From ca8ee0724d7e7323a054e858c3d292da410f95a1 Mon Sep 17 00:00:00 2001 From: Nathaniel Simard Date: Wed, 5 Apr 2023 12:38:53 -0400 Subject: [PATCH] Refactor/optim (#272) --- burn-core/src/module/mod.rs | 2 - burn-core/src/module/param/id.rs | 6 + burn-core/src/module/param/primitive.rs | 5 +- burn-core/src/module/state.rs | 105 ------- burn-core/src/optim/adam.rs | 276 ++++++++++--------- burn-core/src/optim/base.rs | 113 +------- burn-core/src/optim/decay.rs | 60 ++-- burn-core/src/optim/mapper.rs | 29 -- burn-core/src/optim/mod.rs | 3 +- burn-core/src/optim/momentum.rs | 65 ++--- burn-core/src/optim/sgd.rs | 169 +++++------- burn-core/src/optim/simple/adaptor.rs | 105 +++++++ burn-core/src/optim/simple/base.rs | 32 +++ burn-core/src/optim/simple/mod.rs | 5 + burn-core/src/optim/simple/record/base.rs | 68 +++++ burn-core/src/optim/simple/record/mod.rs | 5 + burn-core/src/optim/simple/record/v1.rs | 134 +++++++++ burn-core/src/optim/visitor.rs | 44 +-- burn-core/src/record/base.rs | 4 +- burn-core/src/record/file.rs | 47 +++- burn-core/src/record/primitive.rs | 40 ++- burn-train/src/checkpoint/base.rs | 6 +- burn-train/src/checkpoint/file.rs | 16 +- burn-train/src/learner/base.rs | 16 +- burn-train/src/learner/builder.rs | 21 +- burn-train/src/learner/epoch.rs | 6 +- examples/mnist-inference-web/model.bin | Bin 1508719 -> 1508725 bytes examples/mnist/src/training.rs | 6 +- examples/text-classification/src/training.rs | 4 +- examples/text-generation/src/training.rs | 4 +- 30 files changed, 744 insertions(+), 652 deletions(-) delete mode 100644 burn-core/src/module/state.rs delete mode 100644 burn-core/src/optim/mapper.rs create mode 100644 burn-core/src/optim/simple/adaptor.rs create mode 100644 burn-core/src/optim/simple/base.rs create mode 100644 burn-core/src/optim/simple/mod.rs create mode 100644 burn-core/src/optim/simple/record/base.rs create mode 100644 burn-core/src/optim/simple/record/mod.rs create mode 100644 burn-core/src/optim/simple/record/v1.rs diff --git a/burn-core/src/module/mod.rs b/burn-core/src/module/mod.rs index 103f712ef4..60d1567523 100644 --- a/burn-core/src/module/mod.rs +++ b/burn-core/src/module/mod.rs @@ -1,7 +1,5 @@ mod base; mod param; -mod state; pub use base::*; pub use param::*; -pub use state::*; diff --git a/burn-core/src/module/param/id.rs b/burn-core/src/module/param/id.rs index 2d79fe1ff8..3c4e36fa25 100644 --- a/burn-core/src/module/param/id.rs +++ b/burn-core/src/module/param/id.rs @@ -17,6 +17,12 @@ impl From<&str> for ParamId { } } +impl From for ParamId { + fn from(value: String) -> Self { + Self { value } + } +} + impl Default for ParamId { fn default() -> Self { Self::new() diff --git a/burn-core/src/module/param/primitive.rs b/burn-core/src/module/param/primitive.rs index 2c2034d363..7007474e9c 100644 --- a/burn-core/src/module/param/primitive.rs +++ b/burn-core/src/module/param/primitive.rs @@ -145,6 +145,7 @@ where impl Module for [T; N] where T: Module + Debug + Send + Sync + Clone + Copy, + T::Record: Debug, B: Backend, { type Record = [T::Record; N]; @@ -201,7 +202,9 @@ where impl ADModule for [T; N] where T: ADModule + Debug + Send + Sync + Clone + Copy, - T::InnerModule: Copy, + T::InnerModule: Copy + Debug, + >::Record: Debug, + >::Record: Debug, B: ADBackend, { type InnerModule = [T::InnerModule; N]; diff --git a/burn-core/src/module/state.rs b/burn-core/src/module/state.rs deleted file mode 100644 index 69baede6e8..0000000000 --- a/burn-core/src/module/state.rs +++ /dev/null @@ -1,105 +0,0 @@ -use alloc::{ - format, - string::{String, ToString}, -}; - -use super::ParamId; -use crate::tensor::{DataSerialize, Element}; -use serde::{Deserialize, Serialize}; - -#[cfg(not(feature = "std"))] -use hashbrown::HashMap; -#[cfg(feature = "std")] -use