpy_arrow_extractor().extract_batch(pa_table)
batch = self.python_features_decoder.decode_batch(batch)
batch = self.recursive_tensorize(batch)
for column_name in batch:
batch[column_name] = self._consolidate(batch[column_name])
return batch
| datasets/src/datasets/formatting/torch_formatter.py/0 | {
"file_path": "datasets/src/datasets/formatting/torch_formatter.py",
"repo_id": "datasets",
"token_count": 2151
} | 108 |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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.
# Lint as: python3
"""Utilities for file names."""
import itertools
import os
import re
_uppercase_uppercase_re = re.compile(r"([A-Z]+)([A-Z][a-z])")
_lowercase_uppercase_re = re.compile(r"([a-z\d])([A-Z])")
_single_underscore_re = re.compile(r"(?<!_)_(?!_)")
_multiple_underscores_re = re.compile(r"(_{2,})")
_split_re = r"^\w+(\.\w+)*$"
INVALID_WINDOWS_CHARACTERS_IN_PATH = r"<>:/\|?*"
def camelcase_to_snakecase(name):
"""Convert camel-case string to snake-case."""
name = _uppercase_uppercase_re.sub(r"\1_\2", name)
name = _lowercase_uppercase_re.sub(r"\1_\2", name)
return name.lower()
def snakecase_to_camelcase(name):
"""Convert snake-case string to camel-case string."""
name = _single_underscore_re.split(name)
name = [_multiple_underscores_re.split(n) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(name) if n != "")
def filename_prefix_for_name(name):
if os.path.basename(name) != name:
raise ValueError(f"Should be a dataset name, not a path: {name}")
return camelcase_to_snakecase(name)
def filename_prefix_for_split(name, split):
if os.path.basename(name) != name:
raise ValueError(f"Should be a dataset name, not a path: {name}")
if not re.match(_split_re, split):
raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'.")
return f"{filename_prefix_for_name(name)}-{split}"
def filepattern_for_dataset_split(dataset_name, split, data_dir, filetype_suffix=None):
prefix = filename_prefix_for_split(dataset_name, split)
if filetype_suffix:
prefix += f".{filetype_suffix}"
filepath = os.path.join(data_dir, prefix)
return f"{filepath}*"
def filenames_for_dataset_split(path, dataset_name, split, filetype_suffix=None, shard_lengths=None):
prefix = filename_prefix_for_split(dataset_name, split)
prefix = os.path.join(path, prefix)
if shard_lengths:
num_shards = len(shard_lengths)
filenames = [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(num_shards)]
if filetype_suffix:
filenames = [filename + f".{filetype_suffix}" for filename in filenames]
return filenames
else:
filename = prefix
if filetype_suffix:
filename += f".{filetype_suffix}"
return [filename]
| datasets/src/datasets/naming.py/0 | {
"file_path": "datasets/src/datasets/naming.py",
"repo_id": "datasets",
"token_count": 1179
} | 109 |
#!/usr/bin/env python
# Copyright 2023 The HuggingFace Inc. team. 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
"""Utilities to handle file locking in `datasets`."""
import os
from filelock import FileLock as FileLock_
from filelock import UnixFileLock
from filelock import __version__ as _filelock_version
from packaging import version
class FileLock(FileLock_):
"""
A `filelock.FileLock` initializer that handles long paths.
It also uses the current umask for lock files.
"""
MAX_FILENAME_LENGTH = 255
def __init__(self, lock_file, *args, **kwargs):
# The "mode" argument is required if we want to use the current umask in filelock >= 3.10
# In previous previous it was already using the current umask.
if "mode" not in kwargs and version.parse(_filelock_version) >= version.parse("3.10.0"):
umask = os.umask(0o666)
os.umask(umask)
kwargs["mode"] = 0o666 & ~umask
lock_file = self.hash_filename_if_too_long(lock_file)
super().__init__(lock_file, *args, **kwargs)
@classmethod
def hash_filename_if_too_long(cls, path: str) -> str:
path = os.path.abspath(os.path.expanduser(path))
filename = os.path.basename(path)
max_filename_length = cls.MAX_FILENAME_LENGTH
if issubclass(cls, UnixFileLock):
max_filename_length = min(max_filename_length, os.statvfs(os.path.dirname(path)).f_namemax)
if len(filename) > max_filename_length:
dirname = os.path.dirname(path)
hashed_filename = str(hash(filename))
new_filename = (
filename[: max_filename_length - len(hashed_filename) - 8] + "..." + hashed_filename + ".lock"
)
return os.path.join(dirname, new_filename)
else:
return path
| datasets/src/datasets/utils/_filelock.py/0 | {
"file_path": "datasets/src/datasets/utils/_filelock.py",
"repo_id": "datasets",
"token_count": 896
} | 110 |
{
"monolingual": "contains a single language",
"multilingual": "contains multiple languages",
"translation": "contains translated or aligned text",
"other": "other type of language distribution"
}
| datasets/src/datasets/utils/resources/multilingualities.json/0 | {
"file_path": "datasets/src/datasets/utils/resources/multilingualities.json",
"repo_id": "datasets",
"token_count": 55
} | 111 |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, List, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_TestCommandArgs = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",
"cache_dir",
"data_dir",
"all_configs",
"save_infos",
"ignore_verifications",
"force_redownload",
"clear_cache",
"num_proc",
],
defaults=[None, None, None, False, False, False, False, False, None],
)
def is_1percent_close(source, target):
return (abs(source - target) / target) < 0.01
@pytest.mark.integration
def test_test_command(dataset_dir):
args = _TestCommandArgs(dataset=dataset_dir, all_configs=True, save_infos=True)
test_command = TestCommand(*args)
test_command.run()
dataset_readme_path = os.path.join(dataset_dir, "README.md")
assert os.path.exists(dataset_readme_path)
dataset_infos = DatasetInfosDict.from_directory(dataset_dir)
expected_dataset_infos = DatasetInfosDict(
{
"default": DatasetInfo(
features=Features(
{
"tokens": List(Value("string")),
"ner_tags": List(
ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"])
),
"langs": List(Value("string")),
"spans": List(Value("string")),
}
),
splits=[
{
"name": "train",
"num_bytes": 2351563,
"num_examples": 10000,
},
{
"name": "validation",
"num_bytes": 238418,
"num_examples": 1000,
},
],
download_size=3940680,
dataset_size=2589981,
)
}
)
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
result, expected = getattr(dataset_infos["default"], key), getattr(expected_dataset_infos["default"], key)
if key == "num_bytes":
assert is_1percent_close(result, expected)
elif key == "splits":
assert list(result) == list(expected)
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_1percent_close(result[split].num_bytes, expected[split].num_bytes)
else:
result == expected
| datasets/tests/commands/test_test.py/0 | {
"file_path": "datasets/tests/commands/test_test.py",
"repo_id": "datasets",
"token_count": 1495
} | 112 |
import os
import shutil
import tarfile
import warnings
from io import BytesIO
import numpy as np
import pandas as pd
import pyarrow as pa
import pytest
from datasets import Column, Dataset, Features, Image, List, Value, concatenate_datasets, load_dataset
from datasets.features.image import encode_np_array, image_to_bytes
from ..utils import require_pil
@pytest.fixture
def tar_jpg_path(shared_datadir, tmp_path_factory):
image_path = str(shared_datadir / "test_image_rgb.jpg")
path = tmp_path_factory.mktemp("data") / "image_data.jpg.tar"
with tarfile.TarFile(path, "w") as f:
f.add(image_path, arcname=os.path.basename(image_path))
return path
def iter_archive(archive_path):
with tarfile.open(archive_path) as tar:
for tarinfo in tar:
file_path = tarinfo.name
file_obj = tar.extractfile(tarinfo)
yield file_path, file_obj
def test_image_instantiation():
image = Image()
assert image.id is None
assert image.dtype == "PIL.Image.Image"
assert image.pa_type == pa.struct({"bytes": pa.binary(), "path": pa.string()})
assert image._type == "Image"
def test_image_feature_type_to_arrow():
features = Features({"image": Image()})
assert features.arrow_schema == pa.schema({"image": Image().pa_type})
features = Features({"struct_containing_an_image": {"image": Image()}})
assert features.arrow_schema == pa.schema({"struct_containing_an_image": pa.struct({"image": Image().pa_type})})
features = Features({"sequence_of_images": List(Image())})
assert features.arrow_schema == pa.schema({"sequence_of_images": pa.list_(Image().pa_type)})
@require_pil
@pytest.mark.parametrize(
"build_example",
[
lambda image_path: image_path,
lambda image_path: open(image_path, "rb").read(),
lambda image_path: {"path": image_path},
lambda image_path: {"path": image_path, "bytes": None},
lambda image_path: {"path": image_path, "bytes": open(image_path, "rb").read()},
lambda image_path: {"path": None, "bytes": open(image_path, "rb").read()},
lambda image_path: {"bytes": open(image_path, "rb").read()},
],
)
def test_image_feature_encode_example(shared_datadir, build_example):
import PIL.Image
image_path = str(shared_datadir / "test_image_rgb.jpg")
image = Image()
encoded_example = image.encode_example(build_example(image_path))
assert isinstance(encoded_example, dict)
assert encoded_example.keys() == {"bytes", "path"}
assert encoded_example["bytes"] is not None or encoded_example["path"] is not None
decoded_example = image.decode_example(encoded_example)
assert isinstance(decoded_example, PIL.Image.Image)
@require_pil
def test_image_decode_example(shared_datadir):
import PIL.Image
image_path = str(shared_datadir / "test_image_rgb.jpg")
image = Image()
decoded_example = image.decode_example({"path": image_path, "bytes": None})
assert isinstance(decoded_example, PIL.Image.Image)
assert os.path.samefile(decoded_example.filename, image_path)
assert decoded_example.size == (640, 480)
assert decoded_example.mode == "RGB"
with pytest.raises(RuntimeError):
Image(decode=False).decode_example(image_path)
@require_pil
def test_image_decode_example_with_exif_orientation_tag(shared_datadir):
import PIL.Image
image_path = str(shared_datadir / "test_image_rgb.jpg")
buffer = BytesIO()
exif = PIL.Image.Exif()
exif[PIL.Image.ExifTags.Base.Orientation] = 8 # rotate the image for 90°
PIL.Image.open(image_path).save(buffer, format="JPEG", exif=exif.tobytes())
image = Image()
decoded_example = image.decode_example({"path": None, "bytes": buffer.getvalue()})
assert isinstance(decoded_example, PIL.Image.Image)
assert decoded_example.size == (480, 640) # rotated
assert decoded_example.mode == "RGB"
@require_pil
def test_image_change_mode(shared_datadir):
import PIL.Image
image_path = str(shared_datadir / "test_image_rgb.jpg")
image = Image(mode="YCbCr")
decoded_example = image.decode_example({"path": image_path, "bytes": None})
assert isinstance(decoded_example, PIL.Image.Image)
assert not hasattr(decoded_example, "filename") # changing the mode drops the filename
assert decoded_example.size == (640, 480)
assert decoded_example.mode == "YCbCr"
@require_pil
def test_dataset_with_image_feature(shared_datadir):
import PIL.Image
image_path = str(shared_datadir / "test_image_rgb.jpg")
data = {"image": [image_path]}
features = Features({"image": Image()})
dset = Dataset.from_dict(data, features=features)
item = dset[0]
assert item.keys() == {"image"}
assert isinstance(item["image"], PIL.Image.Image)
assert os.path.samefile(item["image"].filename, image_path)
assert item["image"].format == "JPEG"
assert item["image"].size == (640, 480)
assert item["image"].mode == "RGB"
batch = dset[:1]
assert len(batch) == 1
assert batch.keys() == {"image"}
assert isinstance(batch["image"], list) and all(isinstance(item, PIL.Image.Image) for item in batch["image"])
assert os.path.samefile(batch["image"][0].filename, image_path)
assert batch["image"][0].format == "JPEG"
assert batch["image"][0].size == (640, 480)
assert batch["image"][0].mode == "RGB"
column = dset["image"]
assert len(column) == 1
assert isinstance(column, Column) and all(isinstance(item, PIL.Image.Image) for item in column)
assert os.path.samefile(column[0].filename, image_path)
assert column[0].format == "JPEG"
assert column[0].size == (640, 480)
assert column[0].mode == "RGB"
@require_pil
@pytest.mark.parametrize("infer_feature", [False, True])
def test_dataset_with_image_feature_from_pil_image(infer_feature, shared_datadir):
import PIL.Image
image_path = str(shared_datadir / "test_image_rgb.jpg")
data = {"image": [PIL.Image.open(image_path)]}
features = Features({"image": Image()}) if not infer_feature else None
dset = Dataset.from_dict(data, features=features)
item = dset[0]
assert item.keys() == {"image"}
assert isinstance(item["image"], PIL.Image.Image)
assert os.path.samefile(item["image"].filename, image_path)
assert item["image"].format == "JPEG"
assert item["image"].size == (640, 480)
assert item["image"].mode == "RGB"
batch = dset[:1]
assert len(batch) == 1
assert batch.keys() == {"image"}
assert isinstance(batch["image"], list) and all(isinstance(item, PIL.Image.Image) for item in batch["image"])
assert os.path.samefile(batch["image"][0].filename, image_path)
assert batch["image"][0].format == "JPEG"
assert batch["image"][0].size == (640, 480)
assert batch["image"][0].mode == "RGB"
column = dset["image"]
assert len(column) == 1
assert isinstance(column, Column) and all(isinstance(item, PIL.Image.Image) for item in column)
assert os.path.samefile(column[0].filename, image_path)
assert column[0].format == "JPEG"
assert column[0].size == (640, 480)
assert column[0].mode == "RGB"
@require_pil
def test_dataset_with_image_feature_from_np_array():
import PIL.Image
image_array = np.arange(640 * 480, dtype=np.int32).reshape(480, 640)
data = {"image": [image_array]}
features = Features({"image": Image()})
dset = Dataset.from_dict(data, features=features)
item = dset[0]
assert item.keys() == {"image"}
assert isinstance(item["image"], PIL.Image.Image)
np.testing.assert_array_equal(np.array(item["image"]), image_array)
assert item["image"].filename == ""
assert item["image"].format in ["PNG", "TIFF"]
assert item["image"].size == (640, 480)
batch = dset[:1]
assert len(batch) == 1
assert batch.keys() == {"image"}
assert isinstance(batch["image"], list) and all(isinstance(item, PIL.Image.Image) for item in batch["image"])
np.testing.assert_array_equal(np.array(batch["image"][0]), image_array)
assert batch["image"][0].filename == ""
assert batch["image"][0].format in ["PNG", "TIFF"]
assert batch["image"][0].size == (640, 480)
column = dset["image"]
assert len(column) == 1
assert isinstance(column, Column) and all(isinstance(item, PIL.Image.Image) for item in column)
np.testing.assert_array_equal(np.array(column[0]), image_array)
assert column[0].filename == ""
assert column[0].format in ["PNG", "TIFF"]
assert column[0].size == (640, 480)
@require_pil
def test_dataset_with_image_feature_tar_jpg(tar_jpg_path):
import PIL.Image
data = {"image": []}
for file_path, file_obj in iter_archive(tar_jpg_path):
data["image"].append({"path": file_path, "bytes": file_obj.read()})
break
features = Features({"image": Image()})
dset = Dataset.from_dict(data, features=features)
item = dset[0]
assert item.keys() == {"image"}
assert isinstance(item["image"], PIL.Image.Image)
assert item["image"].filename == ""
assert item["image"].format == "JPEG"
assert item["image"].size == (640, 480)
assert item["image"].mode == "RGB"
batch = dset[:1]
assert len(batch) == 1
assert batch.keys() == {"image"}
assert isinstance(batch["image"], list) and all(isinstance(item, PIL.Image.Image) for item in batch["image"])
assert batch["image"][0].filename == ""
assert batch["image"][0].format == "JPEG"
assert batch["image"][0].size == (640, 480)
assert batch["image"][0].mode == "RGB"
column = dset["image"]
assert len(column) == 1
assert isinstance(column, Column) and all(isinstance(item, PIL.Image.Image) for item in column)
assert column[0].filename == ""
assert column[0].format == "JPEG"
assert column[0].size == (640, 480)
assert column[0].mode == "RGB"
@require_pil
def test_dataset_with_image_feature_with_none():
data = {"image": [None]}
features = Features({"image": Image()})
dset = Dataset.from_dict(data, features=features)
item = dset[0]
assert item.keys() == {"image"}
assert item["image"] is None
batch = dset[:1]
assert len(batch) == 1
assert batch.keys() == {"image"}
assert isinstance(batch["image"], list) and all(item is None for item in batch["image"])
column = dset["image"]
assert len(column) == 1
assert isinstance(column, Column) and all(item is None for item in column)
# nested tests
data = {"images": [[None]]}
features = Features({"images": List(Image())})
dset = Dataset.from_dict(data, features=features)
item = dset[0]
assert item.keys() == {"images"}
assert all(i is None for i in item["images"])
data = {"nested": [{"image": None}]}
features = Features({"nested": {"image": Image()}})
dset = Dataset.from_dict(data, features=features)
item = dset[0]
assert item.keys() == {"nested"}
assert item["nested"].keys() == {"image"}
assert item["nested"]["image"] is None
@require_pil
@pytest.mark.parametrize(
"build_data",
[
lambda image_path: {"image": [image_path]},
lambda image_path: {"image": [open(image_path, "rb").read()]},
lambda image_path: {"image": [{"path": image_path}]},
lambda image_path: {"image": [{"path": image_path, "bytes": None}]},
lambda image_path: {"image": [{"path": image_path, "bytes": open(image_path, "rb").read()}]},
lambda image_path: {"image": [{"path": None, "bytes": open(image_path, "rb").read()}]},
lambda image_path: {"image": [{"bytes": open(image_path, "rb").read()}]},
],
)
def test_dataset_cast_to_image_features(shared_datadir, build_data):
import PIL.Image
image_path = str(shared_datadir / "test_image_rgb.jpg")
data = build_data(image_path)
dset = Dataset.from_dict(data)
item = dset.cast(Features({"image": Image()}))[0]
assert item.keys() == {"image"}
assert isinstance(item["image"], PIL.Image.Image)
item = dset.cast_column("image", Image())[0]
assert item.keys() == {"image"}
assert isinstance(item["image"], PIL.Image.Image)
@require_pil
def test_dataset_concatenate_image_features(shared_datadir):
# we use a different data structure between 1 and 2 to make sure they are compatible with each other
image_path = str(shared_datadir / "test_image_rgb.jpg")
data1 = {"image": [image_path]}
dset1 = Dataset.from_dict(data1, features=Features({"image": Image()}))
data2 = {"image": [{"bytes": open(image_path, "rb").read()}]}
dset2 = Dataset.from_dict(data2, features=Features({"image": Image()}))
concatenated_dataset = concatenate_datasets([dset1, dset2])
assert len(concatenated_dataset) == len(dset1) + len(dset2)
assert concatenated_dataset[0]["image"] == dset1[0]["image"]
assert concatenated_dataset[1]["image"] == dset2[0]["image"]
@require_pil
def test_dataset_concatenate_nested_image_features(shared_datadir):
# we use a different data structure between 1 and 2 to make sure they are compatible with each other
image_path = str(shared_datadir / "test_image_rgb.jpg")
features = Features({"list_of_structs_of_images": List({"image": Image()})})
data1 = {"list_of_structs_of_images": [[{"image": image_path}]]}
dset1 = Dataset.from_dict(data1, features=features)
data2 = {"list_of_structs_of_images": [[{"image": {"bytes": open(image_path, "rb").read()}}]]}
dset2 = Dataset.from_dict(data2, features=features)
concatenated_dataset = concatenate_datasets([dset1, dset2])
assert len(concatenated_dataset) == len(dset1) + len(dset2)
assert (
concatenated_dataset[0]["list_of_structs_of_images"][0]["image"]
== dset1[0]["list_of_structs_of_images"][0]["image"]
)
assert (
concatenated_dataset[1]["list_of_structs_of_images"][0]["image"]
== dset2[0]["list_of_structs_of_images"][0]["image"]
)
@require_pil
def test_dataset_with_image_feature_map(shared_datadir):
image_path = str(shared_datadir / "test_image_rgb.jpg")
data = {"image": [image_path], "caption": ["cats sleeping"]}
features = Features({"image": Image(), "caption": Value("string")})
dset = Dataset.from_dict(data, features=features)
for item in dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image", "caption"}
assert item == {"image": {"path": image_path, "bytes": None}, "caption": "cats sleeping"}
# no decoding
def process_caption(example):
example["caption"] = "Two " + example["caption"]
return example
processed_dset = dset.map(process_caption)
for item in processed_dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image", "caption"}
assert item == {"image": {"path": image_path, "bytes": None}, "caption": "Two cats sleeping"}
# decoding example
def process_image_by_example(example):
example["mode"] = example["image"].mode
return example
decoded_dset = dset.map(process_image_by_example)
for item in decoded_dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image", "caption", "mode"}
assert os.path.samefile(item["image"]["path"], image_path)
assert item["caption"] == "cats sleeping"
assert item["mode"] == "RGB"
# decoding batch
def process_image_by_batch(batch):
batch["mode"] = [image.mode for image in batch["image"]]
return batch
decoded_dset = dset.map(process_image_by_batch, batched=True)
for item in decoded_dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image", "caption", "mode"}
assert os.path.samefile(item["image"]["path"], image_path)
assert item["caption"] == "cats sleeping"
assert item["mode"] == "RGB"
@require_pil
def test_formatted_dataset_with_image_feature_map(shared_datadir):
image_path = str(shared_datadir / "test_image_rgb.jpg")
pil_image = Image().decode_example({"path": image_path, "bytes": None})
data = {"image": [image_path], "caption": ["cats sleeping"]}
features = Features({"image": Image(), "caption": Value("string")})
dset = Dataset.from_dict(data, features=features)
for item in dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image", "caption"}
assert item == {"image": {"path": image_path, "bytes": None}, "caption": "cats sleeping"}
def process_image_by_example(example):
example["num_channels"] = example["image"].shape[-1]
return example
decoded_dset = dset.with_format("numpy").map(process_image_by_example)
for item in decoded_dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image", "caption", "num_channels"}
assert item["image"] == encode_np_array(np.array(pil_image))
assert item["caption"] == "cats sleeping"
assert item["num_channels"] == 3
def process_image_by_batch(batch):
batch["num_channels"] = [image.shape[-1] for image in batch["image"]]
return batch
decoded_dset = dset.with_format("numpy").map(process_image_by_batch, batched=True)
for item in decoded_dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image", "caption", "num_channels"}
