Merge remote-tracking branch 'origin/main' into faster-gemv

Cargo.toml

@@ -1,6 +1,7 @@
 [workspace]
 members = [
     "candle-core",
+    "candle-datasets",
     "candle-examples",
     "candle-nn",
     "candle-pyo3",

README.md

@@ -98,8 +98,9 @@ Cheatsheet:
 - [candle-nn](./candle-nn/): Facilities to build real models
 - [candle-examples](./candle-examples/): Real-world like examples on how to use the library in real settings
 - [candle-kernels](./candle-kernels/): CUDA custom kernels
-
+- [candle-datasets](./candle-datasets/): Datasets and data loaders.
-
+- [candle-transformers](./candle-transformers): Transformer related utilities.
+- [candle-flash-attn](./candle-flash-attn): Flash attention v2 layer.
 
 ## FAQ
 

candle-datasets/Cargo.toml

@@ -0,0 +1,20 @@
+[package]
+name = "candle-datasets"
+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]
+byteorder = { workspace = true }
+candle = { path = "../candle-core", version = "0.1.0", package = "candle-core" }
+candle-nn = { path = "../candle-nn", version = "0.1.0" }
+hf-hub = { workspace = true}
+intel-mkl-src = { workspace = true, optional = true }
+memmap2 = { workspace = true }
+tokenizers = { workspace = true, features = ["onig"] }
+rand = { workspace = true }

candle-datasets/src/lib.rs

@@ -0,0 +1,6 @@
+//! Datasets & Dataloaders for Candle
+pub mod batcher;
+pub mod nlp;
+pub mod vision;
+
+pub use batcher::Batcher;

candle-datasets/src/nlp/mod.rs

@@ -0,0 +1 @@
+pub mod tinystories;