std::collections::HashMap; - -#[derive(Debug, PartialEq, Eq, Clone, Default, Serialize, Deserialize)] -pub struct StateNamed { - pub values: HashMap>, -} - -#[derive(Debug, PartialEq, Eq, Clone, Serialize, Deserialize)] -pub enum State { - StateNamed(StateNamed), - Data(DataSerialize), - ParamId(ParamId), -} - -#[derive(Debug)] -pub enum StateError { - InvalidFormat(String), - Unknown(String), -} - -impl core::fmt::Display for StateError { - fn fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result { - let mut message = "State error => ".to_string(); - - match self { - Self::InvalidFormat(err) => { - message += format!("Invalid format: {err}").as_str(); - } - Self::Unknown(err) => { - message += format!("Unknown error: {err}").as_str(); - } - }; - - f.write_str(message.as_str()) - } -} - -impl StateNamed { - pub fn new() -> Self { - Self { - values: HashMap::new(), - } - } - - pub fn register_state(&mut self, name: &str, state: State) { - self.values.insert(name.to_string(), state); - } -} - -impl StateNamed { - pub fn get(&self, name: &str) -> Option<&State> { - self.values.get(name) - } - - pub fn is_empty(&self) -> bool { - self.values.is_empty() - } - - pub fn convert(self) -> StateNamed { - let mut values = HashMap::with_capacity(self.values.len()); - - for (key, value) in self.values { - values.insert(key, value.convert()); - } - - StateNamed { values } - } -} - -impl State { - pub fn get(&self, name: &str) -> Option<&Self> { - match self { - State::StateNamed(named) => named.get(name), - _ => None, - } - } - - pub fn is_empty(&self) -> bool { - match self { - State::StateNamed(named) => named.is_empty(), - State::Data(_) => false, - State::ParamId(_) => false, - } - } - - pub fn convert(self) -> State { - match self { - State::StateNamed(named) => State::StateNamed(named.convert()), - State::Data(data) => State::Data(data.convert()), - State::ParamId(id) => State::ParamId(id), - } - } -} diff --git a/burn-core/src/optim/adam.rs b/burn-core/src/optim/adam.rs index 20abbe1601..00de2ec6e7 100644 --- a/burn-core/src/optim/adam.rs +++ b/burn-core/src/optim/adam.rs @@ -1,14 +1,13 @@ -use crate::{self as burn, module::ADModule}; +use crate::{self as burn, module::ADModule, record::Record}; use super::{ - decay::{WeightDecay, WeightDecayConfig}, - load_state_gradients, register_state_gradients, GradientsParams, + decay::{WeightDecay, WeightDecayConfig, WeightDecayState}, + Optimizer, SimpleOptimizer, }; use crate::config::Config; -use crate::module::{ParamId, StateNamed}; -use crate::optim::Optimizer; +use crate::optim::adaptor::OptimizerAdaptor; use crate::tensor::{backend::ADBackend, Tensor}; -use burn_tensor::ElementConversion; +use burn_tensor::{backend::Backend, ElementConversion}; #[derive(Config)] pub struct AdamConfig { @@ -28,153 +27,140 @@ pub struct AdamConfig { } /// Adam optimizer as described in the paper [Adam: A Method for Stochastic Optimization](https://arxiv.org/pdf/1412.6980.pdf). -pub struct Adam { +pub struct Adam { learning_rate: B::FloatElem, momentum: AdaptiveMomentum, weight_decay: Option>, } -impl Adam { - pub fn new(config: &AdamConfig) -> Self { - Self { - learning_rate: config.learning_rate.elem(), - momentum: AdaptiveMomentum { - beta_1: config.beta_1, - beta_2: config.beta_2, - epsilon: config.epsilon, - time: GradientsParams::new(), - moment_1: GradientsParams::new(), - moment_2: GradientsParams::new(), - }, - weight_decay: config - .weight_decay - .as_ref() - .map(|config| WeightDecay::new(config)), - } - } +#[derive(Record, Clone, new)] +pub struct AdamState { + weight_decay: Option>, + momentum: AdaptiveMomentumState, } -impl, B: ADBackend> Optimizer for Adam { - fn update_tensor( - &mut self, - id: &ParamId, - tensor: Tensor, - grad: Tensor, - ) -> Tensor { - let grad = match &mut