assert item["image"] == encode_np_array(np.array(pil_image))
assert item["caption"] == "cats sleeping"
assert item["num_channels"] == 3
@require_pil
def test_dataset_with_image_feature_map_change_image(shared_datadir):
import PIL.Image
image_path = str(shared_datadir / "test_image_rgb.jpg")
pil_image = Image().decode_example({"path": image_path, "bytes": None})
data = {"image": [image_path]}
features = Features({"image": Image()})
dset = Dataset.from_dict(data, features=features)
for item in dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image"}
assert item == {
"image": {
"bytes": None,
"path": image_path,
}
}
# return pil image
def process_image_resize_by_example(example):
example["image"] = example["image"].resize((100, 100))
return example
decoded_dset = dset.map(process_image_resize_by_example)
for item in decoded_dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image"}
assert item == {"image": {"bytes": image_to_bytes(pil_image.resize((100, 100))), "path": None}}
def process_image_resize_by_batch(batch):
batch["image"] = [image.resize((100, 100)) for image in batch["image"]]
return batch
decoded_dset = dset.map(process_image_resize_by_batch, batched=True)
for item in decoded_dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image"}
assert item == {"image": {"bytes": image_to_bytes(pil_image.resize((100, 100))), "path": None}}
# return np.ndarray (e.g. when using albumentations)
def process_image_resize_by_example_return_np_array(example):
example["image"] = np.array(example["image"].resize((100, 100)))
return example
decoded_dset = dset.map(process_image_resize_by_example_return_np_array)
for item in decoded_dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image"}
assert item == {
"image": {
"bytes": image_to_bytes(PIL.Image.fromarray(np.array(pil_image.resize((100, 100))))),
"path": None,
}
}
def process_image_resize_by_batch_return_np_array(batch):
batch["image"] = [np.array(image.resize((100, 100))) for image in batch["image"]]
return batch
decoded_dset = dset.map(process_image_resize_by_batch_return_np_array, batched=True)
for item in decoded_dset.cast_column("image", Image(decode=False)):
assert item.keys() == {"image"}
assert item == {
"image": {
"bytes": image_to_bytes(PIL.Image.fromarray(np.array(pil_image.resize((100, 100))))),
"path": None,
}
}
@require_pil
def test_formatted_dataset_with_image_feature(shared_datadir):
import PIL.Image
image_path = str(shared_datadir / "test_image_rgb.jpg")
data = {"image": [image_path, image_path]}
features = Features({"image": Image()})
dset = Dataset.from_dict(data, features=features)
with dset.formatted_as("numpy"):
item = dset[0]
assert item.keys() == {"image"}
assert isinstance(item["image"], np.ndarray)
assert item["image"].shape == (480, 640, 3)
batch = dset[:1]
assert batch.keys() == {"image"}
assert len(batch) == 1
assert isinstance(batch["image"], np.ndarray)
assert batch["image"].shape == (1, 480, 640, 3)
column = dset["image"]
assert len(column) == 2
assert isinstance(column[:], np.ndarray)
assert column[:].shape == (2, 480, 640, 3)
with dset.formatted_as("pandas"):
item = dset[0]
assert item.shape == (1, 1)
assert item.columns == ["image"]
assert isinstance(item["image"][0], PIL.Image.Image)
assert os.path.samefile(item["image"][0].filename, image_path)
assert item["image"][0].format == "JPEG"
assert item["image"][0].size == (640, 480)
assert item["image"][0].mode == "RGB"
batch = dset[:1]
assert batch.shape == (1, 1)
assert batch.columns == ["image"]
assert isinstance(batch["image"], pd.Series) and all(
isinstance(item, PIL.Image.Image) for item in batch["image"]
)
assert os.path.samefile(batch["image"][0].filename, image_path)
assert batch["image"][0].format == "JPEG"
assert batch["image"][0].size == (640, 480)
assert batch["image"][0].mode == "RGB"
column = dset["image"]
assert len(column) == 2
assert isinstance(column, pd.Series) and all(isinstance(item, PIL.Image.Image) for item in column)
assert os.path.samefile(column[0].filename, image_path)
assert column[0].format == "JPEG"
assert column[0].size == (640, 480)
assert column[0].mode == "RGB"
@pytest.fixture
def img_dataset_dir(shared_datadir, tmp_path):
data_dir = tmp_path / "dummy_img_dataset_data"
data_dir.mkdir()
shutil.copy(str(shared_datadir / "test_image_rgb.jpg"), str(data_dir / "image_rgb.jpg"))
with open(data_dir / "metadata.jsonl", "w") as f:
f.write('{"file_name": "image_rgb.jpg", "caption": "Two cats sleeping"}\n')
return str(data_dir)
@require_pil
@pytest.mark.parametrize("streaming", [False, True])
def test_load_dataset_with_image_feature(shared_datadir, img_dataset_dir, streaming):
import PIL.Image
image_path = os.path.join(img_dataset_dir, "image_rgb.jpg")
dset = load_dataset(img_dataset_dir, split="train", streaming=streaming)
item = dset[0] if not streaming else next(iter(dset))
assert item.keys() == {"image", "caption"}
assert isinstance(item["image"], PIL.Image.Image)
assert os.path.samefile(item["image"].filename, image_path)
assert item["image"].format == "JPEG"
assert item["image"].size == (640, 480)
assert item["image"].mode == "RGB"
@require_pil
def test_dataset_with_image_feature_undecoded(shared_datadir):
image_path = str(shared_datadir / "test_image_rgb.jpg")
data = {"image": [image_path]}
features = Features({"image": Image(decode=False)})
dset = Dataset.from_dict(data, features=features)
item = dset[0]
assert item.keys() == {"image"}
assert item["image"] == {"path": image_path, "bytes": None}
batch = dset[:1]
assert batch.keys() == {"image"}
assert len(batch["image"]) == 1
assert batch["image"][0] == {"path": image_path, "bytes": None}
column = dset["image"]
assert len(column) == 1
assert column[0] == {"path": image_path, "bytes": None}
@require_pil
def test_formatted_dataset_with_image_feature_undecoded(shared_datadir):
image_path = str(shared_datadir / "test_image_rgb.jpg")
data = {"image": [image_path]}
features = Features({"image": Image(decode=False)})
dset = Dataset.from_dict(data, features=features)
with dset.formatted_as("numpy"):
item = dset[0]
assert item.keys() == {"image"}
assert item["image"] == {"path": image_path, "bytes": None}
batch = dset[:1]
assert batch.keys() == {"image"}
assert len(batch["image"]) == 1
assert batch["image"][0] == {"path": image_path, "bytes": None}
column = dset["image"]
assert len(column) == 1
assert column[0] == {"path": image_path, "bytes": None}
with dset.formatted_as("pandas"):
item = dset[0]
assert item.shape == (1, 1)
assert item.columns == ["image"]
assert item["image"][0] == {"path": image_path, "bytes": None}
batch = dset[:1]
assert batch.shape == (1, 1)
assert batch.columns == ["image"]
assert batch["image"][0] == {"path": image_path, "bytes": None}
column = dset["image"]
assert len(column) == 1
assert column[0] == {"path": image_path, "bytes": None}
@require_pil
def test_dataset_with_image_feature_map_undecoded(shared_datadir):
image_path = str(shared_datadir / "test_image_rgb.jpg")
data = {"image": [image_path]}
features = Features({"image": Image(decode=False)})
dset = Dataset.from_dict(data, features=features)
def assert_image_example_undecoded(example):
assert example["image"] == {"path": image_path, "bytes": None}
dset.map(assert_image_example_undecoded)
def assert_image_batch_undecoded(batch):
for image in batch["image"]:
assert image == {"path": image_path, "bytes": None}
dset.map(assert_image_batch_undecoded, batched=True)
@require_pil
def test_image_embed_storage(shared_datadir):
image_path = str(shared_datadir / "test_image_rgb.jpg")
example = {"bytes": None, "path": image_path}
storage = pa.array([example], type=pa.struct({"bytes": pa.binary(), "path": pa.string()}))
embedded_storage = Image().embed_storage(storage)
embedded_example = embedded_storage.to_pylist()[0]
assert embedded_example == {"bytes": open(image_path, "rb").read(), "path": "test_image_rgb.jpg"}
@require_pil
@pytest.mark.parametrize(
"array, dtype_cast, expected_image_format",
[
(np.arange(16).reshape(4, 4).astype(np.uint8), "exact_match", "PNG"),
(np.arange(16).reshape(4, 4).astype(np.uint16), "exact_match", "TIFF"),
(np.arange(16).reshape(4, 4).astype(np.int64), "downcast->|i4", "TIFF"),
(np.arange(16).reshape(4, 4).astype(np.complex128), "error", None),
(np.arange(16).reshape(2, 2, 4).astype(np.uint8), "exact_match", "PNG"),
(np.arange(16).reshape(2, 2, 4), "downcast->|u1", "PNG"),
(np.arange(16).reshape(2, 2, 4).astype(np.float64), "error", None),
],
)
def test_encode_np_array(array, dtype_cast, expected_image_format):
if dtype_cast.startswith("downcast"):
_, dest_dtype = dtype_cast.split("->")
dest_dtype = np.dtype(dest_dtype)
with pytest.warns(UserWarning, match=f"Downcasting array dtype.+{dest_dtype}.+"):
encoded_image = Image().encode_example(array)
elif dtype_cast == "error":
with pytest.raises(TypeError):
Image().encode_example(array)
return
else: # exact_match (no warnings are raised)
with warnings.catch_warnings():
warnings.simplefilter("error")
encoded_image = Image().encode_example(array)
assert isinstance(encoded_image, dict)
assert encoded_image.keys() == {"path", "bytes"}
assert encoded_image["path"] is None
assert encoded_image["bytes"] is not None and isinstance(encoded_image["bytes"], bytes)
decoded_image = Image().decode_example(encoded_image)
assert decoded_image.format == expected_image_format
np.testing.assert_array_equal(np.array(decoded_image), array)
| datasets/tests/features/test_image.py/0 | {
"file_path": "datasets/tests/features/test_image.py",
"repo_id": "datasets",
"token_count": 11308
} | 113 |
import os
import tempfile
from pathlib import Path
from unittest import TestCase
import pyarrow as pa
import pytest
from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo
from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
class ReaderTest(BaseReader):
"""
Build a Dataset object out of Instruction instance(s).
This reader is made for testing. It mocks file reads.
"""
def _get_table_from_filename(self, filename_skip_take, in_memory=False):
"""Returns a Dataset instance from given (filename, skip, take)."""
filename, skip, take = (
filename_skip_take["filename"],
filename_skip_take["skip"] if "skip" in filename_skip_take else None,
filename_skip_take["take"] if "take" in filename_skip_take else None,
)
open(os.path.join(filename), "wb").close()
pa_table = pa.Table.from_pydict({"filename": [Path(filename).name] * 100})
if take == -1:
take = len(pa_table) - skip
if skip is not None and take is not None:
pa_table = pa_table.slice(skip, take)
return pa_table
class BaseReaderTest(TestCase):
def test_read(self):
name = "my_name"
train_info = SplitInfo(name="train", num_examples=100)
test_info = SplitInfo(name="test", num_examples=100)
split_infos = [train_info, test_info]
split_dict = SplitDict()
split_dict.add(train_info)
split_dict.add(test_info)
info = DatasetInfo(splits=split_dict)
with tempfile.TemporaryDirectory() as tmp_dir:
reader = ReaderTest(tmp_dir, info)
instructions = "test[:33%]"
dset = Dataset(**reader.read(name, instructions, split_infos))
self.assertEqual(dset["filename"][0], f"{name}-test")
self.assertEqual(dset.num_rows, 33)
self.assertEqual(dset.num_columns, 1)
instructions1 = ["train", "test[:33%]"]
instructions2 = [Split.TRAIN, ReadInstruction.from_spec("test[:33%]")]
for instructions in [instructions1, instructions2]:
datasets_kwargs = [reader.read(name, instr, split_infos) for instr in instructions]
train_dset, test_dset = (Dataset(**dataset_kwargs) for dataset_kwargs in datasets_kwargs)
self.assertEqual(train_dset["filename"][0], f"{name}-train")
self.assertEqual(train_dset.num_rows, 100)
self.assertEqual(train_dset.num_columns, 1)
self.assertIsInstance(train_dset.split, NamedSplit)
self.assertEqual(str(train_dset.split), "train")
self.assertEqual(test_dset["filename"][0], f"{name}-test")
self.assertEqual(test_dset.num_rows, 33)
self.assertEqual(test_dset.num_columns, 1)
self.assertIsInstance(test_dset.split, NamedSplit)
self.assertEqual(str(test_dset.split), "test[:33%]")
del train_dset, test_dset
def test_read_sharded(self):
name = "my_name"
train_info = SplitInfo(name="train", num_examples=1000, shard_lengths=[100] * 10)
split_infos = [train_info]
split_dict = SplitDict()
split_dict.add(train_info)
info = DatasetInfo(splits=split_dict)
with tempfile.TemporaryDirectory() as tmp_dir:
reader = ReaderTest(tmp_dir, info)
instructions = "train[:33%]"
dset = Dataset(**reader.read(name, instructions, split_infos))
self.assertEqual(dset["filename"][0], f"{name}-train-00000-of-00010")
self.assertEqual(dset["filename"][-1], f"{name}-train-00003-of-00010")
self.assertEqual(dset.num_rows, 330)
self.assertEqual(dset.num_columns, 1)
def test_read_files(self):
train_info = SplitInfo(name="train", num_examples=100)
test_info = SplitInfo(name="test", num_examples=100)
split_dict = SplitDict()
split_dict.add(train_info)
split_dict.add(test_info)
info = DatasetInfo(splits=split_dict)
with tempfile.TemporaryDirectory() as tmp_dir:
reader = ReaderTest(tmp_dir, info)
files = [
{"filename": os.path.join(tmp_dir, "train")},
{"filename": os.path.join(tmp_dir, "test"), "skip": 10, "take": 10},
]
dset = Dataset(**reader.read_files(files, original_instructions="train+test[10:20]"))
self.assertEqual(dset.num_rows, 110)
self.assertEqual(dset.num_columns, 1)
del dset
@pytest.mark.parametrize("in_memory", [False, True])
def test_read_table(in_memory, dataset, arrow_file):
filename = arrow_file
with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
table = ArrowReader.read_table(filename, in_memory=in_memory)
assert table.shape == dataset.data.shape
assert set(table.column_names) == set(dataset.data.column_names)
assert dict(table.to_pydict()) == dict(dataset.data.to_pydict()) # to_pydict returns OrderedDict
@pytest.mark.parametrize("in_memory", [False, True])
def test_read_files(in_memory, dataset, arrow_file):
filename = arrow_file
reader = ArrowReader("", None)
with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
dataset_kwargs = reader.read_files([{"filename": filename}], in_memory=in_memory)
assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
table = dataset_kwargs["arrow_table"]
assert table.shape == dataset.data.shape
assert set(table.column_names) == set(dataset.data.column_names)
assert dict(table.to_pydict()) == dict(dataset.data.to_pydict()) # to_pydict returns OrderedDict
def test_read_instruction_spec():
assert ReadInstruction("train", to=10, unit="abs").to_spec() == "train[:10]"
assert ReadInstruction("train", from_=-80, to=10, unit="%").to_spec() == "train[-80%:10%]"
spec_train_test = "train+test"
assert ReadInstruction.from_spec(spec_train_test).to_spec() == spec_train_test
spec_train_abs = "train[2:10]"
assert ReadInstruction.from_spec(spec_train_abs).to_spec() == spec_train_abs
spec_train_pct = "train[15%:-20%]"
assert ReadInstruction.from_spec(spec_train_pct).to_spec() == spec_train_pct
spec_train_pct_rounding = "train[:10%](closest)"
assert ReadInstruction.from_spec(spec_train_pct_rounding).to_spec() == "train[:10%]"
spec_train_pct_rounding = "train[:10%](pct1_dropremainder)"
assert ReadInstruction.from_spec(spec_train_pct_rounding).to_spec() == spec_train_pct_rounding
spec_train_test_pct_rounding = "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
assert ReadInstruction.from_spec(spec_train_test_pct_rounding).to_spec() == spec_train_test_pct_rounding
def test_make_file_instructions_basic():
name = "dummy"
split_infos = [SplitInfo(name="train", num_examples=100)]
instruction = "train[:33%]"
filetype_suffix = "arrow"
prefix_path = "prefix"
file_instructions = make_file_instructions(name, split_infos, instruction, filetype_suffix, prefix_path)
assert isinstance(file_instructions, FileInstructions)
assert file_instructions.num_examples == 33
assert file_instructions.file_instructions == [
{"filename": os.path.join(prefix_path, f"{name}-train.arrow"), "skip": 0, "take": 33}
]
split_infos = [SplitInfo(name="train", num_examples=100, shard_lengths=[10] * 10)]
file_instructions = make_file_instructions(name, split_infos, instruction, filetype_suffix, prefix_path)
assert isinstance(file_instructions, FileInstructions)
assert file_instructions.num_examples == 33
assert file_instructions.file_instructions == [
{"filename": os.path.join(prefix_path, f"{name}-train-00000-of-00010.arrow"), "skip": 0, "take": -1},
{"filename": os.path.join(prefix_path, f"{name}-train-00001-of-00010.arrow"), "skip": 0, "take": -1},
{"filename": os.path.join(prefix_path, f"{name}-train-00002-of-00010.arrow"), "skip": 0, "take": -1},
{"filename": os.path.join(prefix_path, f"{name}-train-00003-of-00010.arrow"), "skip": 0, "take": 3},
]
@pytest.mark.parametrize(
"split_name, instruction, shard_lengths, read_range",
[
("train", "train[-20%:]", 100, (80, 100)),
("train", "train[:200]", 100, (0, 100)),
("train", "train[:-200]", 100, None),
("train", "train[-200:]", 100, (0, 100)),
("train", "train[-20%:]", [10] * 10, (80, 100)),
("train", "train[:200]", [10] * 10, (0, 100)),
("train", "train[:-200]", [10] * 10, None),
("train", "train[-200:]", [10] * 10, (0, 100)),
],
)
def test_make_file_instructions(split_name, instruction, shard_lengths, read_range):
name = "dummy"
split_infos = split_infos = [
SplitInfo(
name="train",
num_examples=shard_lengths if not isinstance(shard_lengths, list) else sum(shard_lengths),
shard_lengths=shard_lengths if isinstance(shard_lengths, list) else None,
)
]
filetype_suffix = "arrow"
prefix_path = "prefix"
file_instructions = make_file_instructions(name, split_infos, instruction, filetype_suffix, prefix_path)
assert isinstance(file_instructions, FileInstructions)
assert file_instructions.num_examples == (read_range[1] - read_range[0] if read_range is not None else 0)
if read_range is None:
assert file_instructions.file_instructions == []
else:
if not isinstance(shard_lengths, list):
assert file_instructions.file_instructions == [
{
"filename": os.path.join(prefix_path, f"{name}-{split_name}.arrow"),
"skip": read_range[0],
"take": read_range[1] - read_range[0],
}
]
else:
file_instructions_list = []
shard_offset = 0
for i, shard_length in enumerate(shard_lengths):
filename = os.path.join(prefix_path, f"{name}-{split_name}-{i:05d}-of-{len(shard_lengths):05d}.arrow")
if shard_offset <= read_range[0] < shard_offset + shard_length:
file_instructions_list.append(
{
"filename": filename,
"skip": read_range[0] - shard_offset,
"take": read_range[1] - read_range[0]
if read_range[1] < shard_offset + shard_length
else -1,
}
)
elif shard_offset < read_range[1] <= shard_offset + shard_length:
file_instructions_list.append(
{
"filename": filename,
"skip": 0,
"take": read_range[1] - shard_offset
if read_range[1] < shard_offset + shard_length
else -1,
}
)
elif read_range[0] < shard_offset and read_range[1] > shard_offset + shard_length:
file_instructions_list.append(
{
"filename": filename,
"skip": 0,
"take": -1,
}
)
shard_offset += shard_length
assert file_instructions.file_instructions == file_instructions_list
@pytest.mark.parametrize("name, expected_exception", [(None, TypeError), ("", ValueError)])
def test_make_file_instructions_raises(name, expected_exception):
split_infos = [SplitInfo(name="train", num_examples=100)]
instruction = "train"
filetype_suffix = "arrow"
prefix_path = "prefix_path"
with pytest.raises(expected_exception):
_ = make_file_instructions(name, split_infos, instruction, filetype_suffix, prefix_path)
| datasets/tests/test_arrow_reader.py/0 | {
"file_path": "datasets/tests/test_arrow_reader.py",
"repo_id": "datasets",
"token_count": 5688
} | 114 |
from textwrap import dedent
from types import SimpleNamespace
from unittest.mock import patch
from urllib.parse import quote
import pytest
from huggingface_hub import CommitOperationAdd, CommitOperationDelete
import datasets
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.hub import delete_from_hub
from datasets.utils.hub import hf_dataset_url
@pytest.mark.parametrize("repo_id", ["canonical_dataset_name", "org-name/dataset-name"])
@pytest.mark.parametrize("filename", ["filename.csv", "filename with blanks.csv"])
@pytest.mark.parametrize("revision", [None, "v2"])
def test_dataset_url(repo_id, filename, revision):
url = hf_dataset_url(repo_id=repo_id, filename=filename, revision=revision)
assert url == f"https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(filename)}"
def test_delete_from_hub(temporary_repo, hf_api, hf_token, csv_path, ci_hub_config) -> None:
with temporary_repo() as repo_id:
hf_api.create_repo(repo_id, token=hf_token, repo_type="dataset")
hf_api.upload_file(
path_or_fileobj=str(csv_path),
path_in_repo="cats/train/0000.csv",
repo_id=repo_id,
repo_type="dataset",
token=hf_token,
)
hf_api.upload_file(
path_or_fileobj=str(csv_path),
path_in_repo="dogs/train/0000.csv",
repo_id=repo_id,
repo_type="dataset",
token=hf_token,
)
hf_api.upload_file(
token=hf_token,
path_or_fileobj=dedent(
f"""\
---
{METADATA_CONFIGS_FIELD}:
- config_name: cats
data_files:
- split: train
path: cats/train/*
- config_name: dogs
data_files:
- split: train
path: dogs/train/*
---
"""
).encode(),
path_in_repo="README.md",
repo_id=repo_id,
repo_type="dataset",
)
commit_info = SimpleNamespace(
pr_url="https:///hub-ci.huggingface.co/datasets/__DUMMY_USER__/__DUMMY_DATASET__/refs%2Fpr%2F1"
)
with patch.object(datasets.hub.HfApi, "create_commit", return_value=commit_info) as mock_method:
_ = delete_from_hub(repo_id, "dogs")
assert mock_method.called
assert mock_method.call_args.kwargs.get("commit_message") == "Delete 'dogs' config"
assert mock_method.call_args.kwargs.get("create_pr")
expected_operations = [
CommitOperationDelete(path_in_repo="dogs/train/0000.csv", is_folder=False),
CommitOperationAdd(
path_in_repo="README.md",
path_or_fileobj=dedent(
f"""\
---
{METADATA_CONFIGS_FIELD}:
- config_name: cats
data_files:
- split: train
path: cats/train/*
---
"""
).encode(),
),
]
assert mock_method.call_args.kwargs.get("operations") == expected_operations
| datasets/tests/test_hub.py/0 | {
"file_path": "datasets/tests/test_hub.py",
"repo_id": "datasets",
"token_count": 1576
} | 115 |
import unittest
from unittest.mock import patch
import pytest
from pytest import CaptureFixture
from datasets.utils import (
are_progress_bars_disabled,
disable_progress_bars,
enable_progress_bars,
tqdm,
)
class TestTqdmUtils(unittest.TestCase):
@pytest.fixture(autouse=True)
def capsys(self, capsys: CaptureFixture) -> None:
"""Workaround to make capsys work in unittest framework.
