Compare commits
2 Commits
f6b9d79062
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b86b3334d6
| Author | SHA1 | Date | |
|---|---|---|---|
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b86b3334d6 | ||
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8fc8addcac |
2
.gitignore
vendored
Normal file
2
.gitignore
vendored
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@@ -0,0 +1,2 @@
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||||
*/target/
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*/mnist.npz
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546
hod_1/Cargo.lock
generated
546
hod_1/Cargo.lock
generated
@@ -274,11 +274,18 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
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"raw-cpuid",
|
||||
"seq-macro",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "gemm-common"
|
||||
version = "0.19.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "88027625910cc9b1085aaaa1c4bc46bb3a36aad323452b33c25b5e4e7c8e2a3e"
|
||||
dependencies = [
|
||||
"bytemuck",
|
||||
"dyn-stack",
|
||||
"half",
|
||||
"libm",
|
||||
"num-complex",
|
||||
"num-traits",
|
||||
"once_cell",
|
||||
"paste",
|
||||
"pulp",
|
||||
"raw-cpuid",
|
||||
"rayon",
|
||||
"seq-macro",
|
||||
"sysctl",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "gemm-f16"
|
||||
version = "0.19.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "e3df7a55202e6cd6739d82ae3399c8e0c7e1402859b30e4cb780e61525d9486e"
|
||||
dependencies = [
|
||||
"dyn-stack",
|
||||
"gemm-common",
|
||||
"gemm-f32",
|
||||
"half",
|
||||
"num-complex",
|
||||
"num-traits",
|
||||
"paste",
|
||||
"raw-cpuid",
|
||||
"rayon",
|
||||
"seq-macro",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "gemm-f32"
|
||||
version = "0.19.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "02e0b8c9da1fbec6e3e3ab2ce6bc259ef18eb5f6f0d3e4edf54b75f9fd41a81c"
|
||||
dependencies = [
|
||||
"dyn-stack",
|
||||
"gemm-common",
|
||||
"num-complex",
|
||||
"num-traits",
|
||||
"paste",
|
||||
"raw-cpuid",
|
||||
"seq-macro",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "gemm-f64"
|
||||
version = "0.19.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "056131e8f2a521bfab322f804ccd652520c79700d81209e9d9275bbdecaadc6a"
|
||||
dependencies = [
|
||||
"dyn-stack",
|
||||
"gemm-common",
|
||||
"num-complex",
|
||||
"num-traits",
|
||||
"paste",
|
||||
"raw-cpuid",
|
||||
"seq-macro",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "generic-array"
|
||||
version = "0.14.7"
|
||||
@@ -1889,10 +2318,21 @@ dependencies = [
|
||||
"cfg-if",
|
||||
"crunchy",
|
||||
"num-traits",
|
||||
"rand",
|
||||
"rand_distr",
|
||||
"serde",
|
||||
"zerocopy",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "hashbrown"
|
||||
version = "0.13.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "43a3c133739dddd0d2990f9a4bdf8eb4b21ef50e4851ca85ab661199821d510e"
|
||||
dependencies = [
|
||||
"ahash",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "hashbrown"
|
||||
version = "0.15.5"
|
||||
@@ -1955,6 +2395,7 @@ dependencies = [
|
||||
"clap",
|
||||
"ndarray",
|
||||
"npyz",
|
||||
"serde",
|
||||
"zip 8.2.0",
|
||||
]
|
||||
|
||||
@@ -2466,6 +2907,16 @@ version = "2.8.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "f8ca58f447f06ed17d5fc4043ce1b10dd205e060fb3ce5b979b8ed8e59ff3f79"
|
||||
|
||||
[[package]]
|
||||
name = "memmap2"
|
||||
version = "0.9.9"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "744133e4a0e0a658e1374cf3bf8e415c4052a15a111acd372764c55b4177d490"
|
||||
dependencies = [
|
||||
"libc",
|
||||
"stable_deref_trait",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "metal"
|
||||
version = "0.32.0"
|
||||
@@ -2650,6 +3101,7 @@ version = "0.4.6"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "73f88a1307638156682bada9d7604135552957b7818057dcef22705b4d509495"
|
||||
dependencies = [
|
||||
"bytemuck",
|
||||
"num-traits",
|
||||
]
|
||||
|
||||
@@ -3013,6 +3465,29 @@ version = "1.0.17"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "3eb8486b569e12e2c32ad3e204dbaba5e4b5b216e9367044f25f1dba42341773"
|
||||
|
||||
[[package]]
|
||||
name = "pulp"
|
||||
version = "0.22.2"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "2e205bb30d5b916c55e584c22201771bcf2bad9aabd5d4127f38387140c38632"
|
||||
dependencies = [
|
||||
"bytemuck",
|
||||
"cfg-if",
|
||||
"libm",
|
||||
"num-complex",
|
||||
"paste",
|
||||