candle-datasets/src/nlp/tinystories.rs

@@ -0,0 +1,122 @@
+//! Helper functions for the tinystories dataset. This uses the pre-tokenized version as generated
+//! by the tools from https://github.com/karpathy/llama2.c
+use candle::{Device, Result, Tensor};
+
+pub struct Dataset {
+    valid_tokens: Vec<memmap2::Mmap>,
+    train_tokens: Vec<memmap2::Mmap>,
+}
+
+fn mmap_file(p: &std::path::PathBuf) -> Result<memmap2::Mmap> {
+    let file = std::fs::File::open(p)?;
+    let mmap = unsafe { memmap2::MmapOptions::new().map(&file)? };
+    Ok(mmap)
+}
+
+impl Dataset {
+    pub fn new<P: AsRef<std::path::Path>>(dir: P) -> Result<Self> {
+        let dir = dir.as_ref();
+        let mut bin_files = vec![];
+        for file in std::fs::read_dir(dir)?.flatten() {
+            let file = file.path();
+            if let Some(extension) = file.extension() {
+                if extension == "bin" {
+                    bin_files.push(file)
+                }
+            }
+        }
+        if bin_files.len() < 2 {
+            candle::bail!("found less than two bin files in {:?}", dir)
+        }
+        bin_files.sort();
+        let valid_tokens = mmap_file(&bin_files[0])?;
+        let train_tokens = bin_files[1..]
+            .iter()
+            .map(mmap_file)
+            .collect::<Result<Vec<_>>>()?;
+        Ok(Self {
+            valid_tokens: vec![valid_tokens],
+            train_tokens,
+        })
+    }
+
+    pub fn train_tokens(&self) -> usize {
+        self.train_tokens.len()
+    }
+
+    pub fn valid_tokens(&self) -> usize {
+        self.valid_tokens.len()
+    }
+}
+
+pub struct DatasetRandomIter<'a> {
+    all_tokens: &'a [memmap2::Mmap],
+    tokens: Vec<&'a memmap2::Mmap>,
+    current_tokens: &'a memmap2::Mmap,
+    indexes_in_bytes: Vec<usize>,
+    seq_len: usize,
+    device: Device,
+}
+
+impl<'a> DatasetRandomIter<'a> {
+    pub fn new(ds: &'a Dataset, valid: bool, seq_len: usize, device: Device) -> Self {
+        use rand::seq::SliceRandom;
+        use rand::thread_rng;
+
+        let all_tokens = if valid {
+            &ds.valid_tokens
+        } else {
+            &ds.train_tokens
+        };
+        let mut tokens = all_tokens.iter().collect::<Vec<_>>();
+        tokens.shuffle(&mut thread_rng());
+        let current_tokens = tokens.pop().unwrap();
+        let seq_len_in_bytes = seq_len * 2;
+        let mut indexes_in_bytes = (0..current_tokens.len() - seq_len_in_bytes)
+            .step_by(seq_len_in_bytes)
+            .collect::<Vec<_>>();
+        indexes_in_bytes.shuffle(&mut thread_rng());
+        Self {
+            all_tokens,
+            tokens,
+            current_tokens,
+            indexes_in_bytes,
+            seq_len,
+            device,
+        }
+    }
+}
+
+impl<'a> Iterator for DatasetRandomIter<'a> {
+    type Item = Result<(Tensor, Tensor)>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        use byteorder::{LittleEndian, ReadBytesExt};
+        use rand::seq::SliceRandom;
+        use rand::thread_rng;
+
+        let seq_len = self.seq_len;
+        if self.indexes_in_bytes.is_empty() {
+            if self.tokens.is_empty() {
+                self.tokens = self.all_tokens.iter().collect();
+                self.tokens.shuffle(&mut thread_rng());
+            }
+            self.current_tokens = self.tokens.pop().unwrap();
+            let seq_len_in_bytes = self.seq_len * 2;
+            self.indexes_in_bytes = (0..self.current_tokens.len() - seq_len_in_bytes)
+                .step_by(seq_len_in_bytes)
+                .collect::<Vec<_>>();
+            self.indexes_in_bytes.shuffle(&mut thread_rng());
+        }
+        let start_idx = self.indexes_in_bytes.pop().unwrap();
+        let bytes = &self.current_tokens[start_idx..start_idx + 2 * (seq_len + 1)];
+        let mut tokens = vec![0u16; bytes.len() / 2];
+        if let Err(err) = std::io::Cursor::new(bytes).read_u16_into::<LittleEndian>(&mut tokens) {
+            return Some(Err(err.into()));
+        }
+        let tokens = tokens.into_iter().map(|v| v as u32).collect::<Vec<_>>();
+        let inputs = Tensor::new(&tokens[..seq_len], &self.device);
+        let targets = Tensor::new(&tokens[1..], &self.device);
+        Some(candle::error::zip(inputs, targets))
+    }
+}

candle-examples/Cargo.toml

@@ -11,6 +11,7 @@ readme = "README.md"
 
 [dependencies]
 candle = { path = "../candle-core", version = "0.1.0", package = "candle-core" }
+candle-datasets = { path = "../candle-datasets", version = "0.1.0" }
 candle-nn = { path = "../candle-nn", version = "0.1.0" }
 candle-transformers = { path = "../candle-transformers", version = "0.1.0" }
 candle-flash-attn = { path = "../candle-flash-attn", version = "0.1.0", optional = true }

candle-examples/examples/llama2-c/main.rs

@@ -200,7 +200,7 @@ fn run_eval(args: &EvaluationCmd, common_args: &Args) -> Result<()> {
             Some(inputs.and_then(|inputs| targets.map(|targets| (inputs, targets))))
         }
     });
-    let batch_iter = candle_nn::dataset::Batcher::new_r2(iter).batch_size(args.batch_size);
+    let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
     for inp_tgt in batch_iter {
         let (inp, tgt) = inp_tgt?;
         let logits = model.forward(&inp, 0)?;