Capsys is a convenient pytest fixture to capture stdout.
See https://waylonwalker.com/pytest-capsys/.
Taken from https://github.com/pytest-dev/pytest/issues/2504#issuecomment-309475790.
"""
self.capsys = capsys
def setUp(self) -> None:
"""Get verbosity to set it back after the tests."""
self._previous_are_progress_bars_disabled = are_progress_bars_disabled()
return super().setUp()
def tearDown(self) -> None:
"""Set back progress bars verbosity as before testing."""
if self._previous_are_progress_bars_disabled:
disable_progress_bars()
else:
enable_progress_bars()
@patch("datasets.utils._tqdm.HF_DATASETS_DISABLE_PROGRESS_BARS", None)
def test_tqdm_helpers(self) -> None:
"""Test helpers to enable/disable progress bars."""
disable_progress_bars()
self.assertTrue(are_progress_bars_disabled())
enable_progress_bars()
self.assertFalse(are_progress_bars_disabled())
@patch("datasets.utils._tqdm.HF_DATASETS_DISABLE_PROGRESS_BARS", True)
def test_cannot_enable_tqdm_when_env_variable_is_set(self) -> None:
"""
Test helpers cannot enable/disable progress bars when
`HF_DATASETS_DISABLE_PROGRESS_BARS` is set.
"""
disable_progress_bars()
self.assertTrue(are_progress_bars_disabled())
with self.assertWarns(UserWarning):
enable_progress_bars()
self.assertTrue(are_progress_bars_disabled()) # Still disabled !
@patch("datasets.utils._tqdm.HF_DATASETS_DISABLE_PROGRESS_BARS", False)
def test_cannot_disable_tqdm_when_env_variable_is_set(self) -> None:
"""
Test helpers cannot enable/disable progress bars when
`HF_DATASETS_DISABLE_PROGRESS_BARS` is set.
"""
enable_progress_bars()
self.assertFalse(are_progress_bars_disabled())
with self.assertWarns(UserWarning):
disable_progress_bars()
self.assertFalse(are_progress_bars_disabled()) # Still enabled !
@patch("datasets.utils._tqdm.HF_DATASETS_DISABLE_PROGRESS_BARS", None)
def test_tqdm_disabled(self) -> None:
"""Test TQDM not outputting anything when globally disabled."""
disable_progress_bars()
for _ in tqdm(range(10)):
pass
captured = self.capsys.readouterr()
self.assertEqual(captured.out, "")
self.assertEqual(captured.err, "")
@patch("datasets.utils._tqdm.HF_DATASETS_DISABLE_PROGRESS_BARS", None)
def test_tqdm_disabled_cannot_be_forced(self) -> None:
"""Test TQDM cannot be forced when globally disabled."""
disable_progress_bars()
for _ in tqdm(range(10), disable=False):
pass
captured = self.capsys.readouterr()
self.assertEqual(captured.out, "")
self.assertEqual(captured.err, "")
@patch("datasets.utils._tqdm.HF_DATASETS_DISABLE_PROGRESS_BARS", None)
def test_tqdm_can_be_disabled_when_globally_enabled(self) -> None:
"""Test TQDM can still be locally disabled even when globally enabled."""
enable_progress_bars()
for _ in tqdm(range(10), disable=True):
pass
captured = self.capsys.readouterr()
self.assertEqual(captured.out, "")
self.assertEqual(captured.err, "")
@patch("datasets.utils._tqdm.HF_DATASETS_DISABLE_PROGRESS_BARS", None)
def test_tqdm_enabled(self) -> None:
"""Test TQDM work normally when globally enabled."""
enable_progress_bars()
for _ in tqdm(range(10)):
pass
captured = self.capsys.readouterr()
self.assertEqual(captured.out, "")
self.assertIn("10/10", captured.err) # tqdm log
| datasets/tests/test_tqdm.py/0 | {
"file_path": "datasets/tests/test_tqdm.py",
"repo_id": "datasets",
"token_count": 1804
} | 116 |
# Diffusers Benchmarks
Welcome to Diffusers Benchmarks. These benchmarks are use to obtain latency and memory information of the most popular models across different scenarios such as:
* Base case i.e., when using `torch.bfloat16` and `torch.nn.functional.scaled_dot_product_attention`.
* Base + `torch.compile()`
* NF4 quantization
* Layerwise upcasting
Instead of full diffusion pipelines, only the forward pass of the respective model classes (such as `FluxTransformer2DModel`) is tested with the real checkpoints (such as `"black-forest-labs/FLUX.1-dev"`).
The entrypoint to running all the currently available benchmarks is in `run_all.py`. However, one can run the individual benchmarks, too, e.g., `python benchmarking_flux.py`. It should produce a CSV file containing various information about the benchmarks run.
The benchmarks are run on a weekly basis and the CI is defined in [benchmark.yml](../.github/workflows/benchmark.yml).
## Running the benchmarks manually
First set up `torch` and install `diffusers` from the root of the directory:
```py
pip install -e ".[quality,test]"
```
Then make sure the other dependencies are installed:
```sh
cd benchmarks/
pip install -r requirements.txt
```
We need to be authenticated to access some of the checkpoints used during benchmarking:
```sh
hf auth login
```
We use an L40 GPU with 128GB RAM to run the benchmark CI. As such, the benchmarks are configured to run on NVIDIA GPUs. So, make sure you have access to a similar machine (or modify the benchmarking scripts accordingly).
Then you can either launch the entire benchmarking suite by running:
```sh
python run_all.py
```
Or, you can run the individual benchmarks.
## Customizing the benchmarks
We define "scenarios" to cover the most common ways in which these models are used. You can
define a new scenario, modifying an existing benchmark file:
```py
BenchmarkScenario(
name=f"{CKPT_ID}-bnb-8bit",
model_cls=FluxTransformer2DModel,
model_init_kwargs={
"pretrained_model_name_or_path": CKPT_ID,
"torch_dtype": torch.bfloat16,
"subfolder": "transformer",
"quantization_config": BitsAndBytesConfig(load_in_8bit=True),
},
get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
model_init_fn=model_init_fn,
)
```
You can also configure a new model-level benchmark and add it to the existing suite. To do so, just defining a valid benchmarking file like `benchmarking_flux.py` should be enough.
Happy benchmarking 🧨 | diffusers/benchmarks/README.md/0 | {
"file_path": "diffusers/benchmarks/README.md",
"repo_id": "diffusers",
"token_count": 765
} | 117 |
<!--Copyright 2025 The HuggingFace Team. 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.
-->
# LoRA
LoRA is a fast and lightweight training method that inserts and trains a significantly smaller number of parameters instead of all the model parameters. This produces a smaller file (~100 MBs) and makes it easier to quickly train a model to learn a new concept. LoRA weights are typically loaded into the denoiser, text encoder or both. The denoiser usually corresponds to a UNet ([`UNet2DConditionModel`], for example) or a Transformer ([`SD3Transformer2DModel`], for example). There are several classes for loading LoRA weights:
- [`StableDiffusionLoraLoaderMixin`] provides functions for loading and unloading, fusing and unfusing, enabling and disabling, and more functions for managing LoRA weights. This class can be used with any model.
- [`StableDiffusionXLLoraLoaderMixin`] is a [Stable Diffusion (SDXL)](../../api/pipelines/stable_diffusion/stable_diffusion_xl) version of the [`StableDiffusionLoraLoaderMixin`] class for loading and saving LoRA weights. It can only be used with the SDXL model.
- [`SD3LoraLoaderMixin`] provides similar functions for [Stable Diffusion 3](https://huggingface.co/blog/sd3).
- [`FluxLoraLoaderMixin`] provides similar functions for [Flux](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux).
- [`CogVideoXLoraLoaderMixin`] provides similar functions for [CogVideoX](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogvideox).
- [`Mochi1LoraLoaderMixin`] provides similar functions for [Mochi](https://huggingface.co/docs/diffusers/main/en/api/pipelines/mochi).
- [`AuraFlowLoraLoaderMixin`] provides similar functions for [AuraFlow](https://huggingface.co/fal/AuraFlow).
- [`LTXVideoLoraLoaderMixin`] provides similar functions for [LTX-Video](https://huggingface.co/docs/diffusers/main/en/api/pipelines/ltx_video).
- [`SanaLoraLoaderMixin`] provides similar functions for [Sana](https://huggingface.co/docs/diffusers/main/en/api/pipelines/sana).
- [`HunyuanVideoLoraLoaderMixin`] provides similar functions for [HunyuanVideo](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hunyuan_video).
- [`Lumina2LoraLoaderMixin`] provides similar functions for [Lumina2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/lumina2).
- [`WanLoraLoaderMixin`] provides similar functions for [Wan](https://huggingface.co/docs/diffusers/main/en/api/pipelines/wan).
- [`SkyReelsV2LoraLoaderMixin`] provides similar functions for [SkyReels-V2](https://huggingface.co/docs/diffusers/main/en/api/pipelines/skyreels_v2).
- [`CogView4LoraLoaderMixin`] provides similar functions for [CogView4](https://huggingface.co/docs/diffusers/main/en/api/pipelines/cogview4).
- [`AmusedLoraLoaderMixin`] is for the [`AmusedPipeline`].
- [`HiDreamImageLoraLoaderMixin`] provides similar functions for [HiDream Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/hidream)
- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen)
- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
<Tip>
To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
</Tip>
## LoraBaseMixin
[[autodoc]] loaders.lora_base.LoraBaseMixin
## StableDiffusionLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.StableDiffusionLoraLoaderMixin
## StableDiffusionXLLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.StableDiffusionXLLoraLoaderMixin
## SD3LoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.SD3LoraLoaderMixin
## FluxLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.FluxLoraLoaderMixin
## CogVideoXLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.CogVideoXLoraLoaderMixin
## Mochi1LoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.Mochi1LoraLoaderMixin
## AuraFlowLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.AuraFlowLoraLoaderMixin
## LTXVideoLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.LTXVideoLoraLoaderMixin
## SanaLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.SanaLoraLoaderMixin
## HunyuanVideoLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.HunyuanVideoLoraLoaderMixin
## Lumina2LoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.Lumina2LoraLoaderMixin
## CogView4LoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.CogView4LoraLoaderMixin
## WanLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.WanLoraLoaderMixin
## SkyReelsV2LoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.SkyReelsV2LoraLoaderMixin
## AmusedLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.AmusedLoraLoaderMixin
## HiDreamImageLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.HiDreamImageLoraLoaderMixin
## QwenImageLoraLoaderMixin
[[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
## LoraBaseMixin
[[autodoc]] loaders.lora_base.LoraBaseMixin | diffusers/docs/source/en/api/loaders/lora.md/0 | {
"file_path": "diffusers/docs/source/en/api/loaders/lora.md",
"repo_id": "diffusers",
"token_count": 1878
} | 118 |
<!--Copyright 2025 The HuggingFace Team. 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.
-->
# AutoencoderKL
The variational autoencoder (VAE) model with KL loss was introduced in [Auto-Encoding Variational Bayes](https://huggingface.co/papers/1312.6114v11) by Diederik P. Kingma and Max Welling. The model is used in 🤗 Diffusers to encode images into latents and to decode latent representations into images.
The abstract from the paper is:
*How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.*
## Loading from the original format
By default the [`AutoencoderKL`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded
from the original format using [`FromOriginalModelMixin.from_single_file`] as follows:
```py
from diffusers import AutoencoderKL
url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors" # can also be a local file
model = AutoencoderKL.from_single_file(url)
```
## AutoencoderKL
[[autodoc]] AutoencoderKL
- decode
- encode
- all
## AutoencoderKLOutput
[[autodoc]] models.autoencoders.autoencoder_kl.AutoencoderKLOutput
## DecoderOutput
[[autodoc]] models.autoencoders.vae.DecoderOutput
## FlaxAutoencoderKL
[[autodoc]] FlaxAutoencoderKL
## FlaxAutoencoderKLOutput
[[autodoc]] models.vae_flax.FlaxAutoencoderKLOutput
## FlaxDecoderOutput
[[autodoc]] models.vae_flax.FlaxDecoderOutput
| diffusers/docs/source/en/api/models/autoencoderkl.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/autoencoderkl.md",
"repo_id": "diffusers",
"token_count": 784
} | 119 |
<!--Copyright 2025 The HuggingFace Team and The InstantX Team. All rights reserved.
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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
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# FluxControlNetModel
FluxControlNetModel is an implementation of ControlNet for Flux.1.
The ControlNet model was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
The abstract from the paper is:
*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
## Loading from the original format
By default the [`FluxControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`].
```py
from diffusers import FluxControlNetPipeline
from diffusers.models import FluxControlNetModel, FluxMultiControlNetModel
controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
controlnet = FluxControlNetModel.from_pretrained("InstantX/FLUX.1-dev-Controlnet-Canny")
controlnet = FluxMultiControlNetModel([controlnet])
pipe = FluxControlNetPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", controlnet=controlnet)
```
## FluxControlNetModel
[[autodoc]] FluxControlNetModel
## FluxControlNetOutput
[[autodoc]] models.controlnet_flux.FluxControlNetOutput | diffusers/docs/source/en/api/models/controlnet_flux.md/0 | {
"file_path": "diffusers/docs/source/en/api/models/controlnet_flux.md",
"repo_id": "diffusers",
"token_count": 740
} | 120 |
<!--Copyright 2025 The HuggingFace Team and Tencent Hunyuan Team. All rights reserved.
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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.
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# ControlNet with Hunyuan-DiT
HunyuanDiTControlNetPipeline is an implementation of ControlNet for [Hunyuan-DiT](https://huggingface.co/papers/2405.08748).
ControlNet was introduced in [Adding Conditional Control to Text-to-Image Diffusion Models](https://huggingface.co/papers/2302.05543) by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala.
With a ControlNet model, you can provide an additional control image to condition and control Hunyuan-DiT generation. For example, if you provide a depth map, the ControlNet model generates an image that'll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
The abstract from the paper is:
*We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with "zero convolutions" (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models.*
This code is implemented by Tencent Hunyuan Team. You can find pre-trained checkpoints for Hunyuan-DiT ControlNets on [Tencent Hunyuan](https://huggingface.co/Tencent-Hunyuan).
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip>
## HunyuanDiTControlNetPipeline
[[autodoc]] HunyuanDiTControlNetPipeline
- all
- __call__
| diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/controlnet_hunyuandit.md",
"repo_id": "diffusers",
"token_count": 712
} | 121 |
<!-- Copyright 2025 The HuggingFace Team. 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.
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# Framepack
<div class="flex flex-wrap space-x-1">
<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>
</div>
[Packing Input Frame Context in Next-Frame Prediction Models for Video Generation](https://huggingface.co/papers/2504.12626) by Lvmin Zhang and Maneesh Agrawala.
*We present a neural network structure, FramePack, to train next-frame (or next-frame-section) prediction models for video generation. The FramePack compresses input frames to make the transformer context length a fixed number regardless of the video length. As a result, we are able to process a large number of frames using video diffusion with computation bottleneck similar to image diffusion. This also makes the training video batch sizes significantly higher (batch sizes become comparable to image diffusion training). We also propose an anti-drifting sampling method that generates frames in inverted temporal order with early-established endpoints to avoid exposure bias (error accumulation over iterations). Finally, we show that existing video diffusion models can be finetuned with FramePack, and their visual quality may be improved because the next-frame prediction supports more balanced diffusion schedulers with less extreme flow shift timesteps.*
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.
</Tip>
## Available models
| Model name | Description |
|:---|:---|
- [`lllyasviel/FramePackI2V_HY`](https://huggingface.co/lllyasviel/FramePackI2V_HY) | Trained with the "inverted anti-drifting" strategy as described in the paper. Inference requires setting `sampling_type="inverted_anti_drifting"` when running the pipeline. |
- [`lllyasviel/FramePack_F1_I2V_HY_20250503`](https://huggingface.co/lllyasviel/FramePack_F1_I2V_HY_20250503) | Trained with a novel anti-drifting strategy but inference is performed in "vanilla" strategy as described in the paper. Inference requires setting `sampling_type="vanilla"` when running the pipeline. |
## Usage
Refer to the pipeline documentation for basic usage examples. The following section contains examples of offloading, different sampling methods, quantization, and more.
### First and last frame to video
The following example shows how to use Framepack with start and end image controls, using the inverted anti-drifiting sampling model.