"pulp-wasm-simd-flag",
|
||||
"raw-cpuid",
|
||||
"reborrow",
|
||||
"version_check",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pulp-wasm-simd-flag"
|
||||
version = "0.1.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "40e24eee682d89fb193496edf918a7f407d30175b2e785fe057e4392dfd182e0"
|
||||
|
||||
[[package]]
|
||||
name = "py_literal"
|
||||
version = "0.4.0"
|
||||
@@ -3153,6 +3628,15 @@ version = "0.1.5"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "ca45419789ae5a7899559e9512e58ca889e41f04f1f2445e9f4b290ceccd1d08"
|
||||
|
||||
[[package]]
|
||||
name = "raw-cpuid"
|
||||
version = "11.6.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "498cd0dc59d73224351ee52a95fee0f1a617a2eae0e7d9d720cc622c73a54186"
|
||||
dependencies = [
|
||||
"bitflags",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "raw-window-handle"
|
||||
version = "0.6.2"
|
||||
@@ -3185,6 +3669,12 @@ dependencies = [
|
||||
"crossbeam-utils",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "reborrow"
|
||||
version = "0.5.5"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "03251193000f4bd3b042892be858ee50e8b3719f2b08e5833ac4353724632430"
|
||||
|
||||
[[package]]
|
||||
name = "redox_syscall"
|
||||
version = "0.5.18"
|
||||
@@ -3445,6 +3935,17 @@ version = "1.0.23"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "9774ba4a74de5f7b1c1451ed6cd5285a32eddb5cccb8cc655a4e50009e06477f"
|
||||
|
||||
[[package]]
|
||||
name = "safetensors"
|
||||
version = "0.7.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "675656c1eabb620b921efea4f9199f97fc86e36dd6ffd1fbbe48d0f59a4987f5"
|
||||
dependencies = [
|
||||
"hashbrown 0.16.1",
|
||||
"serde",
|
||||
"serde_json",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "same-file"
|
||||
version = "1.0.6"
|
||||
@@ -3740,6 +4241,20 @@ dependencies = [
|
||||
"syn",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sysctl"
|
||||
version = "0.6.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "01198a2debb237c62b6826ec7081082d951f46dbb64b0e8c7649a452230d1dfc"
|
||||
dependencies = [
|
||||
"bitflags",
|
||||
"byteorder",
|
||||
"enum-as-inner",
|
||||
"libc",
|
||||
"thiserror 1.0.69",
|
||||
"walkdir",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "sysinfo"
|
||||
version = "0.36.1"
|
||||
@@ -3787,6 +4302,23 @@ dependencies = [
|
||||
"winapi-util",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "text_placeholder"
|
||||
version = "0.5.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "dd5008f74a09742486ef0047596cf35df2b914e2a8dca5727fcb6ba6842a766b"
|
||||
dependencies = [
|
||||
"hashbrown 0.13.2",
|
||||
"serde",
|
||||
"serde_json",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "textdistance"
|
||||
version = "1.1.1"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "aa672c55ab69f787dbc9126cc387dbe57fdd595f585e4524cf89018fa44ab819"
|
||||
|
||||
[[package]]
|
||||
name = "thiserror"
|
||||
version = "1.0.69"
|
||||
@@ -5294,6 +5826,18 @@ dependencies = [
|
||||
"zstd 0.11.2+zstd.1.5.2",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "zip"
|
||||
version = "7.2.0"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "c42e33efc22a0650c311c2ef19115ce232583abbe80850bc8b66509ebef02de0"
|
||||
dependencies = [
|
||||
"crc32fast",
|
||||
"indexmap",
|
||||
"memchr",
|
||||
"typed-path",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "zip"
|
||||
version = "8.2.0"
|
||||
|
||||
@@ -4,10 +4,11 @@ version = "0.1.0"
|
||||
edition = "2024"
|
||||
|
||||
[dependencies]
|
||||
burn = { version = "0.20.1", default-features = false, features = ["ndarray", "train"] }
|
||||
burn = { version = "0.20.1", default-features = false, features = ["ndarray", "std", "train"] }
|
||||
burn-autodiff = "0.20.1"
|
||||
burn-ndarray = "0.20.1"
|
||||
clap = { version = "4.5.60", features = ["derive"] }
|
||||
ndarray = "0.17.2"
|
||||
npyz = { version = "0.8.4", features = ["npz"] }
|
||||
serde = { version = "1.0.228", features = ["derive"] }
|
||||
zip = { version = "8.2.0", features = ["deflate"] }
|
||||
|
||||
313
hod_1/src/lib.rs
313
hod_1/src/lib.rs
@@ -1,17 +1,28 @@
|
||||
use burn::tensor::Tensor;
|
||||
use burn::optim::Optimizer;
|
||||
use burn::nn::loss::CrossEntropyLossConfig;
|
||||
use burn::tensor::Int;
|
||||
use burn::tensor::backend::AutodiffBackend;
|
||||
use burn::tensor::backend::Backend;
|
||||
use burn::optim::GradientsParams;
|
||||
use burn::tensor::activation;
|
||||