self.weight_decay { - Some(weight_decay) => weight_decay.transform(id, grad), - None => grad, - }; - let grad = self.momentum.transform::(id, grad); - let delta = grad.mul_scalar(self.learning_rate); - - Tensor::from_inner(tensor.inner() - delta) - } +impl SimpleOptimizer for Adam { + type State = AdamState; - fn register_param_state( + fn step( &self, - id: &ParamId, - state: &mut StateNamed, - ) { - self.momentum.register_state::(id, state); + tensor: Tensor, + mut grad: Tensor, + state: Option>, + ) -> (Tensor, Option>) { + let mut state_weight_decay = None; + let mut state_momemtum = None; + + if let Some(state) = state { + state_weight_decay = state.weight_decay; + state_momemtum = Some(state.momentum); + } if let Some(weight_decay) = &self.weight_decay { - weight_decay.register_state::(id, state); + let (grad_out, state) = weight_decay.transform(grad, state_weight_decay); + state_weight_decay = Some(state); + grad = grad_out; } + + let (grad, state_momemtum) = self.momentum.transform(grad, state_momemtum); + + let state = AdamState::new(state_weight_decay, state_momemtum); + let delta = grad.mul_scalar(self.learning_rate); + + (tensor - delta, Some(state)) } - fn load_param_state( - &mut self, - id: &ParamId, - state: &StateNamed, - device: &B::Device, - ) { - self.momentum.load_state::(id, state, device); + fn to_device( + mut state: Self::State, + device: &::Device, + ) -> Self::State { + state.weight_decay = state.weight_decay.map(|state| state.to_device(device)); + state.momentum = state.momentum.to_device(device); + state + } +} - if let Some(weight_decay) = &mut self.weight_decay { - weight_decay.load_state::(id, state, device); - } +impl AdamConfig { + pub fn init>(&self) -> impl Optimizer { + let optim = Adam { + learning_rate: self.learning_rate.elem(), + momentum: AdaptiveMomentum { + beta_1: self.beta_1, + beta_2: self.beta_2, + epsilon: self.epsilon, + }, + weight_decay: self.weight_decay.as_ref().map(WeightDecay::new), + }; + OptimizerAdaptor::from(optim) } } +#[derive(Record, new, Clone)] +pub struct AdaptiveMomentumState { + time: usize, + moment_1: Tensor, + moment_2: Tensor, +} + struct AdaptiveMomentum { beta_1: f32, beta_2: f32, epsilon: f32, - time: GradientsParams, - moment_1: GradientsParams, - moment_2: GradientsParams, } impl AdaptiveMomentum { - pub fn transform( - &mut self, - id: &ParamId, - grad: Tensor, - ) -> Tensor { - let factor = 1.0 - self.beta_1; - let moment_1 = match self.moment_1.remove::(id) { - Some(moment_last_step) => moment_last_step + pub fn transform( + &self, + grad: Tensor, + state: Option>, + ) -> (Tensor, AdaptiveMomentumState) { + let state = if let Some(mut state) = state { + let factor = 1.0 - self.beta_1; + state.moment_1 = state + .moment_1 .mul_scalar(self.beta_1) - .add(grad.clone().mul_scalar(factor)), - None => grad.clone().mul_scalar(factor), - }; + .add(grad.clone().mul_scalar(factor)); - let factor = 1.0 - self.beta_2; - let moment_2 = match self.moment_2.remove::(id) { - Some(moment_last_step) => moment_last_step + let factor = 1.0 - self.beta_2; + state.moment_2 = state + .moment_2 .mul_scalar(self.beta_2) - .add(grad.powf(2.0).mul_scalar(factor)), - None => grad.powf(2.0).mul_scalar(factor), - }; + .add(grad.powf(2.0).mul_scalar(factor)); - let time = match self.time.remove::(id) { - Some(time) => time.add_scalar(1), - None => Tensor::ones([1]), - }; - - self.moment_1.register(id.clone(), moment_1.clone()); - self.moment_2.register(id.clone(), moment_2.clone()); - self.time.register(id.clone(), time.clone()); + state.time += 1; - let time = time.single_value().elem(); - let moment_1_corrected = moment_1.div_scalar(1f32 - self.beta_1.powi(time)); - let moment_2_corrected = moment_2.div_scalar(1f32 - self.beta_2.powi(time)); + state + } else { + let factor = 1.0 - self.beta_1; + let moment_1 = grad.clone().mul_scalar(factor); - moment_1_corrected.div(moment_2_corrected.sqrt().add_scalar(self.epsilon)) - } + let factor = 1.0 - self.beta_2; + let moment_2 = grad.powf(2.0).mul_scalar(factor); - pub fn register_state( - &self, - id: &ParamId, - state: &mut StateNamed, - ) { - register_state_gradients::(id, state, &self.moment_1, Self::state_key_1); - register_state_gradients::(id, state, &self.moment_2, Self::state_key_2); - register_state_gradients::<1, B, _>(id, state, &self.time, Self::state_key_time); - } + AdaptiveMomentumState::new(1, moment_1, moment_2) + }; - pub fn load_state( - &mut self, - id: &ParamId, - state: &StateNamed, - device: &B::Device, - ) { - load_state_gradients::(id, state, &mut self.moment_1, Self::state_key_1, device); - load_state_gradients::(id, state, &mut self.moment_2, Self::state_key_2, device); - load_state_gradients::<1, B, _>(id, state, &mut self.time, Self::state_key_time, device); - } + let time = (state.time as i32).elem(); + let moment_1_corrected = state + .moment_1 + .clone() + .div_scalar(1f32 - self.beta_1.powi(time)); + let moment_2_corrected = state + .moment_2 + .clone() + .div_scalar(1f32 - self.beta_2.powi(time)); - fn state_key_1(id: &ParamId) -> String { - format!("moment_1-{id}") - } + let grad = moment_1_corrected.div(moment_2_corrected.sqrt().add_scalar(self.epsilon)); - fn state_key_2(id: &ParamId) -> String { - format!("moment_2-{id}") + (grad, state) } +} - fn state_key_time(id: &ParamId) -> String { - format!("time-{id}") +impl AdaptiveMomentumState { + pub fn to_device(mut self, device: &B::Device) -> Self { + self.moment_1 = self.moment_1.to_device(device); + self.moment_2 = self.moment_2.to_device(device); + self } } @@ -182,24 +168,31 @@ impl AdaptiveMomentum { mod tests { use super::*; use crate::module::{Module, Param}; + use crate::optim::{GradientsParams, Optimizer}; + use crate::record::DebugRecordSettings; use crate::tensor::{Data, Distribution, Tensor}; - use crate::{nn, TestADBackend}; + use crate::{nn, TestADBackend, TestBackend}; #[test] fn test_adam_optimizer_save_load_state() { let linear = nn::LinearConfig::new(6, 6).init(); let x = Tensor::::random([2, 6], Distribution::Standard); - let mut optimizer = Adam::new(&AdamConfig::new(0.01)); + let mut optimizer = create_adam(); let grads = linear.forward(x).backward(); let grads = GradientsParams::from_grads(grads, &linear); - let linear = optimizer.update_module(linear, grads); - - let state_optim_before = optimizer.state(&linear); - let mut optimizer = Adam::new(&AdamConfig::new(0.01)); - optimizer.load(&linear, &state_optim_before).unwrap(); - let state_optim_after = optimizer.state(&linear); - - assert_eq!(state_optim_before, state_optim_after); + let _linear = optimizer.step(linear, grads); + optimizer + .to_record() + .record::("/tmp/test_optim".into()) + .unwrap(); + + let state_optim_before = optimizer.to_record(); + let state_optim_before_copy = optimizer.to_record(); + let optimizer = create_adam(); + let optimizer = optimizer.load_record(state_optim_before_copy); + let state_optim_after = optimizer.to_record(); + + assert_eq!(state_optim_before.len(), state_optim_after.len()); } #[test] @@ -225,20 +218,20 @@ mod tests { [0.3270, 0.0412, 0.5538, 0.9605, 0.3195, 0.9085], ]) .require_grad(); - let mut optimizer = Adam::new( - &AdamConfig::new(0.01) - .with_epsilon(1e-8) - .with_beta_1(0.9) - .with_beta_2(0.999), - ); + + let mut optimizer = AdamConfig::new(0.01) + .with_epsilon(1e-8) + .with_beta_1(0.9) + .with_beta_2(0.999) + .init(); let grads = linear.forward(x_1).backward(); let grads = GradientsParams::from_grads(grads, &linear); - let linear = optimizer.update_module(linear, grads); + let linear = optimizer.step(linear, grads); let grads = linear.forward(x_2).backward(); let grads = GradientsParams::from_grads(grads, &linear); - let linear = optimizer.update_module(linear, grads); + let linear = optimizer.step(linear, grads); let state_updated = linear.into_record(); let state_expected = given_linear_record( @@ -281,4 +274,19 @@ mod tests { bias: Some(Param::from(Tensor::from_data(bias))), } } + + fn create_adam() -> OptimizerAdaptor, nn::Linear, TestADBackend> + { + let config = AdamConfig::new(0.01); + Adam { + learning_rate: config.learning_rate.elem(), + momentum: AdaptiveMomentum { + beta_1: config.beta_1, + beta_2: config.beta_2, + epsilon: config.epsilon, + }, + weight_decay: config.weight_decay.as_ref().map(WeightDecay::new), + } + .into() + } } diff --git a/burn-core/src/optim/base.rs b/burn-core/src/optim/base.rs index 92accb1e0a..5066147feb 100644 --- a/burn-core/src/optim/base.rs +++ b/burn-core/src/optim/base.rs @@ -1,114 +1,23 @@ -use super::mapper::ModuleTensorUpdater; -use super::visitor::{GradientsLoader, GradientsRegister}; use super::GradientsParams; - -use crate::module::{ADModule, LoadingError, ParamId, State, StateNamed}; +use crate::module::ADModule; +use crate::record::Record; use crate::tensor::backend::ADBackend; -use crate::tensor::{Data, Tensor}; +/// General trait to optimize [module](ADModule). pub trait Optimizer: Send + Sync where M: ADModule, B: ADBackend, { - /// Update the tensor parameter using the given the gradients. - fn update_tensor( - &mut self, - id: &ParamId, - tensor: Tensor, - grad: Tensor, - ) -> Tensor; - - /// Update the parameters of the given module using the given the gradients. - fn update_module(&mut self, module: M, grads: GradientsParams) -> M - where - Self: Sized, - { - let mut mapper = ModuleTensorUpdater::new(self, grads); - module.map(&mut mapper) - } - - /// Register the optimizer state for a given parameter. - /// - /// # Notes - /// - /// This should only be called by generated code. - fn register_param_state( - &self, - _id: &ParamId, - _state: &mut StateNamed, - ) { - // By default there is no state to register - } - - /// Load the optimizer state for a given parameter. - /// - /// # Notes - /// - /// This should only be called by generated code. - fn load_param_state( - &mut self, - _id: &ParamId, - _state: &StateNamed, - _device: &B::Device, - ) { - // By default there is no state to load - } - - /// Get the optimizer state for a given module. - fn state(&self, module: &M) -> State - where - Self: Sized, - { - let mut state_named = StateNamed::new(); - let mut visitor = GradientsRegister::new(self, &mut state_named); + /// Optimizer associative type to be used when saving and loading the state. + type Record: Record; - module.visit(&mut visitor); - State::StateNamed(state_named) - } + /// Perform the optimizer step using the given gradients. The updated module will be returned. + fn step(&mut self, module: M, grads: GradientsParams) -> M; - /// Load the optimizer state for a given module. - fn load(&mut self, module: &M, state: &State) -> Result<(), LoadingError> - where - Self: Sized, - { - let state_named = match state { - State::StateNamed(state) => state, - _ => { - return Err(LoadingError::new( - "Can't load state wrapper to fetch id and data".to_string(), - )) - } - }; - - let mut visitor = GradientsLoader::new(self, state_named); - module.visit(&mut visitor); - - Ok(()) - } -} - -pub(super) fn register_state_gradients String>( - id: &ParamId, - state: &mut StateNamed, - grads: &GradientsParams, - id_to_key: F, -) { - if let Some(grad) = grads.get::(id) { - let data = State::Data(grad.into_data().serialize()); - state.register_state(id_to_key(id).as_str(), data); - }; -} + /// Get the current state of the optimizer as a [record](Record). + fn