```python
import torch
from diffusers import HunyuanVideoFramepackPipeline, HunyuanVideoFramepackTransformer3DModel
from diffusers.utils import export_to_video, load_image
from transformers import SiglipImageProcessor, SiglipVisionModel
transformer = HunyuanVideoFramepackTransformer3DModel.from_pretrained(
"lllyasviel/FramePackI2V_HY", torch_dtype=torch.bfloat16
)
feature_extractor = SiglipImageProcessor.from_pretrained(
"lllyasviel/flux_redux_bfl", subfolder="feature_extractor"
)
image_encoder = SiglipVisionModel.from_pretrained(
"lllyasviel/flux_redux_bfl", subfolder="image_encoder", torch_dtype=torch.float16
)
pipe = HunyuanVideoFramepackPipeline.from_pretrained(
"hunyuanvideo-community/HunyuanVideo",
transformer=transformer,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
torch_dtype=torch.float16,
)
# Enable memory optimizations
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
prompt = "CG animation style, a small blue bird takes off from the ground, flapping its wings. The bird's feathers are delicate, with a unique pattern on its chest. The background shows a blue sky with white clouds under bright sunshine. The camera follows the bird upward, capturing its flight and the vastness of the sky from a close-up, low-angle perspective."
first_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_first_frame.png"
)
last_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/flf2v_input_last_frame.png"
)
output = pipe(
image=first_image,
last_image=last_image,
prompt=prompt,
height=512,
width=512,
num_frames=91,
num_inference_steps=30,
guidance_scale=9.0,
generator=torch.Generator().manual_seed(0),
sampling_type="inverted_anti_drifting",
).frames[0]
export_to_video(output, "output.mp4", fps=30)
```
### Vanilla sampling
The following example shows how to use Framepack with the F1 model trained with vanilla sampling but new regulation approach for anti-drifting.
```python
import torch
from diffusers import HunyuanVideoFramepackPipeline, HunyuanVideoFramepackTransformer3DModel
from diffusers.utils import export_to_video, load_image
from transformers import SiglipImageProcessor, SiglipVisionModel
transformer = HunyuanVideoFramepackTransformer3DModel.from_pretrained(
"lllyasviel/FramePack_F1_I2V_HY_20250503", torch_dtype=torch.bfloat16
)
feature_extractor = SiglipImageProcessor.from_pretrained(
"lllyasviel/flux_redux_bfl", subfolder="feature_extractor"
)
image_encoder = SiglipVisionModel.from_pretrained(
"lllyasviel/flux_redux_bfl", subfolder="image_encoder", torch_dtype=torch.float16
)
pipe = HunyuanVideoFramepackPipeline.from_pretrained(
"hunyuanvideo-community/HunyuanVideo",
transformer=transformer,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
torch_dtype=torch.float16,
)
# Enable memory optimizations
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/penguin.png"
)
output = pipe(
image=image,
prompt="A penguin dancing in the snow",
height=832,
width=480,
num_frames=91,
num_inference_steps=30,
guidance_scale=9.0,
generator=torch.Generator().manual_seed(0),
sampling_type="vanilla",
).frames[0]
export_to_video(output, "output.mp4", fps=30)
```
### Group offloading
Group offloading ([`~hooks.apply_group_offloading`]) provides aggressive memory optimizations for offloading internal parts of any model to the CPU, with possibly no additional overhead to generation time. If you have very low VRAM available, this approach may be suitable for you depending on the amount of CPU RAM available.
```python
import torch
from diffusers import HunyuanVideoFramepackPipeline, HunyuanVideoFramepackTransformer3DModel
from diffusers.hooks import apply_group_offloading
from diffusers.utils import export_to_video, load_image
from transformers import SiglipImageProcessor, SiglipVisionModel
transformer = HunyuanVideoFramepackTransformer3DModel.from_pretrained(
"lllyasviel/FramePack_F1_I2V_HY_20250503", torch_dtype=torch.bfloat16
)
feature_extractor = SiglipImageProcessor.from_pretrained(
"lllyasviel/flux_redux_bfl", subfolder="feature_extractor"
)
image_encoder = SiglipVisionModel.from_pretrained(
"lllyasviel/flux_redux_bfl", subfolder="image_encoder", torch_dtype=torch.float16
)
pipe = HunyuanVideoFramepackPipeline.from_pretrained(
"hunyuanvideo-community/HunyuanVideo",
transformer=transformer,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
torch_dtype=torch.float16,
)
# Enable group offloading
onload_device = torch.device("cuda")
offload_device = torch.device("cpu")
list(map(
lambda x: apply_group_offloading(x, onload_device, offload_device, offload_type="leaf_level", use_stream=True, low_cpu_mem_usage=True),
[pipe.text_encoder, pipe.text_encoder_2, pipe.transformer]
))
pipe.image_encoder.to(onload_device)
pipe.vae.to(onload_device)
pipe.vae.enable_tiling()
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/penguin.png"
)
output = pipe(
image=image,
prompt="A penguin dancing in the snow",
height=832,
width=480,
num_frames=91,
num_inference_steps=30,
guidance_scale=9.0,
generator=torch.Generator().manual_seed(0),
sampling_type="vanilla",
).frames[0]
print(f"Max memory: {torch.cuda.max_memory_allocated() / 1024**3:.3f} GB")
export_to_video(output, "output.mp4", fps=30)
```
## HunyuanVideoFramepackPipeline
[[autodoc]] HunyuanVideoFramepackPipeline
- all
- __call__
## HunyuanVideoPipelineOutput
[[autodoc]] pipelines.hunyuan_video.pipeline_output.HunyuanVideoPipelineOutput
| diffusers/docs/source/en/api/pipelines/framepack.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/framepack.md",
"repo_id": "diffusers",
"token_count": 2945
} | 122 |
<!--
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Copyright 2024-2025 The HuggingFace Team. 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.
-->
# Marigold Computer Vision
Marigold was proposed in
[Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://huggingface.co/papers/2312.02145),
a CVPR 2024 Oral paper by
[Bingxin Ke](http://www.kebingxin.com/),
[Anton Obukhov](https://www.obukhov.ai/),
[Shengyu Huang](https://shengyuh.github.io/),
[Nando Metzger](https://nandometzger.github.io/),
[Rodrigo Caye Daudt](https://rcdaudt.github.io/), and
[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en).
The core idea is to **repurpose the generative prior of Text-to-Image Latent Diffusion Models (LDMs) for traditional
computer vision tasks**.
This approach was explored by fine-tuning Stable Diffusion for **Monocular Depth Estimation**, as demonstrated in the
teaser above.
Marigold was later extended in the follow-up paper,
[Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis](https://huggingface.co/papers/2312.02145),
authored by
[Bingxin Ke](http://www.kebingxin.com/),
[Kevin Qu](https://www.linkedin.com/in/kevin-qu-b3417621b/?locale=en_US),
[Tianfu Wang](https://tianfwang.github.io/),
[Nando Metzger](https://nandometzger.github.io/),
[Shengyu Huang](https://shengyuh.github.io/),
[Bo Li](https://www.linkedin.com/in/bobboli0202/),
[Anton Obukhov](https://www.obukhov.ai/), and
[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en).
This work expanded Marigold to support new modalities such as **Surface Normals** and **Intrinsic Image Decomposition**
(IID), introduced a training protocol for **Latent Consistency Models** (LCM), and demonstrated **High-Resolution** (HR)
processing capability.
<Tip>
The early Marigold models (`v1-0` and earlier) were optimized for best results with at least 10 inference steps.
LCM models were later developed to enable high-quality inference in just 1 to 4 steps.
Marigold models `v1-1` and later use the DDIM scheduler to achieve optimal
results in as few as 1 to 4 steps.
</Tip>
## Available Pipelines
Each pipeline is tailored for a specific computer vision task, processing an input RGB image and generating a
corresponding prediction.
Currently, the following computer vision tasks are implemented:
| Pipeline | Recommended Model Checkpoints | Spaces (Interactive Apps) | Predicted Modalities |
|---------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------:|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [MarigoldDepthPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_depth.py) | [prs-eth/marigold-depth-v1-1](https://huggingface.co/prs-eth/marigold-depth-v1-1) | [Depth Estimation](https://huggingface.co/spaces/prs-eth/marigold) | [Depth](https://en.wikipedia.org/wiki/Depth_map), [Disparity](https://en.wikipedia.org/wiki/Binocular_disparity) |
| [MarigoldNormalsPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_normals.py) | [prs-eth/marigold-normals-v1-1](https://huggingface.co/prs-eth/marigold-normals-v1-1) | [Surface Normals Estimation](https://huggingface.co/spaces/prs-eth/marigold-normals) | [Surface normals](https://en.wikipedia.org/wiki/Normal_mapping) |
| [MarigoldIntrinsicsPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/marigold/pipeline_marigold_intrinsics.py) | [prs-eth/marigold-iid-appearance-v1-1](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1),<br>[prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1) | [Intrinsic Image Decomposition](https://huggingface.co/spaces/prs-eth/marigold-iid) | [Albedo](https://en.wikipedia.org/wiki/Albedo), [Materials](https://www.n.aiq3d.com/wiki/roughnessmetalnessao-map), [Lighting](https://en.wikipedia.org/wiki/Diffuse_reflection) |
## Available Checkpoints
All original checkpoints are available under the [PRS-ETH](https://huggingface.co/prs-eth/) organization on Hugging Face.
They are designed for use with diffusers pipelines and the [original codebase](https://github.com/prs-eth/marigold), which can also be used to train
new model checkpoints.
The following is a summary of the recommended checkpoints, all of which produce reliable results with 1 to 4 steps.
| Checkpoint | Modality | Comment |
|-----------------------------------------------------------------------------------------------------|--------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [prs-eth/marigold-depth-v1-1](https://huggingface.co/prs-eth/marigold-depth-v1-1) | Depth | Affine-invariant depth prediction assigns each pixel a value between 0 (near plane) and 1 (far plane), with both planes determined by the model during inference. |
| [prs-eth/marigold-normals-v0-1](https://huggingface.co/prs-eth/marigold-normals-v0-1) | Normals | The surface normals predictions are unit-length 3D vectors in the screen space camera, with values in the range from -1 to 1. |
| [prs-eth/marigold-iid-appearance-v1-1](https://huggingface.co/prs-eth/marigold-iid-appearance-v1-1) | Intrinsics | InteriorVerse decomposition is comprised of Albedo and two BRDF material properties: Roughness and Metallicity. |
| [prs-eth/marigold-iid-lighting-v1-1](https://huggingface.co/prs-eth/marigold-iid-lighting-v1-1) | Intrinsics | HyperSim decomposition of an image  \\(I\\)  is comprised of Albedo  \\(A\\), Diffuse shading  \\(S\\), and Non-diffuse residual  \\(R\\):  \\(I = A*S+R\\). |
<Tip>
Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff
between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to
efficiently load the same components into multiple pipelines.
Also, to know more about reducing the memory usage of this pipeline, refer to the ["Reduce memory usage"] section
[here](../../using-diffusers/svd#reduce-memory-usage).
</Tip>
<Tip warning={true}>
Marigold pipelines were designed and tested with the scheduler embedded in the model checkpoint.
The optimal number of inference steps varies by scheduler, with no universal value that works best across all cases.
To accommodate this, the `num_inference_steps` parameter in the pipeline's `__call__` method defaults to `None` (see the
API reference).
Unless set explicitly, it inherits the value from the `default_denoising_steps` field in the checkpoint configuration
file (`model_index.json`).
This ensures high-quality predictions when invoking the pipeline with only the `image` argument.
</Tip>
See also Marigold [usage examples](../../using-diffusers/marigold_usage).
## Marigold Depth Prediction API
[[autodoc]] MarigoldDepthPipeline
- __call__
[[autodoc]] pipelines.marigold.pipeline_marigold_depth.MarigoldDepthOutput
[[autodoc]] pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_depth
## Marigold Normals Estimation API
[[autodoc]] MarigoldNormalsPipeline
- __call__
[[autodoc]] pipelines.marigold.pipeline_marigold_normals.MarigoldNormalsOutput
[[autodoc]] pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_normals
## Marigold Intrinsic Image Decomposition API
[[autodoc]] MarigoldIntrinsicsPipeline
- __call__
[[autodoc]] pipelines.marigold.pipeline_marigold_intrinsics.MarigoldIntrinsicsOutput
[[autodoc]] pipelines.marigold.marigold_image_processing.MarigoldImageProcessor.visualize_intrinsics
| diffusers/docs/source/en/api/pipelines/marigold.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/marigold.md",
"repo_id": "diffusers",
"token_count": 4138
} | 123 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Stable Diffusion 2
Stable Diffusion 2 is a text-to-image _latent diffusion_ model built upon the work of the original [Stable Diffusion](https://stability.ai/blog/stable-diffusion-public-release), and it was led by Robin Rombach and Katherine Crowson from [Stability AI](https://stability.ai/) and [LAION](https://laion.ai/).
*The Stable Diffusion 2.0 release includes robust text-to-image models trained using a brand new text encoder (OpenCLIP), developed by LAION with support from Stability AI, which greatly improves the quality of the generated images compared to earlier V1 releases. The text-to-image models in this release can generate images with default resolutions of both 512x512 pixels and 768x768 pixels.
These models are trained on an aesthetic subset of the [LAION-5B dataset](https://laion.ai/blog/laion-5b/) created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using [LAION’s NSFW filter](https://openreview.net/forum?id=M3Y74vmsMcY).*
For more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer to the official [announcement post](https://stability.ai/blog/stable-diffusion-v2-release).
The architecture of Stable Diffusion 2 is more or less identical to the original [Stable Diffusion model](./text2img) so check out it's API documentation for how to use Stable Diffusion 2. We recommend using the [`DPMSolverMultistepScheduler`] as it gives a reasonable speed/quality trade-off and can be run with as little as 20 steps.
Stable Diffusion 2 is available for tasks like text-to-image, inpainting, super-resolution, and depth-to-image:
| Task | Repository |
|-------------------------|---------------------------------------------------------------------------------------------------------------|
| text-to-image (512x512) | [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) |
| text-to-image (768x768) | [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2) |
| inpainting | [stabilityai/stable-diffusion-2-inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) |
| super-resolution | [stable-diffusion-x4-upscaler](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler) |
| depth-to-image | [stabilityai/stable-diffusion-2-depth](https://huggingface.co/stabilityai/stable-diffusion-2-depth) |
Here are some examples for how to use Stable Diffusion 2 for each task:
<Tip>
Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!
If you're interested in using one of the official checkpoints for a task, explore the [CompVis](https://huggingface.co/CompVis), [Runway](https://huggingface.co/runwayml), and [Stability AI](https://huggingface.co/stabilityai) Hub organizations!
</Tip>
## Text-to-image
```py
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
import torch
repo_id = "stabilityai/stable-diffusion-2-base"
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "High quality photo of an astronaut riding a horse in space"
image = pipe(prompt, num_inference_steps=25).images[0]
image
```
## Inpainting
```py
import torch
from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler
from diffusers.utils import load_image, make_image_grid
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = load_image(img_url).resize((512, 512))
mask_image = load_image(mask_url).resize((512, 512))
repo_id = "stabilityai/stable-diffusion-2-inpainting"
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cuda")
prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image, num_inference_steps=25).images[0]
make_image_grid([init_image, mask_image, image], rows=1, cols=3)
```
## Super-resolution
```py
from diffusers import StableDiffusionUpscalePipeline
from diffusers.utils import load_image, make_image_grid
import torch
# load model and scheduler
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")
# let's download an image
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
low_res_img = load_image(url)
low_res_img = low_res_img.resize((128, 128))
prompt = "a white cat"
upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
make_image_grid([low_res_img.resize((512, 512)), upscaled_image.resize((512, 512))], rows=1, cols=2)
```
## Depth-to-image
```py
import torch
from diffusers import StableDiffusionDepth2ImgPipeline
from diffusers.utils import load_image, make_image_grid
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth",
torch_dtype=torch.float16,
).to("cuda")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
init_image = load_image(url)
prompt = "two tigers"
negative_prompt = "bad, deformed, ugly, bad anotomy"
image = pipe(prompt=prompt, image=init_image, negative_prompt=negative_prompt, strength=0.7).images[0]
make_image_grid([init_image, image], rows=1, cols=2)
```
| diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md/0 | {
"file_path": "diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_2.md",
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# Quantization
Quantization techniques reduce memory and computational costs by representing weights and activations with lower-precision data types like 8-bit integers (int8). This enables loading larger models you normally wouldn't be able to fit into memory, and speeding up inference.
<Tip>
Learn how to quantize models in the [Quantization](../quantization/overview) guide.
</Tip>
## PipelineQuantizationConfig
[[autodoc]] quantizers.PipelineQuantizationConfig
## BitsAndBytesConfig
[[autodoc]] quantizers.quantization_config.BitsAndBytesConfig
## GGUFQuantizationConfig
[[autodoc]] quantizers.quantization_config.GGUFQuantizationConfig
## QuantoConfig
[[autodoc]] quantizers.quantization_config.QuantoConfig
## TorchAoConfig
[[autodoc]] quantizers.quantization_config.TorchAoConfig
## DiffusersQuantizer
[[autodoc]] quantizers.base.DiffusersQuantizer
| diffusers/docs/source/en/api/quantization.md/0 | {
"file_path": "diffusers/docs/source/en/api/quantization.md",
"repo_id": "diffusers",
"token_count": 385
} | 125 |
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<p align="center">
<br>
<img src="https://raw.githubusercontent.com/huggingface/diffusers/77aadfee6a891ab9fcfb780f87c693f7a5beeb8e/docs/source/imgs/diffusers_library.jpg" width="400" style="border: none;"/>
<br>
</p>
# Diffusers
Diffusers is a library of state-of-the-art pretrained diffusion models for generating videos, images, and audio.
The library revolves around the [`DiffusionPipeline`], an API designed for:
- easy inference with only a few lines of code
- flexibility to mix-and-match pipeline components (models, schedulers)
- loading and using adapters like LoRA
Diffusers also comes with optimizations - such as offloading and quantization - to ensure even the largest models are accessible on memory-constrained devices. If memory is not an issue, Diffusers supports torch.compile to boost inference speed.
Get started right away with a Diffusers model on the [Hub](https://huggingface.co/models?library=diffusers&sort=trending) today!
## Learn
If you're a beginner, we recommend starting with the [Hugging Face Diffusion Models Course](https://huggingface.co/learn/diffusion-course/unit0/1). You'll learn the theory behind diffusion models, and learn how to use the Diffusers library to generate images, fine-tune your own models, and more.
| diffusers/docs/source/en/index.md/0 | {
"file_path": "diffusers/docs/source/en/index.md",
"repo_id": "diffusers",
"token_count": 509
} | 126 |
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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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.
-->
# Intel Gaudi
The Intel Gaudi AI accelerator family includes [Intel Gaudi 1](https://habana.ai/products/gaudi/), [Intel Gaudi 2](https://habana.ai/products/gaudi2/), and [Intel Gaudi 3](https://habana.ai/products/gaudi3/). Each server is equipped with 8 devices, known as Habana Processing Units (HPUs), providing 128GB of memory on Gaudi 3, 96GB on Gaudi 2, and 32GB on the first-gen Gaudi. For more details on the underlying hardware architecture, check out the [Gaudi Architecture](https://docs.habana.ai/en/latest/Gaudi_Overview/Gaudi_Architecture.html) overview.
Diffusers pipelines can take advantage of HPU acceleration, even if a pipeline hasn't been added to [Optimum for Intel Gaudi](https://huggingface.co/docs/optimum/main/en/habana/index) yet, with the [GPU Migration Toolkit](https://docs.habana.ai/en/latest/PyTorch/PyTorch_Model_Porting/GPU_Migration_Toolkit/GPU_Migration_Toolkit.html).
Call `.to("hpu")` on your pipeline to move it to a HPU device as shown below for Flux:
```py
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
pipeline.to("hpu")
image = pipeline("An image of a squirrel in Picasso style").images[0]
```
> [!TIP]
> For Gaudi-optimized diffusion pipeline implementations, we recommend using [Optimum for Intel Gaudi](https://huggingface.co/docs/optimum/main/en/habana/index).
| diffusers/docs/source/en/optimization/habana.md/0 | {
"file_path": "diffusers/docs/source/en/optimization/habana.md",
"repo_id": "diffusers",
"token_count": 595
} | 127 |
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the License. You may obtain a copy of the License at
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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specific language governing permissions and limitations under the License.
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# Quanto
[Quanto](https://github.com/huggingface/optimum-quanto) is a PyTorch quantization backend for [Optimum](https://huggingface.co/docs/optimum/en/index). It has been designed with versatility and simplicity in mind:
- All features are available in eager mode (works with non-traceable models)
- Supports quantization aware training
- Quantized models are compatible with `torch.compile`
- Quantized models are Device agnostic (e.g CUDA,XPU,MPS,CPU)
In order to use the Quanto backend, you will first need to install `optimum-quanto>=0.2.6` and `accelerate`
```shell
pip install optimum-quanto accelerate
```
Now you can quantize a model by passing the `QuantoConfig` object to the `from_pretrained()` method. Although the Quanto library does allow quantizing `nn.Conv2d` and `nn.LayerNorm` modules, currently, Diffusers only supports quantizing the weights in the `nn.Linear` layers of a model. The following snippet demonstrates how to apply `float8` quantization with Quanto.