use std::str::FromStr;
|
||||
// src/lib.rs
|
||||
|
||||
use burn::config::Config;
|
||||
use burn::data::dataloader::DataLoaderBuilder;
|
||||
use burn::data::dataloader::batcher::Batcher;
|
||||
use burn::data::dataset::Dataset;
|
||||
use burn::module::Module;
|
||||
use burn::nn::loss::CrossEntropyLossConfig;
|
||||
use burn::nn::{Linear, LinearConfig};
|
||||
use burn::optim::AdamConfig;
|
||||
use burn::record::CompactRecorder;
|
||||
use burn::tensor::activation;
|
||||
use burn::tensor::backend::{AutodiffBackend, Backend};
|
||||
use burn::tensor::{Int, Tensor};
|
||||
use burn::train::metric::{AccuracyMetric, LossMetric};
|
||||
use burn::lr_scheduler::constant::ConstantLr;
|
||||
use burn::train::{
|
||||
ClassificationOutput, InferenceStep, Learner, SupervisedTraining,
|
||||
TrainOutput, TrainStep, TrainingStrategy,
|
||||
};
|
||||
use std::str::FromStr;
|
||||
|
||||
pub type B = burn_autodiff::Autodiff<burn_ndarray::NdArray<f64>>;
|
||||
|
||||
// Model
|
||||
#[derive(Module, Debug)]
|
||||
pub struct MnistClassifier<B: Backend> {
|
||||
hidden: Vec<Linear<B>>,
|
||||
@@ -19,7 +30,7 @@ pub struct MnistClassifier<B: Backend> {
|
||||
activation: Activation,
|
||||
}
|
||||
|
||||
impl<B: Backend<FloatElem = f64, IntElem = i64>> MnistClassifier<B> {
|
||||
impl<B: Backend> MnistClassifier<B> {
|
||||
pub fn new(
|
||||
device: &B::Device,
|
||||
hidden_layers: usize,
|
||||
@@ -27,123 +38,61 @@ impl<B: Backend<FloatElem = f64, IntElem = i64>> MnistClassifier<B> {
|
||||
activation: Activation,
|
||||
) -> Self {
|
||||
let mut hidden = Vec::new();
|
||||
let mut current_input_size = 784;
|
||||
if hidden_layers > 0 {
|
||||
hidden.push(LinearConfig::new(current_input_size, hidden_layer_size).init(device));
|
||||
current_input_size = hidden_layer_size;
|
||||
let mut in_size = 784;
|
||||
|
||||
for _ in 1..hidden_layers {
|
||||
hidden.push(LinearConfig::new(hidden_layer_size, hidden_layer_size).init(device));
|
||||
}
|
||||
for _ in 0..hidden_layers {
|
||||
hidden.push(LinearConfig::new(in_size, hidden_layer_size).init(device));
|
||||
in_size = hidden_layer_size;
|
||||
}
|
||||
|
||||
let output = LinearConfig::new(current_input_size, 10).init(device);
|
||||
|
||||
let output = LinearConfig::new(in_size, 10).init(device);
|
||||
Self { hidden, output, activation }
|
||||
}
|
||||
|
||||
pub fn forward(&self, images: Tensor<B, 2>) -> Tensor<B, 2> {
|
||||
let mut result = images;
|
||||
let mut x = images;
|
||||
for layer in &self.hidden {
|
||||
result = layer.forward(result);
|
||||
result = self.activation.forward(result);
|
||||
x = layer.forward(x);
|
||||
x = self.activation.forward(x);
|
||||
}
|
||||
self.output.forward(result)
|
||||
}
|
||||
|
||||
pub fn train_step(
|
||||
&self,
|
||||
images: Tensor<B, 2>,
|
||||
labels: Tensor<B, 1, Int>,
|
||||
optimizer: &mut impl Optimizer<Self, B>,
|
||||
lr: f64
|
||||
) -> (Self, f64, usize) where B: AutodiffBackend {
|
||||
// Forward pass
|
||||
let logits = self.forward(images);
|
||||
|
||||
// Loss calculation
|
||||
let loss_fn = CrossEntropyLossConfig::new().init(&logits.device());
|
||||
let loss = loss_fn.forward(logits.clone(), labels.clone());
|
||||
|
||||
// Accuracy
|
||||
let correct = logits.argmax(1)
|
||||
.flatten::<1>(0, 1)
|
||||
.equal(labels)
|
||||
.int()
|
||||
.sum()
|
||||
.into_scalar() as usize;
|
||||
|
||||
let loss_val = loss.clone().into_scalar();
|
||||
|
||||
// Backprop
|
||||
let grads = loss.backward();
|
||||
let grads = GradientsParams::from_grads(grads, self);
|
||||
let updated_model = optimizer.step(lr, self.clone(), grads);
|
||||
|
||||
(updated_model, loss_val, correct)
|
||||
}
|
||||
|
||||
pub fn train_and_evaluate(
|
||||
&mut self,
|
||||
images: Tensor<B, 2>,
|
||||
labels: Tensor<B, 1, Int>,
|
||||
optimizer: &mut impl Optimizer<Self, B>,
|
||||
args_epochs: usize,
|
||||
args_batch_size: usize,
|
||||
) where B: AutodiffBackend {
|
||||
eprintln!("images shape: {:?}", images.shape());
|
||||
eprintln!("labels shape: {:?}", labels.shape());
|
||||
|
||||
let train_size = 50000;
|
||||
let x_train = images.clone().slice([0..train_size]);
|
||||
let y_train = labels.clone().slice([0..train_size]);
|
||||
let x_dev = images.slice([train_size..55000]);
|
||||
let y_dev = labels.slice([train_size..55000]);
|
||||
|
||||
let target_epochs = [1, 5, 10];
|
||||
for epoch in target_epochs {
|
||||
let start = std::time::Instant::now();
|
||||