to_record(&self) -> Self::Record; -pub(super) fn load_state_gradients String>( - id: &ParamId, - state: &StateNamed, - grads: &mut GradientsParams, - id_to_key: F, - device: &B::Device, -) { - if let Some(State::Data(data)) = state.get(id_to_key(id).as_str()) { - let tensor = Tensor::::from_data_device(Data::from(data), device); - grads.register::(id.clone(), tensor); - }; + /// Load the state of the optimizer as a [record](Record). + fn load_record(self, record: Self::Record) -> Self; } diff --git a/burn-core/src/optim/decay.rs b/burn-core/src/optim/decay.rs index 08ad0adfd7..f3024c064d 100644 --- a/burn-core/src/optim/decay.rs +++ b/burn-core/src/optim/decay.rs @@ -1,10 +1,9 @@ -use crate as burn; +use burn_tensor::backend::Backend; -use super::{load_state_gradients, register_state_gradients, GradientsParams}; +use crate as burn; +use crate::record::Record; use crate::config::Config; -use crate::module::{ParamId, StateNamed}; -use crate::tensor::backend::ADBackend; use crate::tensor::{ElementConversion, Tensor}; /// Configuration to create [WeightDecay](WeightDecay). @@ -14,53 +13,42 @@ pub struct WeightDecayConfig { pub penalty: f64, } +#[derive(Record, Clone, new)] +pub struct WeightDecayState { + grad_last_step: Tensor, +} + /// Weight decay implementation that transforms gradients. -pub struct WeightDecay { +pub struct WeightDecay { penalty: B::FloatElem, - gradients: GradientsParams, } -impl WeightDecay { +impl WeightDecay { pub fn new(config: &WeightDecayConfig) -> Self { Self { penalty: config.penalty.elem(), - gradients: GradientsParams::new(), } } pub fn transform( - &mut self, - id: &ParamId, - grad: Tensor, - ) -> Tensor { - let grad = match self.gradients.remove::(id) { - Some(grad_last_step) => grad_last_step.mul_scalar(self.penalty).add(grad), + &self, + grad: Tensor, + state: Option>, + ) -> (Tensor, WeightDecayState) { + let grad_last_step = grad.clone(); + + let grad = match state { + Some(state) => state.grad_last_step.mul_scalar(self.penalty).add(grad), None => grad, }; - // Update gradients - self.gradients.register(id.clone(), grad.clone()); - - grad - } - pub fn register_state( - &self, - id: &ParamId, - state: &mut StateNamed, - ) { - register_state_gradients::(id, state, &self.gradients, Self::state_key); - } - - pub fn load_state( - &mut self, - id: &ParamId, - state: &StateNamed, - device: &B::Device, - ) { - load_state_gradients::(id, state, &mut self.gradients, Self::state_key, device); + (grad, WeightDecayState::new(grad_last_step)) } +} - fn state_key(id: &ParamId) -> String { - format!("weight-decay-{id}") +impl WeightDecayState { + pub fn to_device(mut self, device: &B::Device) -> Self { + self.grad_last_step = self.grad_last_step.to_device(device); + self } } diff --git a/burn-core/src/optim/mapper.rs b/burn-core/src/optim/mapper.rs deleted file mode 100644 index 3ee044db92..0000000000 --- a/burn-core/src/optim/mapper.rs +++ /dev/null @@ -1,29 +0,0 @@ -use core::marker::PhantomData; - -use burn_tensor::{backend::ADBackend, Tensor}; - -use crate::module::{ADModule, ModuleMapper, ParamId}; - -use super::{GradientsParams, Optimizer}; - -#[derive(new)] -pub struct ModuleTensorUpdater<'a, M, O> { - optimizer: &'a mut O, - grads: GradientsParams, - phatom: PhantomData, -} - -impl<'a, M, B, O> ModuleMapper for ModuleTensorUpdater<'a, M, O> -where - M: ADModule, - B: ADBackend, - O: Optimizer, -{ - fn map(&mut self, id: &ParamId, tensor: Tensor) -> Tensor { - if let Some(grad) = self.grads.remove::(id) { - self.optimizer.update_tensor(id, tensor, grad) - } else { - tensor - } - } -} diff --git a/burn-core/src/optim/mod.rs b/burn-core/src/optim/mod.rs index 2ac9003435..1b4dd0dc76 100644 --- a/burn-core/src/optim/mod.rs +++ b/burn-core/src/optim/mod.rs @@ -5,8 +5,8 @@ mod adam; mod