```python
import torch
from diffusers import FluxTransformer2DModel, QuantoConfig
model_id = "black-forest-labs/FLUX.1-dev"
quantization_config = QuantoConfig(weights_dtype="float8")
transformer = FluxTransformer2DModel.from_pretrained(
model_id,
subfolder="transformer",
quantization_config=quantization_config,
torch_dtype=torch.bfloat16,
)
pipe = FluxPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch_dtype)
pipe.to("cuda")
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt, num_inference_steps=50, guidance_scale=4.5, max_sequence_length=512
).images[0]
image.save("output.png")
```
## Skipping Quantization on specific modules
It is possible to skip applying quantization on certain modules using the `modules_to_not_convert` argument in the `QuantoConfig`. Please ensure that the modules passed in to this argument match the keys of the modules in the `state_dict`
```python
import torch
from diffusers import FluxTransformer2DModel, QuantoConfig
model_id = "black-forest-labs/FLUX.1-dev"
quantization_config = QuantoConfig(weights_dtype="float8", modules_to_not_convert=["proj_out"])
transformer = FluxTransformer2DModel.from_pretrained(
model_id,
subfolder="transformer",
quantization_config=quantization_config,
torch_dtype=torch.bfloat16,
)
```
## Using `from_single_file` with the Quanto Backend
`QuantoConfig` is compatible with `~FromOriginalModelMixin.from_single_file`.
```python
import torch
from diffusers import FluxTransformer2DModel, QuantoConfig
ckpt_path = "https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/flux1-dev.safetensors"
quantization_config = QuantoConfig(weights_dtype="float8")
transformer = FluxTransformer2DModel.from_single_file(ckpt_path, quantization_config=quantization_config, torch_dtype=torch.bfloat16)
```
## Saving Quantized models
Diffusers supports serializing Quanto models using the `~ModelMixin.save_pretrained` method.
The serialization and loading requirements are different for models quantized directly with the Quanto library and models quantized
with Diffusers using Quanto as the backend. It is currently not possible to load models quantized directly with Quanto into Diffusers using `~ModelMixin.from_pretrained`
```python
import torch
from diffusers import FluxTransformer2DModel, QuantoConfig
model_id = "black-forest-labs/FLUX.1-dev"
quantization_config = QuantoConfig(weights_dtype="float8")
transformer = FluxTransformer2DModel.from_pretrained(
model_id,
subfolder="transformer",
quantization_config=quantization_config,
torch_dtype=torch.bfloat16,
)
# save quantized model to reuse
transformer.save_pretrained("<your quantized model save path>")
# you can reload your quantized model with
model = FluxTransformer2DModel.from_pretrained("<your quantized model save path>")
```
## Using `torch.compile` with Quanto
Currently the Quanto backend supports `torch.compile` for the following quantization types:
- `int8` weights
```python
import torch
from diffusers import FluxPipeline, FluxTransformer2DModel, QuantoConfig
model_id = "black-forest-labs/FLUX.1-dev"
quantization_config = QuantoConfig(weights_dtype="int8")
transformer = FluxTransformer2DModel.from_pretrained(
model_id,
subfolder="transformer",
quantization_config=quantization_config,
torch_dtype=torch.bfloat16,
)
transformer = torch.compile(transformer, mode="max-autotune", fullgraph=True)
pipe = FluxPipeline.from_pretrained(
model_id, transformer=transformer, torch_dtype=torch_dtype
)
pipe.to("cuda")
images = pipe("A cat holding a sign that says hello").images[0]
images.save("flux-quanto-compile.png")
```
## Supported Quantization Types
### Weights
- float8
- int8
- int4
- int2
| diffusers/docs/source/en/quantization/quanto.md/0 | {
"file_path": "diffusers/docs/source/en/quantization/quanto.md",
"repo_id": "diffusers",
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Overview
🤗 Diffusers provides a collection of training scripts for you to train your own diffusion models. You can find all of our training scripts in [diffusers/examples](https://github.com/huggingface/diffusers/tree/main/examples).
Each training script is:
- **Self-contained**: the training script does not depend on any local files, and all packages required to run the script are installed from the `requirements.txt` file.
- **Easy-to-tweak**: the training scripts are an example of how to train a diffusion model for a specific task and won't work out-of-the-box for every training scenario. You'll likely need to adapt the training script for your specific use-case. To help you with that, we've fully exposed the data preprocessing code and the training loop so you can modify it for your own use.
- **Beginner-friendly**: the training scripts are designed to be beginner-friendly and easy to understand, rather than including the latest state-of-the-art methods to get the best and most competitive results. Any training methods we consider too complex are purposefully left out.
- **Single-purpose**: each training script is expressly designed for only one task to keep it readable and understandable.
Our current collection of training scripts include:
| Training | SDXL-support | LoRA-support | Flax-support |
|---|---|---|---|
| [unconditional image generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) | | | |
| [text-to-image](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) | 👍 | 👍 | 👍 |
| [textual inversion](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb) | | | 👍 |
| [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb) | 👍 | 👍 | 👍 |
| [ControlNet](https://github.com/huggingface/diffusers/tree/main/examples/controlnet) | 👍 | | 👍 |
| [InstructPix2Pix](https://github.com/huggingface/diffusers/tree/main/examples/instruct_pix2pix) | 👍 | | |
| [Custom Diffusion](https://github.com/huggingface/diffusers/tree/main/examples/custom_diffusion) | | | |
| [T2I-Adapters](https://github.com/huggingface/diffusers/tree/main/examples/t2i_adapter) | 👍 | | |
| [Kandinsky 2.2](https://github.com/huggingface/diffusers/tree/main/examples/kandinsky2_2/text_to_image) | | 👍 | |
| [Wuerstchen](https://github.com/huggingface/diffusers/tree/main/examples/wuerstchen/text_to_image) | | 👍 | |
These examples are **actively** maintained, so please feel free to open an issue if they aren't working as expected. If you feel like another training example should be included, you're more than welcome to start a [Feature Request](https://github.com/huggingface/diffusers/issues/new?assignees=&labels=&template=feature_request.md&title=) to discuss your feature idea with us and whether it meets our criteria of being self-contained, easy-to-tweak, beginner-friendly, and single-purpose.
## Install
Make sure you can successfully run the latest versions of the example scripts by installing the library from source in a new virtual environment:
```bash
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install .
```
Then navigate to the folder of the training script (for example, [DreamBooth](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)) and install the `requirements.txt` file. Some training scripts have a specific requirement file for SDXL, LoRA or Flax. If you're using one of these scripts, make sure you install its corresponding requirements file.
```bash
cd examples/dreambooth
pip install -r requirements.txt
# to train SDXL with DreamBooth
pip install -r requirements_sdxl.txt
```
To speedup training and reduce memory-usage, we recommend:
- using PyTorch 2.0 or higher to automatically use [scaled dot product attention](../optimization/fp16#scaled-dot-product-attention) during training (you don't need to make any changes to the training code)
- installing [xFormers](../optimization/xformers) to enable memory-efficient attention | diffusers/docs/source/en/training/overview.md/0 | {
"file_path": "diffusers/docs/source/en/training/overview.md",
"repo_id": "diffusers",
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} | 129 |
# Create a server
Diffusers' pipelines can be used as an inference engine for a server. It supports concurrent and multithreaded requests to generate images that may be requested by multiple users at the same time.
This guide will show you how to use the [`StableDiffusion3Pipeline`] in a server, but feel free to use any pipeline you want.
Start by navigating to the `examples/server` folder and installing all of the dependencies.
```py
pip install .
pip install -f requirements.txt
```
Launch the server with the following command.
```py
python server.py
```
The server is accessed at http://localhost:8000. You can curl this model with the following command.
```
curl -X POST -H "Content-Type: application/json" --data '{"model": "something", "prompt": "a kitten in front of a fireplace"}' http://localhost:8000/v1/images/generations
```
If you need to upgrade some dependencies, you can use either [pip-tools](https://github.com/jazzband/pip-tools) or [uv](https://github.com/astral-sh/uv). For example, upgrade the dependencies with `uv` using the following command.
```
uv pip compile requirements.in -o requirements.txt
```
The server is built with [FastAPI](https://fastapi.tiangolo.com/async/). The endpoint for `v1/images/generations` is shown below.
```py
@app.post("/v1/images/generations")
async def generate_image(image_input: TextToImageInput):
try:
loop = asyncio.get_event_loop()
scheduler = shared_pipeline.pipeline.scheduler.from_config(shared_pipeline.pipeline.scheduler.config)
pipeline = StableDiffusion3Pipeline.from_pipe(shared_pipeline.pipeline, scheduler=scheduler)
generator = torch.Generator(device="cuda")
generator.manual_seed(random.randint(0, 10000000))
output = await loop.run_in_executor(None, lambda: pipeline(image_input.prompt, generator = generator))
logger.info(f"output: {output}")
image_url = save_image(output.images[0])
return {"data": [{"url": image_url}]}
except Exception as e:
if isinstance(e, HTTPException):
raise e
elif hasattr(e, 'message'):
raise HTTPException(status_code=500, detail=e.message + traceback.format_exc())
raise HTTPException(status_code=500, detail=str(e) + traceback.format_exc())
```
The `generate_image` function is defined as asynchronous with the [async](https://fastapi.tiangolo.com/async/) keyword so that FastAPI knows that whatever is happening in this function won't necessarily return a result right away. Once it hits some point in the function that it needs to await some other [Task](https://docs.python.org/3/library/asyncio-task.html#asyncio.Task), the main thread goes back to answering other HTTP requests. This is shown in the code below with the [await](https://fastapi.tiangolo.com/async/#async-and-await) keyword.
```py
output = await loop.run_in_executor(None, lambda: pipeline(image_input.prompt, generator = generator))
```
At this point, the execution of the pipeline function is placed onto a [new thread](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor), and the main thread performs other things until a result is returned from the `pipeline`.
Another important aspect of this implementation is creating a `pipeline` from `shared_pipeline`. The goal behind this is to avoid loading the underlying model more than once onto the GPU while still allowing for each new request that is running on a separate thread to have its own generator and scheduler. The scheduler, in particular, is not thread-safe, and it will cause errors like: `IndexError: index 21 is out of bounds for dimension 0 with size 21` if you try to use the same scheduler across multiple threads.
| diffusers/docs/source/en/using-diffusers/create_a_server.md/0 | {
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-->
# Perturbed-Attention Guidance
[Perturbed-Attention Guidance (PAG)](https://ku-cvlab.github.io/Perturbed-Attention-Guidance/) is a new diffusion sampling guidance that improves sample quality across both unconditional and conditional settings, achieving this without requiring further training or the integration of external modules. PAG is designed to progressively enhance the structure of synthesized samples throughout the denoising process by considering the self-attention mechanisms' ability to capture structural information. It involves generating intermediate samples with degraded structure by substituting selected self-attention maps in diffusion U-Net with an identity matrix, and guiding the denoising process away from these degraded samples.
This guide will show you how to use PAG for various tasks and use cases.
## General tasks
You can apply PAG to the [`StableDiffusionXLPipeline`] for tasks such as text-to-image, image-to-image, and inpainting. To enable PAG for a specific task, load the pipeline using the [AutoPipeline](../api/pipelines/auto_pipeline) API with the `enable_pag=True` flag and the `pag_applied_layers` argument.
> [!TIP]
> 🤗 Diffusers currently only supports using PAG with selected SDXL pipelines and [`PixArtSigmaPAGPipeline`]. But feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you want to add PAG support to a new pipeline!
<hfoptions id="tasks">
<hfoption id="Text-to-image">
```py
from diffusers import AutoPipelineForText2Image
from diffusers.utils import load_image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
enable_pag=True,
pag_applied_layers=["mid"],
torch_dtype=torch.float16
)
pipeline.enable_model_cpu_offload()
```
> [!TIP]
> The `pag_applied_layers` argument allows you to specify which layers PAG is applied to. Additionally, you can use `set_pag_applied_layers` method to update these layers after the pipeline has been created. Check out the [pag_applied_layers](#pag_applied_layers) section to learn more about applying PAG to other layers.
If you already have a pipeline created and loaded, you can enable PAG on it using the `from_pipe` API with the `enable_pag` flag. Internally, a PAG pipeline is created based on the pipeline and task you specified. In the example below, since we used `AutoPipelineForText2Image` and passed a `StableDiffusionXLPipeline`, a `StableDiffusionXLPAGPipeline` is created accordingly. Note that this does not require additional memory, and you will have both `StableDiffusionXLPipeline` and `StableDiffusionXLPAGPipeline` loaded and ready to use. You can read more about the `from_pipe` API and how to reuse pipelines in diffuser [here](https://huggingface.co/docs/diffusers/using-diffusers/loading#reuse-a-pipeline).
```py
pipeline_sdxl = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
pipeline = AutoPipelineForText2Image.from_pipe(pipeline_sdxl, enable_pag=True)
```
To generate an image, you will also need to pass a `pag_scale`. When `pag_scale` increases, images gain more semantically coherent structures and exhibit fewer artifacts. However overly large guidance scale can lead to smoother textures and slight saturation in the images, similarly to CFG. `pag_scale=3.0` is used in the official demo and works well in most of the use cases, but feel free to experiment and select the appropriate value according to your needs! PAG is disabled when `pag_scale=0`.
```py
prompt = "an insect robot preparing a delicious meal, anime style"
for pag_scale in [0.0, 3.0]:
generator = torch.Generator(device="cpu").manual_seed(0)
images = pipeline(
prompt=prompt,
num_inference_steps=25,
guidance_scale=7.0,
generator=generator,
pag_scale=pag_scale,
).images
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_0.0_cfg_7.0_mid.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image without PAG</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_3.0_cfg_7.0_mid.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image with PAG</figcaption>
</div>
</div>
</hfoption>
<hfoption id="Image-to-image">
You can use PAG with image-to-image pipelines.
```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
import torch
pipeline = AutoPipelineForImage2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
enable_pag=True,
pag_applied_layers=["mid"],
torch_dtype=torch.float16
)
pipeline.enable_model_cpu_offload()
```
If you already have a image-to-image pipeline and would like enable PAG on it, you can run this
```py
pipeline_t2i = AutoPipelineForImage2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_t2i, enable_pag=True)
```
It is also very easy to directly switch from a text-to-image pipeline to PAG enabled image-to-image pipeline
```py
pipeline_pag = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_t2i, enable_pag=True)
```
If you have a PAG enabled text-to-image pipeline, you can directly switch to a image-to-image pipeline with PAG still enabled
```py
pipeline_pag = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", enable_pag=True, torch_dtype=torch.float16)
pipeline = AutoPipelineForImage2Image.from_pipe(pipeline_t2i)
```
Now let's generate an image!
```py
pag_scales = 4.0
guidance_scales = 7.0
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-text2img.png"
init_image = load_image(url)
prompt = "a dog catching a frisbee in the jungle"
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipeline(
prompt,
image=init_image,
strength=0.8,
guidance_scale=guidance_scale,
pag_scale=pag_scale,
generator=generator).images[0]
```
</hfoption>
<hfoption id="Inpainting">
```py
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
import torch
pipeline = AutoPipelineForInpainting.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
enable_pag=True,
torch_dtype=torch.float16
)
pipeline.enable_model_cpu_offload()
```
You can enable PAG on an existing inpainting pipeline like this
```py
pipeline_inpaint = AutoPipelineForInpainting.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
pipeline = AutoPipelineForInpainting.from_pipe(pipeline_inpaint, enable_pag=True)
```
This still works when your pipeline has a different task:
```py
pipeline_t2i = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16)
pipeline = AutoPipelineForInpaiting.from_pipe(pipeline_t2i, enable_pag=True)
```
Let's generate an image!
```py
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = load_image(img_url).convert("RGB")
mask_image = load_image(mask_url).convert("RGB")
prompt = "A majestic tiger sitting on a bench"
pag_scales = 3.0
guidance_scales = 7.5
generator = torch.Generator(device="cpu").manual_seed(1)
images = pipeline(
prompt=prompt,
image=init_image,
mask_image=mask_image,
strength=0.8,
num_inference_steps=50,
guidance_scale=guidance_scale,
generator=generator,
pag_scale=pag_scale,
).images
images[0]
```
</hfoption>
</hfoptions>
## PAG with ControlNet
To use PAG with ControlNet, first create a `controlnet`. Then, pass the `controlnet` and other PAG arguments to the `from_pretrained` method of the AutoPipeline for the specified task.
```py
from diffusers import AutoPipelineForText2Image, ControlNetModel
import torch
controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
)
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlnet=controlnet,
enable_pag=True,
pag_applied_layers="mid",
torch_dtype=torch.float16
)
pipeline.enable_model_cpu_offload()
```
<Tip>
If you already have a controlnet pipeline and want to enable PAG, you can use the `from_pipe` API: `AutoPipelineForText2Image.from_pipe(pipeline_controlnet, enable_pag=True)`
</Tip>
You can use the pipeline in the same way you normally use ControlNet pipelines, with the added option to specify a `pag_scale` parameter. Note that PAG works well for unconditional generation. In this example, we will generate an image without a prompt.
```py
from diffusers.utils import load_image
canny_image = load_image(
"https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_control_input.png"
)
for pag_scale in [0.0, 3.0]:
generator = torch.Generator(device="cpu").manual_seed(1)
images = pipeline(
prompt="",
controlnet_conditioning_scale=controlnet_conditioning_scale,
image=canny_image,
num_inference_steps=50,
guidance_scale=0,
generator=generator,
pag_scale=pag_scale,
).images
images[0]
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_0.0_controlnet.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image without PAG</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_3.0_controlnet.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image with PAG</figcaption>
</div>
</div>
## PAG with IP-Adapter
[IP-Adapter](https://hf.co/papers/2308.06721) is a popular model that can be plugged into diffusion models to enable image prompting without any changes to the underlying model. You can enable PAG on a pipeline with IP-Adapter loaded.
```py
from diffusers import AutoPipelineForText2Image
from diffusers.utils import load_image
from transformers import CLIPVisionModelWithProjection
import torch
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter",
subfolder="models/image_encoder",
torch_dtype=torch.float16
)
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
image_encoder=image_encoder,
enable_pag=True,
torch_dtype=torch.float16
).to("cuda")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter-plus_sdxl_vit-h.bin")
pag_scales = 5.0
ip_adapter_scales = 0.8
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_adapter_diner.png")
pipeline.set_ip_adapter_scale(ip_adapter_scale)
generator = torch.Generator(device="cpu").manual_seed(0)
images = pipeline(
prompt="a polar bear sitting in a chair drinking a milkshake",
ip_adapter_image=image,
negative_prompt="deformed, ugly, wrong proportion, low res, bad anatomy, worst quality, low quality",
num_inference_steps=25,
guidance_scale=3.0,
generator=generator,
pag_scale=pag_scale,
).images
images[0]
```
PAG reduces artifacts and improves the overall compposition.
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_0.0_ipa_0.8.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image without PAG</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_5.0_ipa_0.8.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image with PAG</figcaption>
</div>
</div>
## Configure parameters
### pag_applied_layers
The `pag_applied_layers` argument allows you to specify which layers PAG is applied to. By default, it applies only to the mid blocks. Changing this setting will significantly impact the output. You can use the `set_pag_applied_layers` method to adjust the PAG layers after the pipeline is created, helping you find the optimal layers for your model.
As an example, here is the images generated with `pag_layers = ["down.block_2"]` and `pag_layers = ["down.block_2", "up.block_1.attentions_0"]`
```py
prompt = "an insect robot preparing a delicious meal, anime style"
pipeline.set_pag_applied_layers(pag_layers)
generator = torch.Generator(device="cpu").manual_seed(0)
images = pipeline(
prompt=prompt,
num_inference_steps=25,
guidance_scale=guidance_scale,
generator=generator,
pag_scale=pag_scale,
).images
images[0]
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_3.0_cfg_7.0_down2_up1a0.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">down.block_2 + up.block1.attentions_0</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/pag_3.0_cfg_7.0_down2.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">down.block_2</figcaption>
</div>
</div>
| diffusers/docs/source/en/using-diffusers/pag.md/0 | {
"file_path": "diffusers/docs/source/en/using-diffusers/pag.md",
"repo_id": "diffusers",
"token_count": 5179
} | 131 |
- sections:
- local: index
title: 🧨 Diffusers
- local: quicktour
title: クイックツアー
- local: stable_diffusion
title: 有効で効率の良い拡散モデル
- local: installation
title: インストール
title: はじめに
- sections:
- local: tutorials/tutorial_overview
title: 概要
- local: tutorials/autopipeline
title: AutoPipeline
title: チュートリアル | diffusers/docs/source/ja/_toctree.yml/0 | {
"file_path": "diffusers/docs/source/ja/_toctree.yml",
"repo_id": "diffusers",
"token_count": 166
} | 132 |
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# Core ML로 Stable Diffusion을 실행하는 방법
[Core ML](https://developer.apple.com/documentation/coreml)은 Apple 프레임워크에서 지원하는 모델 형식 및 머신 러닝 라이브러리입니다. macOS 또는 iOS/iPadOS 앱 내에서 Stable Diffusion 모델을 실행하는 데 관심이 있는 경우, 이 가이드에서는 기존 PyTorch 체크포인트를 Core ML 형식으로 변환하고 이를 Python 또는 Swift로 추론에 사용하는 방법을 설명합니다.
Core ML 모델은 Apple 기기에서 사용할 수 있는 모든 컴퓨팅 엔진들, 즉 CPU, GPU, Apple Neural Engine(또는 Apple Silicon Mac 및 최신 iPhone/iPad에서 사용할 수 있는 텐서 최적화 가속기인 ANE)을 활용할 수 있습니다. 모델과 실행 중인 기기에 따라 Core ML은 컴퓨팅 엔진도 혼합하여 사용할 수 있으므로, 예를 들어 모델의 일부가 CPU에서 실행되는 반면 다른 부분은 GPU에서 실행될 수 있습니다.