let mut train_loss = 0.0;
|
||||
let mut train_correct = 0;
|
||||
|
||||
for i in (0..train_size).step_by(args_batch_size) {
|
||||
let end = (i + args_batch_size).min(train_size);
|
||||
if i >= end { continue; }
|
||||
|
||||
let b_x = x_train.clone().slice([i..end]);
|
||||
let b_y = y_train.clone().slice([i..end]);
|
||||
|
||||
if i == 0 {
|
||||
eprintln!("first batch shape: {:?}", b_x.shape());
|
||||
eprintln!("output layer: input={:?} output=10", self.output.weight.shape());
|
||||
}
|
||||
|
||||
let (updated_model, loss_val, correct) = self.train_step(b_x, b_y, optimizer, 1e-3);
|
||||
*self = updated_model;
|
||||
|
||||
train_loss += loss_val;
|
||||
train_correct += correct;
|
||||
}
|
||||
|
||||
// Dev metrics
|
||||
let dev_logits = self.forward(x_dev.clone());
|
||||
let loss_fn = CrossEntropyLossConfig::new().init(&dev_logits.device());
|
||||
let dev_loss = loss_fn.forward(dev_logits.clone(), y_dev.clone()).into_scalar();
|
||||
let dev_acc = dev_logits.argmax(1).flatten::<1>(0, 1).equal(y_dev.clone()).int().sum().into_scalar() as f64 / 5000.0;
|
||||
|
||||
println!(
|
||||
"Epoch {:2}/{} {:.1}s loss={:.4} accuracy={:.4} dev:loss={:.4} dev:accuracy={:.4}",
|
||||
epoch, args_epochs, start.elapsed().as_secs_f32(),
|
||||
train_loss / (train_size as f64 / args_batch_size as f64),
|
||||
train_correct as f64 / train_size as f64,
|
||||
dev_loss, dev_acc
|
||||
);
|
||||
self.output.forward(x)
|
||||
}
|
||||
}
|
||||
|
||||
impl<B: AutodiffBackend> TrainStep for MnistClassifier<B> {
|
||||
type Input = MnistBatch<B>;
|
||||
type Output = ClassificationOutput<B>;
|
||||
|
||||
fn step(&self, batch: MnistBatch<B>) -> TrainOutput<ClassificationOutput<B>> {
|
||||
let output = self.forward(batch.images);
|
||||
let loss = CrossEntropyLossConfig::new()
|
||||
.init(&output.device())
|
||||
.forward(output.clone(), batch.targets.clone());
|
||||
|
||||
TrainOutput::new(
|
||||
self,
|
||||
loss.backward(),
|
||||
ClassificationOutput { loss, output, targets: batch.targets },
|
||||
)
|
||||
}
|
||||
}
|
||||
|
||||
impl<B: Backend> InferenceStep for MnistClassifier<B> {
|
||||
type Input = MnistBatch<B>;
|
||||
type Output = ClassificationOutput<B>;
|
||||
|
||||
#[derive(Debug, Clone, Copy, Module, Default)]
|
||||
fn step(&self, batch: MnistBatch<B>) -> ClassificationOutput<B> {
|
||||
let output = self.forward(batch.images);
|
||||
let loss = CrossEntropyLossConfig::new()
|
||||
.init(&output.device())
|
||||
.forward(output.clone(), batch.targets.clone());
|
||||
|
||||
ClassificationOutput { loss, output, targets: batch.targets }
|
||||
}
|
||||
}
|
||||
|
||||
// Activation
|
||||
#[derive(Debug, Clone, Copy, Module, Default, serde::Serialize, serde::Deserialize)]
|
||||
pub enum Activation {
|
||||
#[default]
|
||||
None,
|
||||
@@ -175,3 +124,141 @@ impl Activation {
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Dataset & Batch
|
||||
#[derive(Clone, Debug)]
|
||||
pub struct MnistItem {
|
||||
pub image: [f64; 784],
|
||||
pub label: u8,
|
||||
}
|
||||
|
||||
pub struct MnistDataset {
|
||||
items: Vec<MnistItem>,
|
||||
}
|
||||
|
||||
impl MnistDataset {
|
||||
pub fn new(items: Vec<MnistItem>) -> Self {
|
||||
Self { items }
|
||||
}
|
||||
}
|
||||
|
||||
impl Dataset<MnistItem> for MnistDataset {
|
||||
fn get(&self, index: usize) -> Option<MnistItem> {
|
||||
self.items.get(index).cloned()
|
||||
}
|
||||
fn len(&self) -> usize {
|
||||
self.items.len()
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Clone, Debug)]
|
||||
pub struct MnistBatch<B: Backend> {
|
||||
pub images: Tensor<B, 2>,
|
||||
pub targets: Tensor<B, 1, Int>,
|
||||
}
|
||||
|
||||
#[derive(Clone)]
|
||||
pub struct MnistBatcher;
|
||||
|
||||
impl MnistBatcher {
|
||||
pub fn new() -> Self {
|
||||
Self
|
||||
}
|
||||
}
|
||||
|
||||
impl<B: Backend<FloatElem = f64, IntElem = i64>> Batcher<B, MnistItem, MnistBatch<B>>
|
||||
for MnistBatcher
|
||||
{
|
||||
fn batch(&self, items: Vec<MnistItem>, device: &B::Device) -> MnistBatch<B> {
|
||||
let n = items.len();
|
||||
let image_data: Vec<f64> = items.iter().flat_map(|i| i.image).collect();
|
||||
let label_data: Vec<i64> = items.iter().map(|i| i.label as i64).collect();
|
||||
|
||||
let images = Tensor::<B, 2>::from_data(
|
||||
burn::tensor::TensorData::new(image_data, [n, 784]),
|
||||
device, // ← use the passed-in device, not self.device
|
||||
);
|
||||
let targets = Tensor::<B, 1, Int>::from_data(
