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update burn to v0.15.0 #251

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update burn to v0.15.0 #251

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d5536c7
update burn to v0.15.0
L-M-Sherlock Oct 29, 2024
395c326
avoid unnecessary unsqueeze
L-M-Sherlock Oct 29, 2024
1ca762c
add vec!
L-M-Sherlock Oct 30, 2024
d01880e
simplify
L-M-Sherlock Oct 30, 2024
1dba65d
remove workaround of pow
L-M-Sherlock Oct 30, 2024
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1,841 changes: 1,150 additions & 691 deletions Cargo.lock
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8 changes: 4 additions & 4 deletions Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,21 +9,21 @@ keywords = ["spaced-repetition", "algorithm", "fsrs", "machine-learning"]
license = "BSD-3-Clause"
readme = "README.md"
repository = "https://github.com/open-spaced-repetition/fsrs-rs"
rust-version = "1.75.0"
rust-version = "1.81.0"
description = "FSRS for Rust, including Optimizer and Scheduler"

# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html

[dependencies.burn]
version = "0.13.2"
version = "0.15.0"
# git = "https://github.com/tracel-ai/burn.git"
# rev = "6ae3926006872a204869e84ffc303417c54b6b7f"
# path = "../burn/burn"
default-features = false
features = ["std", "train", "ndarray"]

[dev-dependencies.burn]
version = "0.13.2"
version = "0.15.0"
# git = "https://github.com/tracel-ai/burn.git"
# rev = "6ae3926006872a204869e84ffc303417c54b6b7f"
# path = "../burn/burn"
Expand All @@ -48,7 +48,7 @@ chrono-tz = "0.8.4"
criterion = { version = "0.5.1" }
csv = "1.3.0"
fern = "0.6.0"
rusqlite = { version = "0.30.0" }
rusqlite = { version = "0.32.0" }

[[bench]]
name = "benchmark"
Expand Down
2 changes: 1 addition & 1 deletion rust-toolchain.toml
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
[toolchain]
# older versions may fail to compile; newer versions may fail the clippy tests
channel = "1.80"
channel = "1.81"
components = ["rustfmt", "clippy"]
8 changes: 4 additions & 4 deletions src/batch_shuffle.rs
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Expand Up @@ -129,14 +129,14 @@ mod tests {
assert_eq!(
batch.t_historys.shape(),
Shape {
dims: [7, batch_size]
dims: vec![7, batch_size]
}
);
let batch = iterator.next().unwrap();
assert_eq!(
batch.t_historys.shape(),
Shape {
dims: [6, batch_size]
dims: vec![6, batch_size]
}
);

Expand All @@ -156,14 +156,14 @@ mod tests {
assert_eq!(
batch.t_historys.shape(),
Shape {
dims: [19, batch_size]
dims: vec![19, batch_size]
}
);
let batch = iterator.next().unwrap();
assert_eq!(
batch.t_historys.shape(),
Shape {
dims: [9, batch_size]
dims: vec![9, batch_size]
}
);

Expand Down
21 changes: 11 additions & 10 deletions src/convertor_tests.rs
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Expand Up @@ -7,7 +7,8 @@ use burn::backend::ndarray::NdArrayDevice;
use burn::data::dataloader::batcher::Batcher;
use burn::data::dataloader::Dataset;
use burn::data::dataset::InMemDataset;
use burn::tensor::Data;
use burn::tensor::cast::ToElement;
use burn::tensor::TensorData;
use chrono::prelude::*;
use chrono_tz::Tz;
use itertools::Itertools;
Expand Down Expand Up @@ -391,15 +392,15 @@ fn conversion_works() {
let batcher = FSRSBatcher::<NdArrayAutodiff>::new(device);
let res = batcher.batch(vec![fsrs_items.pop().unwrap()]);
assert_eq!(res.delta_ts.into_scalar(), 64.0);
assert_eq!(
res.r_historys.squeeze(1).to_data(),
Data::from([3.0, 4.0, 3.0, 3.0, 3.0, 2.0])
);
assert_eq!(
res.t_historys.squeeze(1).to_data(),
Data::from([0.0, 0.0, 5.0, 10.0, 22.0, 56.0])
);
assert_eq!(res.labels.to_data(), Data::from([1]));
res.r_historys
.squeeze::<1>(1)
.to_data()
.assert_approx_eq(&TensorData::from([3.0, 4.0, 3.0, 3.0, 3.0, 2.0]), 5);
res.t_historys
.squeeze::<1>(1)
.to_data()
.assert_approx_eq(&TensorData::from([0.0, 0.0, 5.0, 10.0, 22.0, 56.0]), 5);
assert_eq!(res.labels.into_scalar().to_i32(), 1);
}