candle-examples/examples/llama2-c/training.rs

@@ -1,118 +1,6 @@
-#![allow(dead_code)]
-#![allow(unused)]
 use crate::model::{Cache, Config, Llama};
-use candle::{DType, Device, Result, Tensor};
+use candle::{DType, Device, Result};
-
+use candle_datasets::nlp::tinystories::{Dataset, DatasetRandomIter};
-pub struct Dataset {
-    valid_tokens: Vec<memmap2::Mmap>,
-    train_tokens: Vec<memmap2::Mmap>,
-}
-
-fn mmap_file(p: &std::path::PathBuf) -> Result<memmap2::Mmap> {
-    let file = std::fs::File::open(p)?;
-    let mmap = unsafe { memmap2::MmapOptions::new().map(&file)? };
-    Ok(mmap)
-}
-
-impl Dataset {
-    pub fn new<P: AsRef<std::path::Path>>(dir: P) -> Result<Self> {
-        let dir = dir.as_ref();
-        let mut bin_files = vec![];
-        for file in std::fs::read_dir(dir)?.flatten() {
-            let file = file.path();
-            if let Some(extension) = file.extension() {
-                if extension == "bin" {
-                    bin_files.push(file)
-                }
-            }
-        }
-        if bin_files.len() < 2 {
-            candle::bail!("found less than two bin files in {:?}", dir)
-        }
-        bin_files.sort();
-        let valid_tokens = mmap_file(&bin_files[0])?;
-        let train_tokens = bin_files[1..]
-            .iter()
-            .map(mmap_file)
-            .collect::<Result<Vec<_>>>()?;
-        Ok(Self {
-            valid_tokens: vec![valid_tokens],
-            train_tokens,
-        })
-    }
-}
-
-struct DatasetRandomIter<'a> {
-    all_tokens: &'a [memmap2::Mmap],
-    tokens: Vec<&'a memmap2::Mmap>,
-    current_tokens: &'a memmap2::Mmap,
-    indexes_in_bytes: Vec<usize>,
-    seq_len: usize,
-    device: Device,
-}
-
-impl<'a> DatasetRandomIter<'a> {
-    pub fn new(ds: &'a Dataset, valid: bool, seq_len: usize, device: Device) -> Self {
-        use rand::seq::SliceRandom;
-        use rand::thread_rng;
-
-        let all_tokens = if valid {
-            &ds.valid_tokens
-        } else {
-            &ds.train_tokens
-        };
-        let mut tokens = all_tokens.iter().collect::<Vec<_>>();
-        tokens.shuffle(&mut thread_rng());
-        let current_tokens = tokens.pop().unwrap();
-        let seq_len_in_bytes = seq_len * 2;
-        let mut indexes_in_bytes = (0..current_tokens.len() - seq_len_in_bytes)
-            .step_by(seq_len_in_bytes)
-            .collect::<Vec<_>>();
-        indexes_in_bytes.shuffle(&mut thread_rng());
-        Self {
-            all_tokens,
-            tokens,
-            current_tokens,
-            indexes_in_bytes,
-            seq_len,
-            device,
-        }
-    }
-}
-
-impl<'a> Iterator for DatasetRandomIter<'a> {
-    type Item = Result<(Tensor, Tensor)>;
-
-    fn next(&mut self) -> Option<Self::Item> {
-        use byteorder::{LittleEndian, ReadBytesExt};
-        use rand::seq::SliceRandom;
-        use rand::thread_rng;
-
-        let seq_len = self.seq_len;
-        if self.indexes_in_bytes.is_empty() {
-            if self.tokens.is_empty() {
-                self.tokens = self.all_tokens.iter().collect();
-                self.tokens.shuffle(&mut thread_rng());
-            }
-            self.current_tokens = self.tokens.pop().unwrap();
-            let seq_len_in_bytes = self.seq_len * 2;
-            self.indexes_in_bytes = (0..self.current_tokens.len() - seq_len_in_bytes)
-                .step_by(seq_len_in_bytes)
-                .collect::<Vec<_>>();
-            self.indexes_in_bytes.shuffle(&mut thread_rng());
-        }
-        let start_idx = self.indexes_in_bytes.pop().unwrap();
-        let bytes = &self.current_tokens[start_idx..start_idx + 2 * (seq_len + 1)];
-        let mut tokens = vec![0u16; bytes.len() / 2];
-        if let Err(err) = std::io::Cursor::new(bytes).read_u16_into::<LittleEndian>(&mut tokens) {
-            return Some(Err(err.into()));
-        }
-        let tokens = tokens.into_iter().map(|v| v as u32).collect::<Vec<_>>();
-        let inputs = Tensor::new(&tokens[..seq_len], &self.device);
-        let targets = Tensor::new(&tokens[1..], &self.device);
-        Some(candle::error::zip(inputs, targets))
-    }
-}
 