|
||||
burn::tensor::TensorData::new(label_data, [n]),
|
||||
device,
|
||||
);
|
||||
|
||||
MnistBatch { images, targets }
|
||||
}
|
||||
}
|
||||
|
||||
// Config
|
||||
#[derive(Config, Debug)]
|
||||
pub struct MnistModelConfig {
|
||||
pub hidden_layers: usize,
|
||||
pub hidden_layer_size: usize,
|
||||
pub activation: Activation,
|
||||
}
|
||||
|
||||
impl MnistModelConfig {
|
||||
pub fn init<B: Backend>(&self, device: &B::Device) -> MnistClassifier<B> {
|
||||
MnistClassifier::new(device, self.hidden_layers, self.hidden_layer_size, self.activation)
|
||||
}
|
||||
}
|
||||
|
||||
#[derive(Config, Debug)]
|
||||
pub struct MnistTrainingConfig {
|
||||
pub model: MnistModelConfig,
|
||||
pub optimizer: AdamConfig,
|
||||
|
||||
#[config(default = 10)]
|
||||
pub num_epochs: usize,
|
||||
#[config(default = 64)]
|
||||
pub batch_size: usize,
|
||||
#[config(default = 4)]
|
||||
pub num_workers: usize,
|
||||
#[config(default = 42)]
|
||||
pub seed: u64,
|
||||
#[config(default = 1.0e-4)]
|
||||
pub learning_rate: f64,
|
||||
}
|
||||
|
||||
// Training
|
||||
impl MnistTrainingConfig {
|
||||
pub fn train<B>(
|
||||
&self,
|
||||
device: B::Device,
|
||||
train_dataset: MnistDataset,
|
||||
valid_dataset: MnistDataset,
|
||||
) where
|
||||
B: AutodiffBackend<FloatElem = f64, IntElem = i64>,
|
||||
B::InnerBackend: Backend<FloatElem = f64, IntElem = i64>,
|
||||
{
|
||||
B::seed(&device, self.seed);
|
||||
|
||||
let model = self.model.init::<B>(&device);
|
||||
let optim = self.optimizer.init();
|
||||
|
||||
let batcher_train = MnistBatcher::new();
|
||||
let batcher_valid = MnistBatcher::new();
|
||||
|
||||
let dataloader_train = DataLoaderBuilder::new(batcher_train)
|
||||
.batch_size(self.batch_size)
|
||||
.shuffle(self.seed)
|
||||
.num_workers(self.num_workers)
|
||||
.build(train_dataset);
|
||||
|
||||
let dataloader_valid = DataLoaderBuilder::new(batcher_valid)
|
||||
.batch_size(self.batch_size)
|
||||
.num_workers(self.num_workers)
|
||||
.build(valid_dataset);
|
||||
|
||||
let training = SupervisedTraining::new("/tmp/artifacts", dataloader_train, dataloader_valid)
|
||||
.metrics((AccuracyMetric::new(), LossMetric::new()))
|
||||
.with_file_checkpointer(CompactRecorder::new())
|
||||
.num_epochs(self.num_epochs)
|
||||
.summary()
|
||||
.with_training_strategy(TrainingStrategy::SingleDevice(device));
|
||||
|
||||
let _result = training.launch(Learner::new(
|
||||
model,
|
||||
optim,
|
||||
ConstantLr::new(self.learning_rate), // plain float → constant LR scheduler
|
||||
));
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,114 +1,83 @@
|
||||
use burn::tensor::backend::Backend;
|
||||
use burn::tensor::Tensor;
|
||||
use clap::Parser;
|
||||
use hod_1::B;
|
||||
use hod_1::{Activation, MnistDataset, MnistItem, MnistModelConfig, MnistTrainingConfig, B};
|
||||
use burn::optim::AdamConfig;
|
||||
use std::fs::File;
|
||||
use std::io::Read;
|
||||
use std::str::FromStr;
|
||||
use hod_1::*;
|
||||
|
||||
use burn::optim::AdamConfig;
|
||||
use burn::optim::Optimizer;
|
||||
use burn::nn::loss::CrossEntropyLossConfig;
|
||||
|
||||
#[derive(Parser, Debug)]
|
||||
#[command(author, version, about, long_about = None)]
|
||||
#[command(author, version, about)]
|
||||
struct Args {
|
||||
#[arg(long = "activation", default_value = "none")]
|
||||
#[arg(long, default_value = "none")]
|
||||
activation: String,
|
||||
|
||||
#[arg(long = "batch_size", default_value = "50")]
|
||||
#[arg(long, default_value = "64")]
|
||||
batch_size: usize,
|
||||
|
||||
#[arg(long = "epochs", default_value = "10")]
|
||||
#[arg(long, default_value = "10")]
|
||||
epochs: usize,
|
||||
|
||||
#[arg(long = "hidden_layer_size", default_value = "100")]
|
||||
#[arg(long, default_value = "100")]
|
||||
hidden_layer_size: usize,
|
||||
|
||||
#[arg(long = "hidden_layers", default_value = "1")]
|
||||
#[arg(long, default_value = "1")]
|
||||
hidden_layers: usize,
|
||||
|
||||
#[arg(long = "seed", default_value = "42")]
|
||||
#[arg(long, default_value = "42")]
|
||||
seed: u64,
|
||||
|
||||
#[arg(long = "threads", default_value = "1")]
|
||||
threads: usize,
|
||||
/// Fraction of training data used for validation (e.g. 0.1 = 10 %)
|
||||
#[arg(long, default_value = "0.1")]
|
||||
valid_split: f64,
|
||||
}
|
||||
|
||||
/// Load MNIST images and labels for training.
|
||||
/// Returns (images [N, 784], labels [N]) where labels are class indices 0-9.