<Tip>
PyTorch에 내장된 `mps` 가속기를 사용하여 Apple Silicon Macs에서 `diffusers` Python 코드베이스를 실행할 수도 있습니다. 이 방법은 [mps 가이드]에 자세히 설명되어 있지만 네이티브 앱과 호환되지 않습니다.
</Tip>
## Stable Diffusion Core ML 체크포인트
Stable Diffusion 가중치(또는 체크포인트)는 PyTorch 형식으로 저장되기 때문에 네이티브 앱에서 사용하기 위해서는 Core ML 형식으로 변환해야 합니다.
다행히도 Apple 엔지니어들이 `diffusers`를 기반으로 한 [변환 툴](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml)을 개발하여 PyTorch 체크포인트를 Core ML로 변환할 수 있습니다.
모델을 변환하기 전에 잠시 시간을 내어 Hugging Face Hub를 살펴보세요. 관심 있는 모델이 이미 Core ML 형식으로 제공되고 있을 가능성이 높습니다:
- [Apple](https://huggingface.co/apple) organization에는 Stable Diffusion 버전 1.4, 1.5, 2.0 base 및 2.1 base가 포함되어 있습니다.
- [coreml](https://huggingface.co/coreml) organization에는 커스텀 DreamBooth가 적용되거나, 파인튜닝된 모델이 포함되어 있습니다.
- 이 [필터](https://huggingface.co/models?pipeline_tag=text-to-image&library=coreml&p=2&sort=likes)를 사용하여 사용 가능한 모든 Core ML 체크포인트들을 반환합니다.
원하는 모델을 찾을 수 없는 경우 Apple의 [모델을 Core ML로 변환하기](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml) 지침을 따르는 것이 좋습니다.
## 사용할 Core ML 변형(Variant) 선택하기
Stable Diffusion 모델은 다양한 목적에 따라 다른 Core ML 변형으로 변환할 수 있습니다:
- 사용되는 어텐션 블록 유형. 어텐션 연산은 이미지 표현의 여러 영역 간의 관계에 '주의를 기울이고' 이미지와 텍스트 표현이 어떻게 연관되어 있는지 이해하는 데 사용됩니다. 어텐션 연산은 컴퓨팅 및 메모리 집약적이므로 다양한 장치의 하드웨어 특성을 고려한 다양한 구현이 존재합니다. Core ML Stable Diffusion 모델의 경우 두 가지 주의 변형이 있습니다:
* `split_einsum` ([Apple에서 도입](https://machinelearning.apple.com/research/neural-engine-transformers)은 최신 iPhone, iPad 및 M 시리즈 컴퓨터에서 사용할 수 있는 ANE 장치에 최적화되어 있습니다.
* "원본" 어텐션(`diffusers`에 사용되는 기본 구현)는 CPU/GPU와만 호환되며 ANE와는 호환되지 않습니다. "원본" 어텐션을 사용하여 CPU + GPU에서 모델을 실행하는 것이 ANE보다 *더* 빠를 수 있습니다. 자세한 내용은 [이 성능 벤치마크](https://huggingface.co/blog/fast-mac-diffusers#performance-benchmarks)와 커뮤니티에서 제공하는 일부 [추가 측정](https://github.com/huggingface/swift-coreml-diffusers/issues/31)을 참조하십시오.
- 지원되는 추론 프레임워크
* `packages`는 Python 추론에 적합합니다. 네이티브 앱에 통합하기 전에 변환된 Core ML 모델을 테스트하거나, Core ML 성능을 알고 싶지만 네이티브 앱을 지원할 필요는 없는 경우에 사용할 수 있습니다. 예를 들어, 웹 UI가 있는 애플리케이션은 Python Core ML 백엔드를 완벽하게 사용할 수 있습니다.
* Swift 코드에는 `컴파일된` 모델이 필요합니다. Hub의 `컴파일된` 모델은 iOS 및 iPadOS 기기와의 호환성을 위해 큰 UNet 모델 가중치를 여러 파일로 분할합니다. 이는 [`--chunk-unet` 변환 옵션](https://github.com/apple/ml-stable-diffusion#-converting-models-to-core-ml)에 해당합니다. 네이티브 앱을 지원하려면 `컴파일된` 변형을 선택해야 합니다.
공식 Core ML Stable Diffusion [모델](https://huggingface.co/apple/coreml-stable-diffusion-v1-4/tree/main)에는 이러한 변형이 포함되어 있지만 커뮤니티 버전은 다를 수 있습니다:
```
coreml-stable-diffusion-v1-4
├── README.md
├── original
│ ├── compiled
│ └── packages
└── split_einsum
├── compiled
└── packages
```
아래와 같이 필요한 변형을 다운로드하여 사용할 수 있습니다.
## Python에서 Core ML 추론
Python에서 Core ML 추론을 실행하려면 다음 라이브러리를 설치하세요:
```bash
pip install huggingface_hub
pip install git+https://github.com/apple/ml-stable-diffusion
```
### 모델 체크포인트 다운로드하기
`컴파일된` 버전은 Swift와만 호환되므로 Python에서 추론을 실행하려면 `packages` 폴더에 저장된 버전 중 하나를 사용하세요. `원본` 또는 `split_einsum` 어텐션 중 어느 것을 사용할지 선택할 수 있습니다.
다음은 Hub에서 'models'라는 디렉토리로 'original' 어텐션 변형을 다운로드하는 방법입니다:
```Python
from huggingface_hub import snapshot_download
from pathlib import Path
repo_id = "apple/coreml-stable-diffusion-v1-4"
variant = "original/packages"
model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_"))
snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False)
print(f"Model downloaded at {model_path}")
```
### 추론[[python-inference]]
모델의 snapshot을 다운로드한 후에는 Apple의 Python 스크립트를 사용하여 테스트할 수 있습니다.
```shell
python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" -i models/coreml-stable-diffusion-v1-4_original_packages -o </path/to/output/image> --compute-unit CPU_AND_GPU --seed 93
```
`<output-mlpackages-directory>`는 위 단계에서 다운로드한 체크포인트를 가리켜야 하며, `--compute-unit`은 추론을 허용할 하드웨어를 나타냅니다. 이는 다음 옵션 중 하나이어야 합니다: `ALL`, `CPU_AND_GPU`, `CPU_ONLY`, `CPU_AND_NE`. 선택적 출력 경로와 재현성을 위한 시드를 제공할 수도 있습니다.
추론 스크립트에서는 Stable Diffusion 모델의 원래 버전인 `CompVis/stable-diffusion-v1-4`를 사용한다고 가정합니다. 다른 모델을 사용하는 경우 추론 명령줄에서 `--model-version` 옵션을 사용하여 해당 허브 ID를 *지정*해야 합니다. 이는 이미 지원되는 모델과 사용자가 직접 학습하거나 파인튜닝한 사용자 지정 모델에 적용됩니다.
예를 들어, [`stable-diffusion-v1-5/stable-diffusion-v1-5`](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)를 사용하려는 경우입니다:
```shell
python -m python_coreml_stable_diffusion.pipeline --prompt "a photo of an astronaut riding a horse on mars" --compute-unit ALL -o output --seed 93 -i models/coreml-stable-diffusion-v1-5_original_packages --model-version stable-diffusion-v1-5/stable-diffusion-v1-5
```
## Swift에서 Core ML 추론하기
Swift에서 추론을 실행하는 것은 모델이 이미 `mlmodelc` 형식으로 컴파일되어 있기 때문에 Python보다 약간 빠릅니다. 이는 앱이 시작될 때 모델이 불러와지는 것이 눈에 띄지만, 이후 여러 번 실행하면 눈에 띄지 않을 것입니다.
### 다운로드
Mac에서 Swift에서 추론을 실행하려면 `컴파일된` 체크포인트 버전 중 하나가 필요합니다. 이전 예제와 유사하지만 `컴파일된` 변형 중 하나를 사용하여 Python 코드를 로컬로 다운로드하는 것이 좋습니다:
```Python
from huggingface_hub import snapshot_download
from pathlib import Path
repo_id = "apple/coreml-stable-diffusion-v1-4"
variant = "original/compiled"
model_path = Path("./models") / (repo_id.split("/")[-1] + "_" + variant.replace("/", "_"))
snapshot_download(repo_id, allow_patterns=f"{variant}/*", local_dir=model_path, local_dir_use_symlinks=False)
print(f"Model downloaded at {model_path}")
```
### 추론[[swift-inference]]
추론을 실행하기 위해서, Apple의 리포지토리를 복제하세요:
```bash
git clone https://github.com/apple/ml-stable-diffusion
cd ml-stable-diffusion
```
그 다음 Apple의 명령어 도구인 [Swift 패키지 관리자](https://www.swift.org/package-manager/#)를 사용합니다:
```bash
swift run StableDiffusionSample --resource-path models/coreml-stable-diffusion-v1-4_original_compiled --compute-units all "a photo of an astronaut riding a horse on mars"
```
`--resource-path`에 이전 단계에서 다운로드한 체크포인트 중 하나를 지정해야 하므로 확장자가 `.mlmodelc`인 컴파일된 Core ML 번들이 포함되어 있는지 확인하시기 바랍니다. `--compute-units`는 다음 값 중 하나이어야 합니다: `all`, `cpuOnly`, `cpuAndGPU`, `cpuAndNeuralEngine`.
자세한 내용은 [Apple의 리포지토리 안의 지침](https://github.com/apple/ml-stable-diffusion)을 참고하시기 바랍니다.
## 지원되는 Diffusers 기능
Core ML 모델과 추론 코드는 🧨 Diffusers의 많은 기능, 옵션 및 유연성을 지원하지 않습니다. 다음은 유의해야 할 몇 가지 제한 사항입니다:
- Core ML 모델은 추론에만 적합합니다. 학습이나 파인튜닝에는 사용할 수 없습니다.
- Swift에 포팅된 스케줄러는 Stable Diffusion에서 사용하는 기본 스케줄러와 `diffusers` 구현에서 Swift로 포팅한 `DPMSolverMultistepScheduler` 두 개뿐입니다. 이들 중 약 절반의 스텝으로 동일한 품질을 생성하는 `DPMSolverMultistepScheduler`를 사용하는 것이 좋습니다.
- 추론 코드에서 네거티브 프롬프트, classifier-free guidance scale 및 image-to-image 작업을 사용할 수 있습니다. depth guidance, ControlNet, latent upscalers와 같은 고급 기능은 아직 사용할 수 없습니다.
Apple의 [변환 및 추론 리포지토리](https://github.com/apple/ml-stable-diffusion)와 자체 [swift-coreml-diffusers](https://github.com/huggingface/swift-coreml-diffusers) 리포지토리는 다른 개발자들이 구축할 수 있는 기술적인 데모입니다.
누락된 기능이 있다고 생각되면 언제든지 기능을 요청하거나, 더 좋은 방법은 기여 PR을 열어주세요. :)
## 네이티브 Diffusers Swift 앱
자체 Apple 하드웨어에서 Stable Diffusion을 실행하는 쉬운 방법 중 하나는 `diffusers`와 Apple의 변환 및 추론 리포지토리를 기반으로 하는 [자체 오픈 소스 Swift 리포지토리](https://github.com/huggingface/swift-coreml-diffusers)를 사용하는 것입니다. 코드를 공부하고 [Xcode](https://developer.apple.com/xcode/)로 컴파일하여 필요에 맞게 조정할 수 있습니다. 편의를 위해 앱스토어에 [독립형 Mac 앱](https://apps.apple.com/app/diffusers/id1666309574)도 있으므로 코드나 IDE를 다루지 않고도 사용할 수 있습니다. 개발자로서 Core ML이 Stable Diffusion 앱을 구축하는 데 가장 적합한 솔루션이라고 판단했다면, 이 가이드의 나머지 부분을 사용하여 프로젝트를 시작할 수 있습니다. 여러분이 무엇을 빌드할지 기대됩니다. :) | diffusers/docs/source/ko/optimization/coreml.md/0 | {
"file_path": "diffusers/docs/source/ko/optimization/coreml.md",
"repo_id": "diffusers",
"token_count": 8303
} | 133 |
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# DreamBooth
[DreamBooth](https://huggingface.co/papers/2208.12242)는 한 주제에 대한 적은 이미지(3~5개)만으로도 stable diffusion과 같이 text-to-image 모델을 개인화할 수 있는 방법입니다. 이를 통해 모델은 다양한 장면, 포즈 및 장면(뷰)에서 피사체에 대해 맥락화(contextualized)된 이미지를 생성할 수 있습니다.
<small>에서의 Dreambooth 예시 <a href="https://dreambooth.github.io">project's blog.</a></small>
이 가이드는 다양한 GPU, Flax 사양에 대해 [`CompVis/stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) 모델로 DreamBooth를 파인튜닝하는 방법을 보여줍니다. 더 깊이 파고들어 작동 방식을 확인하는 데 관심이 있는 경우, 이 가이드에 사용된 DreamBooth의 모든 학습 스크립트를 [여기](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth)에서 찾을 수 있습니다.
스크립트를 실행하기 전에 라이브러리의 학습에 필요한 dependencies를 설치해야 합니다. 또한 `main` GitHub 브랜치에서 🧨 Diffusers를 설치하는 것이 좋습니다.
```bash
pip install git+https://github.com/huggingface/diffusers
pip install -U -r diffusers/examples/dreambooth/requirements.txt
```
xFormers는 학습에 필요한 요구 사항은 아니지만, 가능하면 [설치](../optimization/xformers)하는 것이 좋습니다. 학습 속도를 높이고 메모리 사용량을 줄일 수 있기 때문입니다.
모든 dependencies을 설정한 후 다음을 사용하여 [🤗 Accelerate](https://github.com/huggingface/accelerate/) 환경을 다음과 같이 초기화합니다:
```bash
accelerate config
```
별도 설정 없이 기본 🤗 Accelerate 환경을 설치하려면 다음을 실행합니다:
```bash
accelerate config default
```
또는 현재 환경이 노트북과 같은 대화형 셸을 지원하지 않는 경우 다음을 사용할 수 있습니다:
```py
from accelerate.utils import write_basic_config
write_basic_config()
```
## 파인튜닝
<Tip warning={true}>
DreamBooth 파인튜닝은 하이퍼파라미터에 매우 민감하고 과적합되기 쉽습니다. 적절한 하이퍼파라미터를 선택하는 데 도움이 되도록 다양한 권장 설정이 포함된 [심층 분석](https://huggingface.co/blog/dreambooth)을 살펴보는 것이 좋습니다.
</Tip>
<frameworkcontent>
<pt>
[몇 장의 강아지 이미지들](https://drive.google.com/drive/folders/1BO_dyz-p65qhBRRMRA4TbZ8qW4rB99JZ)로 DreamBooth를 시도해봅시다.
이를 다운로드해 디렉터리에 저장한 다음 `INSTANCE_DIR` 환경 변수를 해당 경로로 설정합니다:
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export OUTPUT_DIR="path_to_saved_model"
```
그런 다음, 다음 명령을 사용하여 학습 스크립트를 실행할 수 있습니다 (전체 학습 스크립트는 [여기](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py)에서 찾을 수 있습니다):
```bash
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--max_train_steps=400
```
</pt>
<jax>
TPU에 액세스할 수 있거나 더 빠르게 훈련하고 싶다면 [Flax 학습 스크립트](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_flax.py)를 사용해 볼 수 있습니다. Flax 학습 스크립트는 gradient checkpointing 또는 gradient accumulation을 지원하지 않으므로, 메모리가 30GB 이상인 GPU가 필요합니다.
스크립트를 실행하기 전에 요구 사항이 설치되어 있는지 확인하십시오.
```bash
pip install -U -r requirements.txt
```
그러면 다음 명령어로 학습 스크립트를 실행시킬 수 있습니다:
```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="path-to-instance-images"
export OUTPUT_DIR="path-to-save-model"
python train_dreambooth_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--output_dir=$OUTPUT_DIR \
--instance_prompt="a photo of sks dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=5e-6 \
--max_train_steps=400
```
</jax>
</frameworkcontent>
### Prior-preserving(사전 보존) loss를 사용한 파인튜닝
과적합과 language drift를 방지하기 위해 사전 보존이 사용됩니다(관심이 있는 경우 [논문](https://huggingface.co/papers/2208.12242)을 참조하세요). 사전 보존을 위해 동일한 클래스의 다른 이미지를 학습 프로세스의 일부로 사용합니다. 좋은 점은 Stable Diffusion 모델 자체를 사용하여 이러한 이미지를 생성할 수 있다는 것입니다! 학습 스크립트는 생성된 이미지를 우리가 지정한 로컬 경로에 저장합니다.
저자들에 따르면 사전 보존을 위해 `num_epochs * num_samples`개의 이미지를 생성하는 것이 좋습니다. 200-300개에서 대부분 잘 작동합니다.
<frameworkcontent>
<pt>
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
</pt>
<jax>
```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
python train_dreambooth_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=5e-6 \
--num_class_images=200 \
--max_train_steps=800
```
</jax>
</frameworkcontent>
## 텍스트 인코더와 and UNet로 파인튜닝하기
해당 스크립트를 사용하면 `unet`과 함께 `text_encoder`를 파인튜닝할 수 있습니다. 실험에서(자세한 내용은 [🧨 Diffusers를 사용해 DreamBooth로 Stable Diffusion 학습하기](https://huggingface.co/blog/dreambooth) 게시물을 확인하세요), 특히 얼굴 이미지를 생성할 때 훨씬 더 나은 결과를 얻을 수 있습니다.
<Tip warning={true}>
텍스트 인코더를 학습시키려면 추가 메모리가 필요해 16GB GPU로는 동작하지 않습니다. 이 옵션을 사용하려면 최소 24GB VRAM이 필요합니다.
</Tip>
`--train_text_encoder` 인수를 학습 스크립트에 전달하여 `text_encoder` 및 `unet`을 파인튜닝할 수 있습니다:
<frameworkcontent>
<pt>
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_text_encoder \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--use_8bit_adam
--gradient_checkpointing \
--learning_rate=2e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
</pt>
<jax>
```bash
export MODEL_NAME="duongna/stable-diffusion-v1-4-flax"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
python train_dreambooth_flax.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--train_text_encoder \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--learning_rate=2e-6 \
--num_class_images=200 \
--max_train_steps=800
```
</jax>
</frameworkcontent>
## LoRA로 파인튜닝하기
DreamBooth에서 대규모 모델의 학습을 가속화하기 위한 파인튜닝 기술인 LoRA(Low-Rank Adaptation of Large Language Models)를 사용할 수 있습니다. 자세한 내용은 [LoRA 학습](training/lora#dreambooth) 가이드를 참조하세요.
### 학습 중 체크포인트 저장하기
Dreambooth로 훈련하는 동안 과적합하기 쉬우므로, 때때로 학습 중에 정기적인 체크포인트를 저장하는 것이 유용합니다. 중간 체크포인트 중 하나가 최종 모델보다 더 잘 작동할 수 있습니다! 체크포인트 저장 기능을 활성화하려면 학습 스크립트에 다음 인수를 전달해야 합니다:
```bash
--checkpointing_steps=500
```
이렇게 하면 `output_dir`의 하위 폴더에 전체 학습 상태가 저장됩니다. 하위 폴더 이름은 접두사 `checkpoint-`로 시작하고 지금까지 수행된 step 수입니다. 예시로 `checkpoint-1500`은 1500 학습 step 후에 저장된 체크포인트입니다.
#### 저장된 체크포인트에서 훈련 재개하기
저장된 체크포인트에서 훈련을 재개하려면, `--resume_from_checkpoint` 인수를 전달한 다음 사용할 체크포인트의 이름을 지정하면 됩니다. 특수 문자열 `"latest"`를 사용하여 저장된 마지막 체크포인트(즉, step 수가 가장 많은 체크포인트)에서 재개할 수도 있습니다. 예를 들어 다음은 1500 step 후에 저장된 체크포인트에서부터 학습을 재개합니다:
```bash
--resume_from_checkpoint="checkpoint-1500"
```
원하는 경우 일부 하이퍼파라미터를 조정할 수 있습니다.
#### 저장된 체크포인트를 사용하여 추론 수행하기
저장된 체크포인트는 훈련 재개에 적합한 형식으로 저장됩니다. 여기에는 모델 가중치뿐만 아니라 옵티마이저, 데이터 로더 및 학습률의 상태도 포함됩니다.