#[test]
Expand Down
17 changes: 8 additions & 9 deletions src/cosine_annealing.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -20,8 +20,8 @@ impl CosineAnnealingLR {
}
}

impl<B: Backend> LrScheduler<B> for CosineAnnealingLR {
type Record = usize;
impl LrScheduler for CosineAnnealingLR {
type Record<B: Backend> = usize;

fn step(&mut self) -> LearningRate {
self.step_count += 1.0;
Expand Down Expand Up @@ -52,11 +52,11 @@ impl<B: Backend> LrScheduler<B> for CosineAnnealingLR {
self.current_lr
}

fn to_record(&self) -> Self::Record {
fn to_record<B: Backend>(&self) -> Self::Record<B> {
self.step_count as usize
}

fn load_record(mut self, record: Self::Record) -> Self {
fn load_record<B: Backend>(mut self, record: Self::Record<B>) -> Self {
self.step_count = record as LearningRate;
self
}
Expand All @@ -65,23 +65,22 @@ impl<B: Backend> LrScheduler<B> for CosineAnnealingLR {
#[cfg(test)]
mod tests {
use super::*;
use burn::{backend::NdArray, tensor::Data};
type Backend = NdArray<f32>;
use burn::tensor::TensorData;

#[test]
fn lr_scheduler() {
let mut lr_scheduler = CosineAnnealingLR::init(100000.0, 1.0e-1);

let lrs = (0..=200000)
.map(|_| {
LrScheduler::<Backend>::step(&mut lr_scheduler);
LrScheduler::step(&mut lr_scheduler);
lr_scheduler.current_lr
})
.step_by(20000)
.collect::<Vec<_>>();

Data::from(&lrs[..]).assert_approx_eq(
&Data::from([
TensorData::from(&lrs[..]).assert_approx_eq(
&TensorData::from([
0.1,
0.09045084971874785,
0.06545084971874875,
Expand Down
60 changes: 31 additions & 29 deletions src/dataset.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ use std::collections::{HashMap, HashSet};
use burn::data::dataloader::batcher::Batcher;
use burn::{
data::dataset::Dataset,
tensor::{backend::Backend, Data, ElementConversion, Float, Int, Shape, Tensor},
tensor::{backend::Backend, Float, Int, Shape, Tensor, TensorData},
};

use itertools::Itertools;
Expand Down Expand Up @@ -106,24 +106,22 @@ impl<B: Backend> Batcher<FSRSItem, FSRSBatch<B>> for FSRSBatcher<B> {
item.history().map(|r| (r.delta_t, r.rating)).unzip();
delta_t.resize(pad_size, 0);
rating.resize(pad_size, 0);
let delta_t = Tensor::from_data(
Data::new(
let delta_t = Tensor::<B, 2>::from_floats(
TensorData::new(
delta_t,
Shape {
dims: [1, pad_size],
dims: vec![1, pad_size],
},
)
.convert(),
),
&self.device,
);
let rating = Tensor::from_data(
Data::new(
let rating = Tensor::<B, 2>::from_data(
TensorData::new(
rating,
Shape {
dims: [1, pad_size],
dims: vec![1, pad_size],
},
)
.convert(),
),
&self.device,
);
(delta_t, rating)
Expand All @@ -134,12 +132,12 @@ impl<B: Backend> Batcher<FSRSItem, FSRSBatch<B>> for FSRSBatcher<B> {
.iter()
.map(|item| {
let current = item.current();
let delta_t = Tensor::from_data(Data::from([current.delta_t.elem()]), &self.device);
let delta_t = Tensor::<B, 1>::from_floats([current.delta_t], &self.device);
let label = match current.rating {
1 => 0.0,
_ => 1.0,
1 => 0,
_ => 1,
};
let label = Tensor::from_data(Data::from([label.elem()]), &self.device);
let label = Tensor::<B, 1, Int>::from_ints([label], &self.device);
(delta_t, label)
})
.unzip();
Expand Down Expand Up @@ -436,29 +434,33 @@ mod tests {
},
];
let batch = batcher.batch(items);
assert_eq!(
batch.t_historys.to_data(),
Data::from([
batch.t_historys.to_data().assert_approx_eq(
&TensorData::from([
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 5.0, 0.0, 2.0, 2.0, 2.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 0.0, 6.0, 6.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 16.0, 0.0, 0.0]
])
[0.0, 0.0, 0.0, 0.0, 0.0, 16.0, 0.0, 0.0],
]),
5,
);
assert_eq!(
batch.r_historys.to_data(),
Data::from([
batch.r_historys.to_data().assert_approx_eq(
&TensorData::from([
[4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 1.0, 1.0],
[0.0, 3.0, 0.0, 3.0, 3.0, 3.0, 0.0, 1.0],
[0.0, 0.0, 0.0, 0.0, 3.0, 3.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0]
])
[0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0],
]),
5,
);
assert_eq!(
batch.delta_ts.to_data(),
Data::from([5.0, 11.0, 2.0, 6.0, 16.0, 39.0, 1.0, 1.0])

batch.delta_ts.to_data().assert_approx_eq(
&TensorData::from([5.0, 11.0, 2.0, 6.0, 16.0, 39.0, 1.0, 1.0]),
5,
);
assert_eq!(batch.labels.to_data(), Data::from([1, 1, 1, 1, 1, 1, 0, 1]));
batch
.labels
.to_data()
.assert_approx_eq(&TensorData::from([1, 1, 1, 1, 1, 1, 0, 1]), 5);
}

#[test]
Expand Down
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