|
||||
fn load_mnist_labeled(
|
||||
examples: usize,
|
||||
device: &<B as Backend>::Device,
|
||||
) -> (Tensor<B, 2>, Tensor<B, 1, burn::tensor::Int>) {
|
||||
fn load_mnist_items(examples: usize) -> Vec<MnistItem> {
|
||||
let file = File::open("mnist.npz").expect("Cannot open mnist.npz");
|
||||
let mut archive = zip::ZipArchive::new(file).expect("Cannot read zip");
|
||||
|
||||
// Load images
|
||||
let image_candidates = [
|
||||
"train_images.npy",
|
||||
"train.images.npy",
|
||||
"x_train.npy",
|
||||
"images.npy",
|
||||
];
|
||||
// images
|
||||
let image_candidates = ["train_images.npy", "train.images.npy", "x_train.npy", "images.npy"];
|
||||
let mut image_bytes = Vec::new();
|
||||
let mut found_images = false;
|
||||
for name in &image_candidates {
|
||||
if let Ok(mut entry) = archive.by_name(name) {
|
||||
entry.read_to_end(&mut image_bytes).expect("Failed to read images");
|
||||
found_images = true;
|
||||
entry.read_to_end(&mut image_bytes).expect("read images");
|
||||
break;
|
||||
}
|
||||
}
|
||||
assert!(found_images, "Could not find train images in mnist.npz");
|
||||
assert!(!image_bytes.is_empty(), "Could not find train images in mnist.npz");
|
||||
|
||||
// Load labels
|
||||
let label_candidates = [
|
||||
"train_labels.npy",
|
||||
"train.labels.npy",
|
||||
"y_train.npy",
|
||||
"labels.npy",
|
||||
];
|
||||
// labels
|
||||
let label_candidates = ["train_labels.npy", "train.labels.npy", "y_train.npy", "labels.npy"];
|
||||
let mut label_bytes = Vec::new();
|
||||
let mut found_labels = false;
|
||||
for name in &label_candidates {
|
||||
if let Ok(mut entry) = archive.by_name(name) {
|
||||
entry.read_to_end(&mut label_bytes).expect("Failed to read labels");
|
||||
found_labels = true;
|
||||
entry.read_to_end(&mut label_bytes).expect("read labels");
|
||||
break;
|
||||
}
|
||||
}
|
||||
assert!(found_labels, "Could not find train labels in mnist.npz");
|
||||
assert!(!label_bytes.is_empty(), "Could not find train labels in mnist.npz");
|
||||
|
||||
// Parse images
|
||||
let image_npy = npyz::NpyFile::new(&image_bytes[..]).expect("Cannot parse images npy");
|
||||
// parse
|
||||
let image_npy = npyz::NpyFile::new(&image_bytes[..]).expect("parse images");
|
||||
let image_shape = image_npy.shape().to_vec();
|
||||
let image_raw: Vec<u8> = image_npy.into_vec().expect("Failed to read images as u8");
|
||||
let image_raw: Vec<u8> = image_npy.into_vec().expect("images to vec");
|
||||
let n = examples.min(image_shape[0] as usize);
|
||||
let pixels = image_raw.len() / image_shape[0] as usize;
|
||||
let pixels = image_raw.len() / image_shape[0] as usize; // should be 784
|
||||
assert_eq!(pixels, 784, "Expected 784 pixels per image, got {pixels}");
|
||||
|
||||
let image_data: Vec<f64> = image_raw[..n * pixels]
|
||||
.iter()
|
||||
.map(|&p| p as f64 / 255.0)
|
||||
.collect();
|
||||
let label_npy = npyz::NpyFile::new(&label_bytes[..]).expect("parse labels");
|
||||
let label_raw: Vec<u8> = label_npy.into_vec().expect("labels to vec");
|
||||
|
||||
let image_tensor_data = burn::tensor::TensorData::new(image_data, [n, pixels]);
|
||||
let images = Tensor::<B, 2>::from_data(image_tensor_data, device);
|
||||
|
||||
// Parse labels
|
||||
let label_npy = npyz::NpyFile::new(&label_bytes[..]).expect("Cannot parse labels npy");
|
||||
let label_raw: Vec<u8> = label_npy.into_vec().expect("Failed to read labels as u8");
|
||||
|
||||
let label_data: Vec<i64> = label_raw[..n]
|
||||
.iter()
|
||||
.map(|&p| p as i64)
|
||||
.collect();
|
||||
|
||||
let label_tensor_data = burn::tensor::TensorData::new(label_data, [n]);
|
||||
let labels = Tensor::<B, 1, burn::tensor::Int>::from_data(label_tensor_data, device);
|
||||
|
||||
(images, labels)
|
||||
// build items
|
||||
(0..n)
|
||||
.map(|i| {
|
||||
let mut image = [0f64; 784];
|
||||
for (j, &px) in image_raw[i * 784..(i + 1) * 784].iter().enumerate() {
|
||||
image[j] = px as f64 / 255.0;
|
||||
}
|
||||
MnistItem { image, label: label_raw[i] }
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
fn main() {
|
||||
@@ -116,18 +85,31 @@ fn main() {
|
||||
let device = burn_ndarray::NdArrayDevice::Cpu;
|
||||
let activation = Activation::from_str(&args.activation).unwrap_or_default();
|
||||
|
||||
let mut model = MnistClassifier::<B>::new(
|
||||
&device,
|
||||
args.hidden_layers,
|
||||
args.hidden_layer_size,
|
||||
activation,
|
||||
);
|
||||
println!("Loading MNIST…");
|
||||
let all_items = load_mnist_items(60_000);
|
||||
|
||||
let mut optim = AdamConfig::new().init::<B, MnistClassifier<B>>();
|
||||
let (images, labels) = load_mnist_labeled(60000, &device);
|
||||
// Split into train / validation
|
||||
let valid_n = (all_items.len() as f64 * args.valid_split) as usize;
|