**`"accelerate>=0.16.0"`**이 설치된 경우 다음 코드를 사용하여 중간 체크포인트에서 추론을 실행합니다.
```python
from diffusers import DiffusionPipeline, UNet2DConditionModel
from transformers import CLIPTextModel
import torch
# 학습에 사용된 것과 동일한 인수(model, revision)로 파이프라인을 불러옵니다.
model_id = "CompVis/stable-diffusion-v1-4"
unet = UNet2DConditionModel.from_pretrained("/sddata/dreambooth/daruma-v2-1/checkpoint-100/unet")
# `args.train_text_encoder`로 학습한 경우면 텍스트 인코더를 꼭 불러오세요
text_encoder = CLIPTextModel.from_pretrained("/sddata/dreambooth/daruma-v2-1/checkpoint-100/text_encoder")
pipeline = DiffusionPipeline.from_pretrained(model_id, unet=unet, text_encoder=text_encoder, dtype=torch.float16)
pipeline.to("cuda")
# 추론을 수행하거나 저장하거나, 허브에 푸시합니다.
pipeline.save_pretrained("dreambooth-pipeline")
```
If you have **`"accelerate<0.16.0"`** installed, you need to convert it to an inference pipeline first:
```python
from accelerate import Accelerator
from diffusers import DiffusionPipeline
# 학습에 사용된 것과 동일한 인수(model, revision)로 파이프라인을 불러옵니다.
model_id = "CompVis/stable-diffusion-v1-4"
pipeline = DiffusionPipeline.from_pretrained(model_id)
accelerator = Accelerator()
# 초기 학습에 `--train_text_encoder`가 사용된 경우 text_encoder를 사용합니다.
unet, text_encoder = accelerator.prepare(pipeline.unet, pipeline.text_encoder)
# 체크포인트 경로로부터 상태를 복원합니다. 여기서는 절대 경로를 사용해야 합니다.
accelerator.load_state("/sddata/dreambooth/daruma-v2-1/checkpoint-100")
# unwrapped 모델로 파이프라인을 다시 빌드합니다.(.unet and .text_encoder로의 할당도 작동해야 합니다)
pipeline = DiffusionPipeline.from_pretrained(
model_id,
unet=accelerator.unwrap_model(unet),
text_encoder=accelerator.unwrap_model(text_encoder),
)
# 추론을 수행하거나 저장하거나, 허브에 푸시합니다.
pipeline.save_pretrained("dreambooth-pipeline")
```
## 각 GPU 용량에서의 최적화
하드웨어에 따라 16GB에서 8GB까지 GPU에서 DreamBooth를 최적화하는 몇 가지 방법이 있습니다!
### xFormers
[xFormers](https://github.com/facebookresearch/xformers)는 Transformers를 최적화하기 위한 toolbox이며, 🧨 Diffusers에서 사용되는[memory-efficient attention](https://facebookresearch.github.io/xformers/components/ops.html#module-xformers.ops) 메커니즘을 포함하고 있습니다. [xFormers를 설치](./optimization/xformers)한 다음 학습 스크립트에 다음 인수를 추가합니다:
```bash
--enable_xformers_memory_efficient_attention
```
xFormers는 Flax에서 사용할 수 없습니다.
### 그래디언트 없음으로 설정
메모리 사용량을 줄일 수 있는 또 다른 방법은 [기울기 설정](https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html)을 0 대신 `None`으로 하는 것입니다. 그러나 이로 인해 특정 동작이 변경될 수 있으므로 문제가 발생하면 이 인수를 제거해 보십시오. 학습 스크립트에 다음 인수를 추가하여 그래디언트를 `None`으로 설정합니다.
```bash
--set_grads_to_none
```
### 16GB GPU
Gradient checkpointing과 [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)의 8비트 옵티마이저의 도움으로, 16GB GPU에서 dreambooth를 훈련할 수 있습니다. bitsandbytes가 설치되어 있는지 확인하세요:
```bash
pip install bitsandbytes
```
그 다음, 학습 스크립트에 `--use_8bit_adam` 옵션을 명시합니다:
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=2 --gradient_checkpointing \
--use_8bit_adam \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
### 12GB GPU
12GB GPU에서 DreamBooth를 실행하려면 gradient checkpointing, 8비트 옵티마이저, xFormers를 활성화하고 그래디언트를 `None`으로 설정해야 합니다.
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path-to-instance-images"
export CLASS_DIR="path-to-class-images"
export OUTPUT_DIR="path-to-save-model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--gradient_accumulation_steps=1 --gradient_checkpointing \
--use_8bit_adam \
--enable_xformers_memory_efficient_attention \
--set_grads_to_none \
--learning_rate=2e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800
```
### 8GB GPU에서 학습하기
8GB GPU에 대해서는 [DeepSpeed](https://www.deepspeed.ai/)를 사용해 일부 텐서를 VRAM에서 CPU 또는 NVME로 오프로드하여 더 적은 GPU 메모리로 학습할 수도 있습니다.
🤗 Accelerate 환경을 구성하려면 다음 명령을 실행하세요:
```bash
accelerate config
```
환경 구성 중에 DeepSpeed를 사용할 것을 확인하세요.
그러면 DeepSpeed stage 2, fp16 혼합 정밀도를 결합하고 모델 매개변수와 옵티마이저 상태를 모두 CPU로 오프로드하면 8GB VRAM 미만에서 학습할 수 있습니다.
단점은 더 많은 시스템 RAM(약 25GB)이 필요하다는 것입니다. 추가 구성 옵션은 [DeepSpeed 문서](https://huggingface.co/docs/accelerate/usage_guides/deepspeed)를 참조하세요.
또한 기본 Adam 옵티마이저를 DeepSpeed의 최적화된 Adam 버전으로 변경해야 합니다.
이는 상당한 속도 향상을 위한 Adam인 [`deepspeed.ops.adam.DeepSpeedCPUAdam`](https://deepspeed.readthedocs.io/en/latest/optimizers.html#adam-cpu)입니다.
`DeepSpeedCPUAdam`을 활성화하려면 시스템의 CUDA toolchain 버전이 PyTorch와 함께 설치된 것과 동일해야 합니다.
8비트 옵티마이저는 현재 DeepSpeed와 호환되지 않는 것 같습니다.
다음 명령으로 학습을 시작합니다:
```bash
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="path_to_training_images"
export CLASS_DIR="path_to_class_images"
export OUTPUT_DIR="path_to_saved_model"
accelerate launch train_dreambooth.py \
--pretrained_model_name_or_path=$MODEL_NAME \
--instance_data_dir=$INSTANCE_DIR \
--class_data_dir=$CLASS_DIR \
--output_dir=$OUTPUT_DIR \
--with_prior_preservation --prior_loss_weight=1.0 \
--instance_prompt="a photo of sks dog" \
--class_prompt="a photo of dog" \
--resolution=512 \
--train_batch_size=1 \
--sample_batch_size=1 \
--gradient_accumulation_steps=1 --gradient_checkpointing \
--learning_rate=5e-6 \
--lr_scheduler="constant" \
--lr_warmup_steps=0 \
--num_class_images=200 \
--max_train_steps=800 \
--mixed_precision=fp16
```
## 추론
모델을 학습한 후에는, 모델이 저장된 경로를 지정해 [`StableDiffusionPipeline`]로 추론을 수행할 수 있습니다. 프롬프트에 학습에 사용된 특수 `식별자`(이전 예시의 `sks`)가 포함되어 있는지 확인하세요.
**`"accelerate>=0.16.0"`**이 설치되어 있는 경우 다음 코드를 사용하여 중간 체크포인트에서 추론을 실행할 수 있습니다:
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "path_to_saved_model"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
prompt = "A photo of sks dog in a bucket"
image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
image.save("dog-bucket.png")
```
[저장된 학습 체크포인트](#inference-from-a-saved-checkpoint)에서도 추론을 실행할 수도 있습니다.
| diffusers/docs/source/ko/training/dreambooth.md/0 | {
"file_path": "diffusers/docs/source/ko/training/dreambooth.md",
"repo_id": "diffusers",
"token_count": 11696
} | 134 |
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# Kandinsky
[[open-in-colab]]
Kandinsky 모델은 일련의 다국어 text-to-image 생성 모델입니다. Kandinsky 2.0 모델은 두 개의 다국어 텍스트 인코더를 사용하고 그 결과를 연결해 UNet에 사용됩니다.
[Kandinsky 2.1](../api/pipelines/kandinsky)은 텍스트와 이미지 임베딩 간의 매핑을 생성하는 image prior 모델([`CLIP`](https://huggingface.co/docs/transformers/model_doc/clip))을 포함하도록 아키텍처를 변경했습니다. 이 매핑은 더 나은 text-image alignment를 제공하며, 학습 중에 텍스트 임베딩과 함께 사용되어 더 높은 품질의 결과를 가져옵니다. 마지막으로, Kandinsky 2.1은 spatial conditional 정규화 레이어를 추가하여 사실감을 높여주는 [Modulating Quantized Vectors (MoVQ)](https://huggingface.co/papers/2209.09002) 디코더를 사용하여 latents를 이미지로 디코딩합니다.
[Kandinsky 2.2](../api/pipelines/kandinsky_v22)는 image prior 모델의 이미지 인코더를 더 큰 CLIP-ViT-G 모델로 교체하여 품질을 개선함으로써 이전 모델을 개선했습니다. 또한 image prior 모델은 해상도와 종횡비가 다른 이미지로 재훈련되어 더 높은 해상도의 이미지와 다양한 이미지 크기를 생성합니다.
[Kandinsky 3](../api/pipelines/kandinsky3)는 아키텍처를 단순화하고 prior 모델과 diffusion 모델을 포함하는 2단계 생성 프로세스에서 벗어나고 있습니다. 대신, Kandinsky 3는 [Flan-UL2](https://huggingface.co/google/flan-ul2)를 사용하여 텍스트를 인코딩하고, [BigGan-deep](https://hf.co/papers/1809.11096) 블록이 포함된 UNet을 사용하며, [Sber-MoVQGAN](https://github.com/ai-forever/MoVQGAN)을 사용하여 latents를 이미지로 디코딩합니다. 텍스트 이해와 생성된 이미지 품질은 주로 더 큰 텍스트 인코더와 UNet을 사용함으로써 달성됩니다.
이 가이드에서는 text-to-image, image-to-image, 인페인팅, 보간 등을 위해 Kandinsky 모델을 사용하는 방법을 설명합니다.
시작하기 전에 다음 라이브러리가 설치되어 있는지 확인하세요:
```py
# Colab에서 필요한 라이브러리를 설치하기 위해 주석을 제외하세요
#!pip install -q diffusers transformers accelerate
```
<Tip warning={true}>
Kandinsky 2.1과 2.2의 사용법은 매우 유사합니다! 유일한 차이점은 Kandinsky 2.2는 latents를 디코딩할 때 `프롬프트`를 입력으로 받지 않는다는 것입니다. 대신, Kandinsky 2.2는 디코딩 중에는 `image_embeds`만 받아들입니다.
<br>
Kandinsky 3는 더 간결한 아키텍처를 가지고 있으며 prior 모델이 필요하지 않습니다. 즉, [Stable Diffusion XL](sdxl)과 같은 다른 diffusion 모델과 사용법이 동일합니다.
</Tip>
## Text-to-image
모든 작업에 Kandinsky 모델을 사용하려면 항상 프롬프트를 인코딩하고 이미지 임베딩을 생성하는 prior 파이프라인을 설정하는 것부터 시작해야 합니다. 이전 파이프라인은 negative 프롬프트 `""`에 해당하는 `negative_image_embeds`도 생성합니다. 더 나은 결과를 얻으려면 이전 파이프라인에 실제 `negative_prompt`를 전달할 수 있지만, 이렇게 하면 prior 파이프라인의 유효 배치 크기가 2배로 증가합니다.
<hfoptions id="text-to-image">
<hfoption id="Kandinsky 2.1">
```py
from diffusers import KandinskyPriorPipeline, KandinskyPipeline
import torch
prior_pipeline = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16).to("cuda")
pipeline = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16).to("cuda")
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
negative_prompt = "low quality, bad quality" # negative 프롬프트 포함은 선택적이지만, 보통 결과는 더 좋습니다
image_embeds, negative_image_embeds = prior_pipeline(prompt, negative_prompt, guidance_scale=1.0).to_tuple()
```
이제 모든 프롬프트와 임베딩을 [`KandinskyPipeline`]에 전달하여 이미지를 생성합니다:
```py
image = pipeline(prompt, image_embeds=image_embeds, negative_prompt=negative_prompt, negative_image_embeds=negative_image_embeds, height=768, width=768).images[0]
image
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/cheeseburger.png"/>
</div>
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline
import torch
prior_pipeline = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16).to("cuda")
pipeline = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16).to("cuda")
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
negative_prompt = "low quality, bad quality" # negative 프롬프트 포함은 선택적이지만, 보통 결과는 더 좋습니다
image_embeds, negative_image_embeds = prior_pipeline(prompt, guidance_scale=1.0).to_tuple()
```
이미지 생성을 위해 `image_embeds`와 `negative_image_embeds`를 [`KandinskyV22Pipeline`]에 전달합니다:
```py
image = pipeline(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768).images[0]
image
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-text-to-image.png"/>
</div>
</hfoption>
<hfoption id="Kandinsky 3">
Kandinsky 3는 prior 모델이 필요하지 않으므로 [`Kandinsky3Pipeline`]을 직접 불러오고 이미지 생성 프롬프트를 전달할 수 있습니다:
```py
from diffusers import Kandinsky3Pipeline
import torch
pipeline = Kandinsky3Pipeline.from_pretrained("kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
image = pipeline(prompt).images[0]
image
```
</hfoption>
</hfoptions>
🤗 Diffusers는 또한 [`KandinskyCombinedPipeline`] 및 [`KandinskyV22CombinedPipeline`]이 포함된 end-to-end API를 제공하므로 prior 파이프라인과 text-to-image 변환 파이프라인을 별도로 불러올 필요가 없습니다. 결합된 파이프라인은 prior 모델과 디코더를 모두 자동으로 불러옵니다. 원하는 경우 `prior_guidance_scale` 및 `prior_num_inference_steps` 매개 변수를 사용하여 prior 파이프라인에 대해 다른 값을 설정할 수 있습니다.
내부에서 결합된 파이프라인을 자동으로 호출하려면 [`AutoPipelineForText2Image`]를 사용합니다:
<hfoptions id="text-to-image">
<hfoption id="Kandinsky 2.1">
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
negative_prompt = "low quality, bad quality"
image = pipeline(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale=1.0, guidance_scale=4.0, height=768, width=768).images[0]
image
```
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()
prompt = "A alien cheeseburger creature eating itself, claymation, cinematic, moody lighting"
negative_prompt = "low quality, bad quality"
image = pipeline(prompt=prompt, negative_prompt=negative_prompt, prior_guidance_scale=1.0, guidance_scale=4.0, height=768, width=768).images[0]
image
```
</hfoption>
</hfoptions>
## Image-to-image
Image-to-image 경우, 초기 이미지와 텍스트 프롬프트를 전달하여 파이프라인에 이미지를 conditioning합니다. Prior 파이프라인을 불러오는 것으로 시작합니다:
<hfoptions id="image-to-image">
<hfoption id="Kandinsky 2.1">
```py
import torch
from diffusers import KandinskyImg2ImgPipeline, KandinskyPriorPipeline
prior_pipeline = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
pipeline = KandinskyImg2ImgPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
```
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
import torch
from diffusers import KandinskyV22Img2ImgPipeline, KandinskyPriorPipeline
prior_pipeline = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
pipeline = KandinskyV22Img2ImgPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
```
</hfoption>
<hfoption id="Kandinsky 3">
Kandinsky 3는 prior 모델이 필요하지 않으므로 image-to-image 파이프라인을 직접 불러올 수 있습니다:
```py
from diffusers import Kandinsky3Img2ImgPipeline
from diffusers.utils import load_image
import torch
pipeline = Kandinsky3Img2ImgPipeline.from_pretrained("kandinsky-community/kandinsky-3", variant="fp16", torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()
```
</hfoption>
</hfoptions>
Conditioning할 이미지를 다운로드합니다:
```py
from diffusers.utils import load_image
# 이미지 다운로드
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
original_image = load_image(url)
original_image = original_image.resize((768, 512))
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"/>
</div>
Prior 파이프라인으로 `image_embeds`와 `negative_image_embeds`를 생성합니다:
```py
prompt = "A fantasy landscape, Cinematic lighting"
negative_prompt = "low quality, bad quality"
image_embeds, negative_image_embeds = prior_pipeline(prompt, negative_prompt).to_tuple()
```
이제 원본 이미지와 모든 프롬프트 및 임베딩을 파이프라인으로 전달하여 이미지를 생성합니다:
<hfoptions id="image-to-image">
<hfoption id="Kandinsky 2.1">
```py
from diffusers.utils import make_image_grid
image = pipeline(prompt, negative_prompt=negative_prompt, image=original_image, image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768, strength=0.3).images[0]
make_image_grid([original_image.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/img2img_fantasyland.png"/>
</div>
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
from diffusers.utils import make_image_grid
image = pipeline(image=original_image, image_embeds=image_embeds, negative_image_embeds=negative_image_embeds, height=768, width=768, strength=0.3).images[0]
make_image_grid([original_image.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-image-to-image.png"/>
</div>
</hfoption>
<hfoption id="Kandinsky 3">
```py
image = pipeline(prompt, negative_prompt=negative_prompt, image=image, strength=0.75, num_inference_steps=25).images[0]
image
```
</hfoption>
</hfoptions>
또한 🤗 Diffusers에서는 [`KandinskyImg2ImgCombinedPipeline`] 및 [`KandinskyV22Img2ImgCombinedPipeline`]이 포함된 end-to-end API를 제공하므로 prior 파이프라인과 image-to-image 파이프라인을 별도로 불러올 필요가 없습니다. 결합된 파이프라인은 prior 모델과 디코더를 모두 자동으로 불러옵니다. 원하는 경우 `prior_guidance_scale` 및 `prior_num_inference_steps` 매개 변수를 사용하여 이전 파이프라인에 대해 다른 값을 설정할 수 있습니다.
내부에서 결합된 파이프라인을 자동으로 호출하려면 [`AutoPipelineForImage2Image`]를 사용합니다:
<hfoptions id="image-to-image">
<hfoption id="Kandinsky 2.1">
```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import make_image_grid, load_image
import torch
pipeline = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16, use_safetensors=True)
pipeline.enable_model_cpu_offload()
prompt = "A fantasy landscape, Cinematic lighting"
negative_prompt = "low quality, bad quality"
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
original_image = load_image(url)
original_image.thumbnail((768, 768))
image = pipeline(prompt=prompt, negative_prompt=negative_prompt, image=original_image, strength=0.3).images[0]
make_image_grid([original_image.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)
```
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
from diffusers import AutoPipelineForImage2Image
from diffusers.utils import make_image_grid, load_image
import torch
pipeline = AutoPipelineForImage2Image.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16)
pipeline.enable_model_cpu_offload()
prompt = "A fantasy landscape, Cinematic lighting"
negative_prompt = "low quality, bad quality"
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
original_image = load_image(url)
original_image.thumbnail((768, 768))
image = pipeline(prompt=prompt, negative_prompt=negative_prompt, image=original_image, strength=0.3).images[0]
make_image_grid([original_image.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)
```
</hfoption>
</hfoptions>
## Inpainting
<Tip warning={true}>
⚠️ Kandinsky 모델은 이제 검은색 픽셀 대신 ⬜️ **흰색 픽셀**을 사용하여 마스크 영역을 표현합니다. 프로덕션에서 [`KandinskyInpaintPipeline`]을 사용하는 경우 흰색 픽셀을 사용하도록 마스크를 변경해야 합니다:
```py
# PIL 입력에 대해
import PIL.ImageOps
mask = PIL.ImageOps.invert(mask)
# PyTorch와 NumPy 입력에 대해
mask = 1 - mask
```
</Tip>
인페인팅에서는 원본 이미지, 원본 이미지에서 대체할 영역의 마스크, 인페인팅할 내용에 대한 텍스트 프롬프트가 필요합니다. Prior 파이프라인을 불러옵니다:
<hfoptions id="inpaint">
<hfoption id="Kandinsky 2.1">
```py
from diffusers import KandinskyInpaintPipeline, KandinskyPriorPipeline
from diffusers.utils import load_image, make_image_grid
import torch
import numpy as np
from PIL import Image
prior_pipeline = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
pipeline = KandinskyInpaintPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
```
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
from diffusers import KandinskyV22InpaintPipeline, KandinskyV22PriorPipeline
from diffusers.utils import load_image, make_image_grid
import torch
import numpy as np
from PIL import Image
prior_pipeline = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
pipeline = KandinskyV22InpaintPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder-inpaint", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
```
</hfoption>
</hfoptions>
초기 이미지를 불러오고 마스크를 생성합니다:
```py
init_image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png")
mask = np.zeros((768, 768), dtype=np.float32)
# mask area above cat's head
mask[:250, 250:-250] = 1
```
Prior 파이프라인으로 임베딩을 생성합니다:
```py
prompt = "a hat"
prior_output = prior_pipeline(prompt)
```
이제 이미지 생성을 위해 초기 이미지, 마스크, 프롬프트와 임베딩을 파이프라인에 전달합니다:
<hfoptions id="inpaint">
<hfoption id="Kandinsky 2.1">
```py
output_image = pipeline(prompt, image=init_image, mask_image=mask, **prior_output, height=768, width=768, num_inference_steps=150).images[0]
mask = Image.fromarray((mask*255).astype('uint8'), 'L')
make_image_grid([init_image, mask, output_image], rows=1, cols=3)
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/inpaint_cat_hat.png"/>
</div>
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
output_image = pipeline(image=init_image, mask_image=mask, **prior_output, height=768, width=768, num_inference_steps=150).images[0]
mask = Image.fromarray((mask*255).astype('uint8'), 'L')
make_image_grid([init_image, mask, output_image], rows=1, cols=3)
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinskyv22-inpaint.png"/>
</div>
</hfoption>
</hfoptions>
[`KandinskyInpaintCombinedPipeline`] 및 [`KandinskyV22InpaintCombinedPipeline`]을 사용하여 내부에서 prior 및 디코더 파이프라인을 함께 호출할 수 있습니다. 이를 위해 [`AutoPipelineForInpainting`]을 사용합니다:
<hfoptions id="inpaint">
<hfoption id="Kandinsky 2.1">
```py
import torch
import numpy as np
from PIL import Image
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid
pipe = AutoPipelineForInpainting.from_pretrained("kandinsky-community/kandinsky-2-1-inpaint", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
init_image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png")
mask = np.zeros((768, 768), dtype=np.float32)
# 고양이 머리 위 마스크 지역
mask[:250, 250:-250] = 1
prompt = "a hat"
output_image = pipe(prompt=prompt, image=init_image, mask_image=mask).images[0]
mask = Image.fromarray((mask*255).astype('uint8'), 'L')
make_image_grid([init_image, mask, output_image], rows=1, cols=3)
```
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
import torch
import numpy as np
from PIL import Image
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image, make_image_grid
pipe = AutoPipelineForInpainting.from_pretrained("kandinsky-community/kandinsky-2-2-decoder-inpaint", torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
init_image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png")
mask = np.zeros((768, 768), dtype=np.float32)
# 고양이 머리 위 마스크 영역
mask[:250, 250:-250] = 1
prompt = "a hat"
output_image = pipe(prompt=prompt, image=original_image, mask_image=mask).images[0]
mask = Image.fromarray((mask*255).astype('uint8'), 'L')
make_image_grid([init_image, mask, output_image], rows=1, cols=3)
```
</hfoption>
</hfoptions>
## Interpolation (보간)
Interpolation(보간)을 사용하면 이미지와 텍스트 임베딩 사이의 latent space를 탐색할 수 있어 prior 모델의 중간 결과물을 볼 수 있는 멋진 방법입니다. Prior 파이프라인과 보간하려는 두 개의 이미지를 불러옵니다:
<hfoptions id="interpolate">
<hfoption id="Kandinsky 2.1">
```py
from diffusers import KandinskyPriorPipeline, KandinskyPipeline
from diffusers.utils import load_image, make_image_grid
import torch
prior_pipeline = KandinskyPriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-1-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
img_1 = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png")
img_2 = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg")
make_image_grid([img_1.resize((512,512)), img_2.resize((512,512))], rows=1, cols=2)
```
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
from diffusers import KandinskyV22PriorPipeline, KandinskyV22Pipeline
from diffusers.utils import load_image, make_image_grid
import torch
prior_pipeline = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
img_1 = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png")
img_2 = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg")
make_image_grid([img_1.resize((512,512)), img_2.resize((512,512))], rows=1, cols=2)
```
</hfoption>
</hfoptions>
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/cat.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">a cat</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinsky/starry_night.jpeg"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">Van Gogh's Starry Night painting</figcaption>
</div>
</div>
보간할 텍스트 또는 이미지를 지정하고 각 텍스트 또는 이미지에 대한 가중치를 설정합니다. 가중치를 실험하여 보간에 어떤 영향을 미치는지 확인하세요!