 fn valid_loss(
     dataset: &Dataset,
@@ -121,7 +9,7 @@ fn valid_loss(
     device: &Device,
 ) -> Result<f64> {
     let iter = DatasetRandomIter::new(dataset, true, model.config.seq_len, device.clone());
-    let batch_iter = candle_nn::dataset::Batcher::new_r2(iter).batch_size(args.batch_size);
+    let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
     let mut sum_ce = 0f64;
     let mut cnt = 0usize;
     for inp_tgt in batch_iter.take(50) {
@@ -139,14 +27,14 @@ pub fn run(args: &crate::TrainingCmd, common_args: &crate::Args) -> Result<()> {
     let dataset = Dataset::new(&args.pretokenized_dir)?;
     println!(
         "loaded dataset, train: {} files, valid: {} files",
-        dataset.train_tokens.len(),
+        dataset.train_tokens(),
-        dataset.valid_tokens.len()
+        dataset.valid_tokens()
     );
     let varmap = candle_nn::VarMap::new();
     let vb = candle_nn::VarBuilder::from_varmap(&varmap, DType::F32, &device);
     let config = Config::tiny();
     let iter = DatasetRandomIter::new(&dataset, false, config.seq_len, device.clone());
-    let batch_iter = candle_nn::dataset::Batcher::new_r2(iter).batch_size(args.batch_size);
+    let batch_iter = candle_datasets::Batcher::new_r2(iter).batch_size(args.batch_size);
 
     let cache = Cache::new(false, &config, vb.pp("rot"))?;
     let model = Llama::load(vb, &cache, config)?;

candle-examples/examples/mnist-training/main.rs

@@ -63,7 +63,7 @@ struct TrainingArgs {
 }
 
 fn training_loop<M: Model>(
-    m: candle_nn::vision::Dataset,
+    m: candle_datasets::vision::Dataset,
     args: &TrainingArgs,
 ) -> anyhow::Result<()> {
     let dev = candle::Device::cuda_if_available(0)?;
@@ -140,7 +140,7 @@ struct Args {
 pub fn main() -> anyhow::Result<()> {
     let args = Args::parse();
     // Load the dataset
-    let m = candle_nn::vision::mnist::load_dir("data")?;
+    let m = candle_datasets::vision::mnist::load_dir("data")?;
     println!("train-images: {:?}", m.train_images.shape());
     println!("train-labels: {:?}", m.train_labels.shape());
     println!("test-images: {:?}", m.test_images.shape());

candle-nn/src/lib.rs

@@ -2,7 +2,6 @@
 // error type if needed or add some specialized cases on the candle-core side.
 pub mod activation;
 pub mod conv;
-pub mod dataset;
 pub mod embedding;
 pub mod init;
 pub mod layer_norm;
@@ -11,7 +10,6 @@ pub mod loss;
 pub mod ops;
 pub mod optim;
 pub mod var_builder;
-pub mod vision;
 
 pub use activation::Activation;
 pub use conv::{Conv1d, Conv1dConfig};