||||
let train_n = all_items.len() - valid_n;
|
||||
let mut items = all_items;
|
||||
let valid_items = items.split_off(train_n); // last `valid_n` items
|
||||
let train_items = items;
|
||||
|
||||
println!("Starting training...");
|
||||
println!("Train: {} Valid: {}", train_items.len(), valid_items.len());
|
||||
|
||||
// Main just tells the model to run the process
|
||||
model.train_and_evaluate(images, labels, &mut optim, args.epochs, args.batch_size);
|
||||
let train_dataset = MnistDataset::new(train_items);
|
||||
let valid_dataset = MnistDataset::new(valid_items);
|
||||
|
||||
let config = MnistTrainingConfig::new(
|
||||
MnistModelConfig::new(args.hidden_layers, args.hidden_layer_size, activation),
|
||||
AdamConfig::new(),
|
||||
)
|
||||
.with_num_epochs(args.epochs)
|
||||
.with_batch_size(args.batch_size)
|
||||
.with_num_workers(1) // NdArray backend is single-threaded; keep at 1
|
||||
.with_seed(args.seed)
|
||||
.with_learning_rate(1e-3);
|
||||
|
||||
println!("Starting training…");
|
||||
config.train::<B>(device, train_dataset, valid_dataset);
|
||||
}
|
||||
|
||||
5987
hod_2/Cargo.lock
generated
Normal file
5987
hod_2/Cargo.lock
generated
Normal file
File diff suppressed because it is too large
Load Diff
16
hod_2/Cargo.toml
Normal file
16
hod_2/Cargo.toml
Normal file
@@ -0,0 +1,16 @@
|
||||
[package]
|
||||
name = "hod_2"
|
||||
version = "0.1.0"
|
||||
edition = "2024"
|
||||
|
||||
[dependencies]
|
||||
burn = { version = "0.20.1", default-features = false, features = ["ndarray", "std", "train"] }
|
||||
burn-autodiff = "0.20.1"
|
||||
burn-ndarray = "0.20.1"
|
||||
clap = { version = "4.5.60", features = ["derive"] }
|
||||
ndarray = "0.17.2"
|
||||
npyz = { version = "0.8.4", features = ["npz"] }
|
||||
rand = "0.10.0"
|
||||
rand_distr = "0.6.0"
|
||||
serde = { version = "1.0.228", features = ["derive"] }
|
||||
zip = { version = "8.2.0", features = ["deflate"] }
|
||||
50
hod_2/plan.md
Normal file
50
hod_2/plan.md
Normal file
@@ -0,0 +1,50 @@
|
||||
## Phase 1: Core Data Structures
|
||||
|
||||
**`src/model.rs`** - Manual parameter management
|
||||
- `struct Parameters<B: Backend>`: holds `w1, b1, w2, b2` as `Tensor<B, 2>`
|
||||
- `impl Parameters`: initialization with `randn(0.1)` for weights, zeros for biases
|
||||
- No `nn.Linear`—manual tensors to match the Python exercise
|
||||
|
||||
## Phase 2: Forward Pass
|
||||
|
||||
**`src/forward.rs`** or in `model.rs`
|
||||
- `fn forward<B: Backend>(params: &Parameters<B>, images: Tensor<B, 2>) -> Tensor<B, 2>`
|
||||
- Cast `uint8` images to `f32`, divide by 255, flatten to `[batch, 784]`
|
||||
- `hidden = tanh(images @ w1 + b1)`
|
||||
- `logits = hidden @ w2 + b2`
|
||||
- Return raw logits (no softmax here)
|
||||
|
||||
## Phase 3: Loss Computation
|
||||
|
||||
**`src/loss.rs`**
|
||||
- `fn cross_entropy_loss<B: Backend>(logits: Tensor<B, 2>, labels: Tensor<B, 1, Int>) -> Tensor<B, 0>`
|
||||
- Manual implementation—no `CrossEntropyLoss` module
|
||||
- `softmax = exp(logits - max) / sum(exp(logits - max))`
|
||||
- Index `softmax` by gold labels to get `p_correct`
|
||||
- `loss = -mean(log(p_correct))`
|
||||
|
||||
## Phase 4: Backward Pass & SGD
|
||||
|
||||
**`src/train.rs`**
|
||||
- `fn train_epoch<B: Backend>(params: &mut Parameters<B>, dataset: &[MnistItem], args: &Args)`
|
||||
- For each batch:
|
||||
1. `let loss = cross_entropy_loss(forward(¶ms, images), labels)`
|
||||
2. `let grads = loss.backward()` — automatic differentiation
|
||||
3. **Manual SGD**: `param = param - lr * grad` for each parameter
|
||||
4. No `Optimizer`—raw gradient descent like Python
|
||||
|
||||
## Phase 5: Evaluation
|
||||
|
||||
**`src/eval.rs`**
|
||||
- `fn evaluate<B: Backend>(params: &Parameters<B>, dataset: &[MnistItem]) -> f64`
|
||||
- `argmax` on logits, compare to labels, return accuracy
|
||||
|
||||
## Phase 6: Main Training Loop
|
||||
|
||||
**Update `src/main.rs`**
|
||||
- Parse args ✓ (done)
|
||||
- Load data ✓ (done)
|
||||
- Initialize `Parameters` with seed
|
||||
- Loop `args.epochs`: `train_epoch` → `evaluate(dev)` → print
|
||||
- Final `evaluate(test)`
|
||||
|
||||
1
hod_2/src/lib.rs
Normal file
1
hod_2/src/lib.rs
Normal file
@@ -0,0 +1 @@
|
||||
pub mod model;
|
||||
79
hod_2/src/main.rs
Normal file
79
hod_2/src/main.rs
Normal file
@@ -0,0 +1,79 @@
|
||||
use clap::Parser;
|
||||
use std::fs::File;
|
||||
use std::io::{Cursor, Read};
|
||||
|
||||
#[derive(Parser, Debug)]
|
||||
#[command(author, version, about)]
|
||||
struct Args {
|
||||
#[arg(long, default_value_t = 50)]
|
||||
batch_size: usize,
|
||||
|
||||
#[arg(long, default_value_t = 10)]
|
||||
epochs: usize,
|
||||
|
||||
#[arg(long, default_value_t = 100)]
|
||||
hidden_layer_size: usize,
|