```py
images_texts = ["a cat", img_1, img_2]
weights = [0.3, 0.3, 0.4]
```
`interpolate` 함수를 호출하여 임베딩을 생성한 다음, 파이프라인으로 전달하여 이미지를 생성합니다:
<hfoptions id="interpolate">
<hfoption id="Kandinsky 2.1">
```py
# 프롬프트는 빈칸으로 남겨도 됩니다
prompt = ""
prior_out = prior_pipeline.interpolate(images_texts, weights)
pipeline = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
image = pipeline(prompt, **prior_out, height=768, width=768).images[0]
image
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinsky-docs/starry_cat.png"/>
</div>
</hfoption>
<hfoption id="Kandinsky 2.2">
```py
# 프롬프트는 빈칸으로 남겨도 됩니다
prompt = ""
prior_out = prior_pipeline.interpolate(images_texts, weights)
pipeline = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16, use_safetensors=True).to("cuda")
image = pipeline(prompt, **prior_out, height=768, width=768).images[0]
image
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/kandinskyv22-interpolate.png"/>
</div>
</hfoption>
</hfoptions>
## ControlNet
<Tip warning={true}>
⚠️ ControlNet은 Kandinsky 2.2에서만 지원됩니다!
</Tip>
ControlNet을 사용하면 depth map이나 edge detection와 같은 추가 입력을 통해 사전학습된 large diffusion 모델을 conditioning할 수 있습니다. 예를 들어, 모델이 depth map의 구조를 이해하고 보존할 수 있도록 깊이 맵으로 Kandinsky 2.2를 conditioning할 수 있습니다.
이미지를 불러오고 depth map을 추출해 보겠습니다:
```py
from diffusers.utils import load_image
img = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png"
).resize((768, 768))
img
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png"/>
</div>
그런 다음 🤗 Transformers의 `depth-estimation` [`~transformers.Pipeline`]을 사용하여 이미지를 처리해 depth map을 구할 수 있습니다:
```py
import torch
import numpy as np
from transformers import pipeline
def make_hint(image, depth_estimator):
image = depth_estimator(image)["depth"]
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
detected_map = torch.from_numpy(image).float() / 255.0
hint = detected_map.permute(2, 0, 1)
return hint
depth_estimator = pipeline("depth-estimation")
hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
```
### Text-to-image [[controlnet-text-to-image]]
Prior 파이프라인과 [`KandinskyV22ControlnetPipeline`]를 불러옵니다:
```py
from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
prior_pipeline = KandinskyV22PriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
pipeline = KandinskyV22ControlnetPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
).to("cuda")
```
프롬프트와 negative 프롬프트로 이미지 임베딩을 생성합니다:
```py
prompt = "A robot, 4k photo"
negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
generator = torch.Generator(device="cuda").manual_seed(43)
image_emb, zero_image_emb = prior_pipeline(
prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
).to_tuple()
```
마지막으로 이미지 임베딩과 depth 이미지를 [`KandinskyV22ControlnetPipeline`]에 전달하여 이미지를 생성합니다:
```py
image = pipeline(image_embeds=image_emb, negative_image_embeds=zero_image_emb, hint=hint, num_inference_steps=50, generator=generator, height=768, width=768).images[0]
image
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/robot_cat_text2img.png"/>
</div>
### Image-to-image [[controlnet-image-to-image]]
ControlNet을 사용한 image-to-image의 경우, 다음을 사용할 필요가 있습니다:
- [`KandinskyV22PriorEmb2EmbPipeline`]로 텍스트 프롬프트와 이미지에서 이미지 임베딩을 생성합니다.
- [`KandinskyV22ControlnetImg2ImgPipeline`]로 초기 이미지와 이미지 임베딩에서 이미지를 생성합니다.
🤗 Transformers에서 `depth-estimation` [`~transformers.Pipeline`]을 사용하여 고양이의 초기 이미지의 depth map을 처리해 추출합니다:
```py
import torch
import numpy as np
from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
from diffusers.utils import load_image
from transformers import pipeline
img = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/cat.png"
).resize((768, 768))
def make_hint(image, depth_estimator):
image = depth_estimator(image)["depth"]
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
detected_map = torch.from_numpy(image).float() / 255.0
hint = detected_map.permute(2, 0, 1)
return hint
depth_estimator = pipeline("depth-estimation")
hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
```
Prior 파이프라인과 [`KandinskyV22ControlnetImg2ImgPipeline`]을 불러옵니다:
```py
prior_pipeline = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16, use_safetensors=True
).to("cuda")
pipeline = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
).to("cuda")
```
텍스트 프롬프트와 초기 이미지를 이전 파이프라인에 전달하여 이미지 임베딩을 생성합니다:
```py
prompt = "A robot, 4k photo"
negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
generator = torch.Generator(device="cuda").manual_seed(43)
img_emb = prior_pipeline(prompt=prompt, image=img, strength=0.85, generator=generator)
negative_emb = prior_pipeline(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)
```
이제 [`KandinskyV22ControlnetImg2ImgPipeline`]을 실행하여 초기 이미지와 이미지 임베딩으로부터 이미지를 생성할 수 있습니다:
```py
image = pipeline(image=img, strength=0.5, image_embeds=img_emb.image_embeds, negative_image_embeds=negative_emb.image_embeds, hint=hint, num_inference_steps=50, generator=generator, height=768, width=768).images[0]
make_image_grid([img.resize((512, 512)), image.resize((512, 512))], rows=1, cols=2)
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/kandinskyv22/robot_cat.png"/>
</div>
## 최적화
Kandinsky는 mapping을 생성하기 위한 prior 파이프라인과 latents를 이미지로 디코딩하기 위한 두 번째 파이프라인이 필요하다는 점에서 독특합니다. 대부분의 계산이 두 번째 파이프라인에서 이루어지므로 최적화의 노력은 두 번째 파이프라인에 집중되어야 합니다. 다음은 추론 중 Kandinsky키를 개선하기 위한 몇 가지 팁입니다.
1. PyTorch < 2.0을 사용할 경우 [xFormers](../optimization/xformers)을 활성화합니다.
```diff
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
+ pipe.enable_xformers_memory_efficient_attention()
```
2. PyTorch >= 2.0을 사용할 경우 `torch.compile`을 활성화하여 scaled dot-product attention (SDPA)를 자동으로 사용하도록 합니다:
```diff
pipe.unet.to(memory_format=torch.channels_last)
+ pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
```
이는 attention processor를 명시적으로 [`~models.attention_processor.AttnAddedKVProcessor2_0`]을 사용하도록 설정하는 것과 동일합니다:
```py
from diffusers.models.attention_processor import AttnAddedKVProcessor2_0
pipe.unet.set_attn_processor(AttnAddedKVProcessor2_0())
```
3. 메모리 부족 오류를 방지하기 위해 [`~KandinskyPriorPipeline.enable_model_cpu_offload`]를 사용하여 모델을 CPU로 오프로드합니다:
```diff
from diffusers import DiffusionPipeline
import torch
pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", torch_dtype=torch.float16)
+ pipe.enable_model_cpu_offload()
```
4. 기본적으로 text-to-image 파이프라인은 [`DDIMScheduler`]를 사용하지만, [`DDPMScheduler`]와 같은 다른 스케줄러로 대체하여 추론 속도와 이미지 품질 간의 균형에 어떤 영향을 미치는지 확인할 수 있습니다:
```py
from diffusers import DDPMScheduler
from diffusers import DiffusionPipeline
scheduler = DDPMScheduler.from_pretrained("kandinsky-community/kandinsky-2-1", subfolder="ddpm_scheduler")
pipe = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-1", scheduler=scheduler, torch_dtype=torch.float16, use_safetensors=True).to("cuda")
```
| diffusers/docs/source/ko/using-diffusers/kandinsky.md/0 | {
"file_path": "diffusers/docs/source/ko/using-diffusers/kandinsky.md",
"repo_id": "diffusers",
"token_count": 16319
} | 135 |
<!--Copyright 2025 The HuggingFace Team. 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.
-->
# Instalação
🤗 Diffusers é testado no Python 3.8+, PyTorch 1.7.0+, e Flax. Siga as instruções de instalação abaixo para a biblioteca de deep learning que você está utilizando:
- [PyTorch](https://pytorch.org/get-started/locally/) instruções de instalação
- [Flax](https://flax.readthedocs.io/en/latest/) instruções de instalação
## Instalação com pip
Recomenda-se instalar 🤗 Diffusers em um [ambiente virtual](https://docs.python.org/3/library/venv.html).
Se você não está familiarizado com ambiente virtuals, veja o [guia](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
Um ambiente virtual deixa mais fácil gerenciar diferentes projetos e evitar problemas de compatibilidade entre dependências.
Comece criando um ambiente virtual no diretório do projeto:
```bash
python -m venv .env
```
Ative o ambiente virtual:
```bash
source .env/bin/activate
```
Recomenda-se a instalação do 🤗 Transformers porque 🤗 Diffusers depende de seus modelos:
<frameworkcontent>
<pt>
```bash
pip install diffusers["torch"] transformers
```
</pt>
<jax>
```bash
pip install diffusers["flax"] transformers
```
</jax>
</frameworkcontent>
## Instalação a partir do código fonte
Antes da instalação do 🤗 Diffusers a partir do código fonte, certifique-se de ter o PyTorch e o 🤗 Accelerate instalados.
Para instalar o 🤗 Accelerate:
```bash
pip install accelerate
```
então instale o 🤗 Diffusers do código fonte:
```bash
pip install git+https://github.com/huggingface/diffusers
```
Esse comando instala a última versão em desenvolvimento `main` em vez da última versão estável `stable`.
A versão `main` é útil para se manter atualizado com os últimos desenvolvimentos.
Por exemplo, se um bug foi corrigido desde o último lançamento estável, mas um novo lançamento ainda não foi lançado.
No entanto, isso significa que a versão `main` pode não ser sempre estável.
Nós nos esforçamos para manter a versão `main` operacional, e a maioria dos problemas geralmente são resolvidos em algumas horas ou um dia.
Se você encontrar um problema, por favor abra uma [Issue](https://github.com/huggingface/diffusers/issues/new/choose), assim conseguimos arrumar o quanto antes!
## Instalação editável
Você precisará de uma instalação editável se você:
- Usar a versão `main` do código fonte.
- Contribuir para o 🤗 Diffusers e precisa testar mudanças no código.
Clone o repositório e instale o 🤗 Diffusers com os seguintes comandos:
```bash
git clone https://github.com/huggingface/diffusers.git
cd diffusers
```
<frameworkcontent>
<pt>
```bash
pip install -e ".[torch]"
```
</pt>
<jax>
```bash
pip install -e ".[flax]"
```
</jax>
</frameworkcontent>
Esses comandos irá linkar a pasta que você clonou o repositório e os caminhos das suas bibliotecas Python.
Python então irá procurar dentro da pasta que você clonou além dos caminhos normais das bibliotecas.
Por exemplo, se o pacote python for tipicamente instalado no `~/anaconda3/envs/main/lib/python3.10/site-packages/`, o Python também irá procurar na pasta `~/diffusers/` que você clonou.
<Tip warning={true}>
Você deve deixar a pasta `diffusers` se você quiser continuar usando a biblioteca.
</Tip>
Agora você pode facilmente atualizar seu clone para a última versão do 🤗 Diffusers com o seguinte comando:
```bash
cd ~/diffusers/
git pull
```
Seu ambiente Python vai encontrar a versão `main` do 🤗 Diffusers na próxima execução.
## Cache
Os pesos e os arquivos dos modelos são baixados do Hub para o cache que geralmente é o seu diretório home. Você pode mudar a localização do cache especificando as variáveis de ambiente `HF_HOME` ou `HUGGINFACE_HUB_CACHE` ou configurando o parâmetro `cache_dir` em métodos como [`~DiffusionPipeline.from_pretrained`].
Aquivos em cache permitem que você rode 🤗 Diffusers offline. Para prevenir que o 🤗 Diffusers se conecte à internet, defina a variável de ambiente `HF_HUB_OFFLINE` para `True` e o 🤗 Diffusers irá apenas carregar arquivos previamente baixados em cache.
```shell
export HF_HUB_OFFLINE=True
```
Para mais detalhes de como gerenciar e limpar o cache, olhe o guia de [caching](https://huggingface.co/docs/huggingface_hub/guides/manage-cache).
## Telemetria
Nossa biblioteca coleta informações de telemetria durante as requisições [`~DiffusionPipeline.from_pretrained`].
O dado coletado inclui a versão do 🤗 Diffusers e PyTorch/Flax, o modelo ou classe de pipeline requisitado,
e o caminho para um checkpoint pré-treinado se ele estiver hospedado no Hugging Face Hub.
Esse dado de uso nos ajuda a debugar problemas e priorizar novas funcionalidades.
Telemetria é enviada apenas quando é carregado modelos e pipelines do Hub,
e não é coletado se você estiver carregando arquivos locais.
Nos entendemos que nem todo mundo quer compartilhar informações adicionais, e nós respeitamos sua privacidade.
Você pode desabilitar a coleta de telemetria definindo a variável de ambiente `DISABLE_TELEMETRY` do seu terminal:
No Linux/MacOS:
```bash
export DISABLE_TELEMETRY=YES
```
No Windows:
```bash
set DISABLE_TELEMETRY=YES
```
| diffusers/docs/source/pt/installation.md/0 | {
"file_path": "diffusers/docs/source/pt/installation.md",
"repo_id": "diffusers",
"token_count": 2101
} | 136 |
<!--版权 2025 The HuggingFace Team。保留所有权利。
根据 Apache 许可证 2.0 版（"许可证"）授权；除非符合许可证，否则不得使用此文件。您可以在
http://www.apache.org/licenses/LICENSE-2.0
获取许可证的副本。
除非适用法律要求或书面同意，根据许可证分发的软件按"原样"分发，无任何明示或暗示的担保或条件。请参阅许可证了解
特定语言下的权限和限制。
-->
# LoopSequentialPipelineBlocks
[`~modular_pipelines.LoopSequentialPipelineBlocks`] 是一种多块类型，它将其他 [`~modular_pipelines.ModularPipelineBlocks`] 以循环方式组合在一起。数据循环流动，使用 `intermediate_inputs` 和 `intermediate_outputs`，并且每个块都是迭代运行的。这通常用于创建一个默认是迭代的去噪循环。
本指南向您展示如何创建 [`~modular_pipelines.LoopSequentialPipelineBlocks`]。
## 循环包装器
[`~modular_pipelines.LoopSequentialPipelineBlocks`]，也被称为 *循环包装器*，因为它定义了循环结构、迭代变量和配置。在循环包装器内，您需要以下变量。
- `loop_inputs` 是用户提供的值，等同于 [`~modular_pipelines.ModularPipelineBlocks.inputs`]。
- `loop_intermediate_inputs` 是来自 [`~modular_pipelines.PipelineState`] 的中间变量，等同于 [`~modular_pipelines.ModularPipelineBlocks.intermediate_inputs`]。
- `loop_intermediate_outputs` 是由块创建并添加到 [`~modular_pipelines.PipelineState`] 的新中间变量。它等同于 [`~modular_pipelines.ModularPipelineBlocks.intermediate_outputs`]。
- `__call__` 方法定义了循环结构和迭代逻辑。
```py
import torch
from diffusers.modular_pipelines import LoopSequentialPipelineBlocks, ModularPipelineBlocks, InputParam, OutputParam
class LoopWrapper(LoopSequentialPipelineBlocks):
model_name = "test"
@property
def description(self):
return "I'm a loop!!"
@property
def loop_inputs(self):
return [InputParam(name="num_steps")]
@torch.no_grad()
def __call__(self, components, state):
block_state = self.get_block_state(state)
# 循环结构 - 可以根据您的需求定制
for i in range(block_state.num_steps):
# loop_step 按顺序执行所有注册的块
components, block_state = self.loop_step(components, block_state, i=i)
self.set_block_state(state, block_state)
return components, state
```
循环包装器可以传递额外的参数，如当前迭代索引，到循环块。
## 循环块
循环块是一个 [`~modular_pipelines.ModularPipelineBlocks`]，但 `__call__` 方法的行为不同。
- 它从循环包装器。
- 它直接与[`~modular_pipelines.BlockState`]一起工作，而不是[`~modular_pipelines.PipelineState`]。
- 它不需要检索或更新[`~modular_pipelines.BlockState`]。
循环块共享相同的[`~modular_pipelines.BlockState`]，以允许值在循环的每次迭代中累积和变化。
```py
class LoopBlock(ModularPipelineBlocks):
model_name = "test"
@property
def inputs(self):
return [InputParam(name="x")]
@property
def intermediate_outputs(self):
# 这个块产生的输出
return [OutputParam(name="x")]
@property
def description(self):
return "我是一个在`LoopWrapper`类内部使用的块"
def __call__(self, components, block_state, i: int):
block_state.x += 1
return components, block_state
```
## LoopSequentialPipelineBlocks
使用[`~modular_pipelines.LoopSequentialPipelineBlocks.from_blocks_dict`]方法将循环块添加到循环包装器中，以创建[`~modular_pipelines.LoopSequentialPipelineBlocks`]。
```py
loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock})
```
添加更多的循环块以在每次迭代中运行，使用[`~modular_pipelines.LoopSequentialPipelineBlocks.from_blocks_dict`]。这允许您在不改变循环逻辑本身的情况下修改块。
```py
loop = LoopWrapper.from_blocks_dict({"block1": LoopBlock(), "block2": LoopBlock})
```
| diffusers/docs/source/zh/modular_diffusers/loop_sequential_pipeline_blocks.md/0 | {
"file_path": "diffusers/docs/source/zh/modular_diffusers/loop_sequential_pipeline_blocks.md",
"repo_id": "diffusers",
"token_count": 2174
} | 137 |
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