||||
|
||||
#[arg(long, default_value_t = 0.1)]
|
||||
learning_rate: f64,
|
||||
|
||||
#[arg(long, default_value_t = 42)]
|
||||
seed: u64,
|
||||
|
||||
#[arg(long, default_value_t = 1)]
|
||||
threads: usize,
|
||||
}
|
||||
|
||||
fn load_mnist_items(path: &str, examples: usize) -> Vec<(Vec<f32>, u8)> {
|
||||
let file = File::open(path).expect("Cannot open mnist.npz");
|
||||
let mut archive = zip::ZipArchive::new(file).expect("Cannot read zip");
|
||||
|
||||
let image_names = ["train_images.npy", "train.images.npy", "x_train.npy", "images.npy"];
|
||||
let mut image_bytes = Vec::new();
|
||||
for name in &image_names {
|
||||
if let Ok(mut entry) = archive.by_name(name) {
|
||||
entry.read_to_end(&mut image_bytes).unwrap();
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
let label_names = ["train_labels.npy", "train.labels.npy", "y_train.npy", "labels.npy"];
|
||||
let mut label_bytes = Vec::new();
|
||||
for name in &label_names {
|
||||
if let Ok(mut entry) = archive.by_name(name) {
|
||||
entry.read_to_end(&mut label_bytes).unwrap();
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
let images_npy = npyz::NpyFile::new(Cursor::new(&image_bytes)).unwrap();
|
||||
let shape = images_npy.shape().to_vec();
|
||||
let n = shape[0] as usize;
|
||||
let pixels = shape[1..].iter().product::<u64>() as usize;
|
||||
let image_raw: Vec<u8> = images_npy.into_vec().unwrap();
|
||||
|
||||
let labels_npy = npyz::NpyFile::new(Cursor::new(&label_bytes)).unwrap();
|
||||
let label_raw: Vec<u8> = labels_npy.into_vec().unwrap();
|
||||
|
||||
(0..n.min(examples))
|
||||
.map(|i| {
|
||||
let image: Vec<f32> = image_raw[i * pixels..(i + 1) * pixels]
|
||||
.iter()
|
||||
.map(|&p| p as f32 / 255.0)
|
||||
.collect();
|
||||
(image, label_raw[i])
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
fn main() {
|
||||
let args = Args::parse();
|
||||
|
||||
println!("Loading MNIST...");
|
||||
let train_items = load_mnist_items("mnist.npz", 55_000);
|
||||
let dev_items = load_mnist_items("mnist.npz", 5_000);
|
||||
let test_items = load_mnist_items("mnist.npz", 10_000);
|
||||
|
||||
println!("Train: {}, Dev: {}, Test: {}", train_items.len(), dev_items.len(), test_items.len());
|
||||
println!("Args: {:?}", args);
|
||||
}
|
||||
64
hod_2/src/model.rs
Normal file
64
hod_2/src/model.rs
Normal file
@@ -0,0 +1,64 @@
|
||||
use burn::tensor::{backend::Backend, Tensor};
|
||||
use rand::{rngs::StdRng, SeedableRng};
|
||||
use rand_distr::{Distribution, Normal};
|
||||
|
||||
/// Manual neural network parameters for SGD backpropagation.
|
||||
/// No nn.Linear — just raw tensors to match the Python exercise.
|
||||
pub struct Parameters<B: Backend> {
|
||||
/// First layer weights: [784, hidden_layer_size]
|
||||
pub w1: Tensor<B, 2>,
|
||||
/// First layer biases: [hidden_layer_size]
|
||||
pub b1: Tensor<B, 1>,
|
||||
/// Second layer weights: [hidden_layer_size, 10]
|
||||
pub w2: Tensor<B, 2>,
|
||||
/// Second layer biases: [10]
|
||||
pub b2: Tensor<B, 1>,
|
||||
}
|
||||
|
||||
impl<B: Backend> Parameters<B> {
|
||||
/// Initialize parameters with given hidden size and random seed.
|
||||
/// Weights: randn * 0.1, Biases: zeros
|
||||
pub fn new(device: &B::Device, hidden_size: usize, seed: u64) -> Self {
|
||||
let w1 = random_tensor([784, hidden_size], 0.1, seed, device);
|
||||
let b1 = Tensor::zeros([hidden_size], device);
|
||||
|
||||
let w2 = random_tensor([hidden_size, 10], 0.1, seed.wrapping_add(1), device);
|
||||
let b2 = Tensor::zeros([10], device);
|
||||
|
||||
Self { w1, b1, w2, b2 }
|
||||
}
|
||||
|
||||
/// Get all parameters as a vector for gradient updates.
|
||||
/// Order: w1, b1, w2, b2
|
||||
pub fn to_vec(&self) -> Vec<ParamRef<B>> {
|
||||
vec![
|
||||
ParamRef::TwoD(self.w1.clone()),
|
||||
ParamRef::OneD(self.b1.clone()),
|
||||
ParamRef::TwoD(self.w2.clone()),
|
||||
ParamRef::OneD(self.b2.clone()),
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
/// Helper enum to handle 1D and 2D parameters uniformly.
|
||||
pub enum ParamRef<B: Backend> {
|
||||
OneD(Tensor<B, 1>),
|
||||
TwoD(Tensor<B, 2>),
|
||||
}
|
||||
|
||||
/// Create a random tensor with normal distribution, scaled by std_dev.
|
||||
fn random_tensor<B: Backend, const D: usize>(
|
||||
shape: [usize; D],
|
||||
std_dev: f64,
|
||||
seed: u64,
|
||||
device: &B::Device,
|
||||
) -> Tensor<B, D> {
|
||||
|
||||
let dist = Normal::new(0.0, std_dev).unwrap();
|
||||
let mut rng = StdRng::seed_from_u64(seed);
|
||||
|
||||
let total: usize = shape.iter().product();
|
||||
let data: Vec<f64> = (0..total).map(|_| dist.sample(&mut rng)).collect();
|
||||
|
||||
Tensor::from_floats(burn::tensor::TensorData::new(data, shape), device)
|
||||
}
|
||||
Reference in New Issue
Block a user