base; mod grad_accum; mod grads; -mod mapper; mod sgd; +mod simple; mod visitor; pub use adam::*; @@ -14,3 +14,4 @@ pub use base::*; pub use grad_accum::*; pub use grads::*; pub use sgd::*; +pub use simple::*; diff --git a/burn-core/src/optim/momentum.rs b/burn-core/src/optim/momentum.rs index 8618c757c9..7d8cf89f8c 100644 --- a/burn-core/src/optim/momentum.rs +++ b/burn-core/src/optim/momentum.rs @@ -1,10 +1,9 @@ use crate as burn; -use super::{load_state_gradients, register_state_gradients, GradientsParams}; use crate::config::Config; -use crate::module::{ParamId, StateNamed}; -use crate::tensor::backend::ADBackend; +use crate::record::Record; use crate::tensor::{ElementConversion, Tensor}; +use burn_tensor::backend::Backend; /// Configuration to create momentum [Momentum](Momentum). #[derive(Config)] @@ -20,66 +19,52 @@ pub struct MomentumConfig { pub nesterov: bool, } +#[derive(Record, Clone, new)] +pub struct MomemtumState { + velocity: Tensor, +} + /// Momemtum implementation that transforms gradients. -pub struct Momentum { +pub struct Momentum { momentum: B::FloatElem, dampening: f64, nesterov: bool, - velocity: GradientsParams, } -impl Momentum { +impl Momentum { pub fn new(config: &MomentumConfig) -> Self { Self { momentum: config.momentum.elem(), dampening: config.dampening, - velocity: GradientsParams::new(), nesterov: config.nesterov, } } pub fn transform( - &mut self, - id: &ParamId, - grad: Tensor, - ) -> Tensor { - let velocity = match self.velocity.remove::(id) { - Some(grad_last_step) => grad - .clone() + &self, + grad: Tensor, + state: Option>, + ) -> (Tensor, MomemtumState) { + let velocity = if let Some(state) = state { + grad.clone() .mul_scalar(1.0 - self.dampening) - .add(grad_last_step.mul_scalar(self.momentum)), - None => grad.clone(), + .add(state.velocity.mul_scalar(self.momentum)) + } else { + grad.clone() }; - let output = match self.nesterov { + let grad = match self.nesterov { true => velocity.clone().mul_scalar(self.momentum).add(grad), false => velocity.clone(), }; - // Update velocity - self.velocity.register(id.clone(), velocity); - - output - } - - pub fn register_state( - &self, - id: &ParamId, - state: &mut StateNamed, - ) { - register_state_gradients::(id, state, &self.velocity, Self::state_key); - } - - pub fn load_state( - &mut self, - id: &ParamId, - state: &StateNamed, - device: &B::Device, - ) { - load_state_gradients::(id, state, &mut self.velocity, Self::state_key, device); + (grad, MomemtumState::new(velocity)) } +} - fn state_key(id: &ParamId) -> String { - format!("momentum-{id}") +impl MomemtumState { + pub fn to_device(mut self, device: &B::Device) -> Self { + self.velocity = self.velocity.to_device(device); + self } } diff --git a/burn-core/src/optim/sgd.rs b/burn-core/src/optim/sgd.rs index 209f8b8953..3de4be6ca1 100644 --- a/burn-core/src/optim/sgd.rs +++ b/burn-core/src/optim/sgd.rs @@ -1,12 +1,14 @@ use crate as burn; +use crate::module::ADModule; -use super::decay::{WeightDecay, WeightDecayConfig}; -use super::momentum::{Momentum, MomentumConfig}; +use super::decay::{WeightDecay, WeightDecayConfig, WeightDecayState}; +use super::momentum::{MomemtumState, Momentum, MomentumConfig}; +use super::SimpleOptimizer; use crate::config::Config; -use crate::module::{ADModule, ParamId, StateNamed}; -use crate::optim::Optimizer; -use crate::tensor::backend::ADBackend; +use crate::optim::adaptor::OptimizerAdaptor; +use crate::record::Record; use crate::tensor::{ElementConversion, Tensor}; +use burn_tensor::backend::{ADBackend, Backend}; /// Configuration to create the [Sgd](Sgd) optimizer. #[derive(Config)] @@ -22,76 +24,74 @@ pub struct SgdConfig { /// Optimizer that implements stochastic gradient descent with momentum. /// /// Momentum is optinal and can be [configured](SgdConfig::momentum). -pub struct Sgd { +pub struct Sgd { learning_rate: B::FloatElem, momentum: Option>, weight_decay: Option>, } -impl Sgd { - pub fn new(config: &SgdConfig) -> Self { - let learning_rate = config.learning_rate.elem(); - let momentum = config.momentum.as_ref().map(|config| Momentum::new(config)); - let weight_decay = config - .weight_decay - .as_ref() - .map(|config| WeightDecay::new(config)); +#[derive(Record, Clone, new)] +pub struct SgdState { + weight_decay: Option>, + momentum: Option>, +} + +impl SgdConfig { + pub fn init>( + &self, + ) -> OptimizerAdaptor, M, B> { + let learning_rate = self.learning_rate.elem(); + let momentum = self.momentum.as_ref().map(Momentum::new); + let weight_decay = self.weight_decay.as_ref().map(WeightDecay::new); - Self { + Sgd { learning_rate, momentum, weight_decay, } + .into() } } -impl, B: ADBackend> Optimizer for Sgd { - fn update_tensor( - &mut self, - id: &ParamId, - tensor: Tensor, - grad: Tensor, - ) -> Tensor { - let grad = match &mut self.weight_decay { - Some(weight_decay) => weight_decay.transform(id, grad), - None => grad, - }; - let grad = match &mut self.momentum { - Some(momentum) => momentum.transform(id, grad), - None => grad, - }; - let delta = grad.mul_scalar(self.learning_rate); +impl SimpleOptimizer for Sgd { + type State = SgdState; - Tensor::from_inner(tensor.inner() - delta) - } - - fn register_param_state( + fn step( &self, - id: &ParamId, - state: &mut StateNamed, - ) { - if let Some(momentum) = &self.momentum { - momentum.register_state::(id, state); + tensor: Tensor, + mut grad: Tensor, + state: Option>, + ) -> (Tensor, Option>) { + let mut state_weight_decay = None; + let mut state_momemtum = None; + + if let Some(state) = state { + state_weight_decay = state.weight_decay; + state_momemtum = state.momentum; } if let Some(weight_decay) = &self.weight_decay { - weight_decay.register_state::(id, state); + let (grad_out, state) = weight_decay.transform(grad, state_weight_decay); + state_weight_decay = Some(state); + grad = grad_out; } - } - fn load_param_state( - &mut self, - id: &ParamId, - state: &StateNamed, - device: &B::Device, - ) { - if let Some(momentum) = &mut self.momentum { - momentum.load_state::(id, state, device); + if let Some(momentum) = &self.momentum { + let (grad_out, state) = momentum.transform(grad, state_momemtum); + state_momemtum = Some(state); + grad = grad_out; } - if let Some(weight_decay) = &mut self.weight_decay { - weight_decay.load_state::(id, state, device); - } + let state = SgdState::new(state_weight_decay, state_momemtum); + let delta = grad.mul_scalar(self.learning_rate); + + (tensor - delta, Some(state)) + } + + fn to_device(mut state: Self::State, device: &B::Device) -> Self::State { + state.weight_decay = state.weight_decay.map(|state| state.to_device(device)); + state.momentum = state.momentum.map(|state| state.to_device(device)); + state } } @@ -100,9 +100,9 @@ mod tests { use super::*; use crate::{ nn::{Linear, LinearConfig}, - optim::GradientsParams, + optim::{GradientsParams, Optimizer}, tensor::{Distribution, Shape}, - TestADBackend, + TestADBackend, TestBackend, }; #[test] @@ -112,36 +112,18 @@ mod tests { let loss = layer.forward(random_tensor()); let grads = loss.backward(); let grads = GradientsParams::from_grads(grads, &layer); - let layer = optim.update_module(layer, grads); + let _layer = optim.step(layer, grads); - let state = optim.state(&layer); + let record = optim.to_record(); - assert!(!state.is_empty()); + assert!(!record.is_empty()); } #[test] fn without_updated_params_should_not_have_state() { - let layer = layer(); let optim = sgd_with_all(); - - let state = optim.state(&layer); - - assert!(state.is_empty()); - } - - #[test] - fn without_momentum_and_weights_decay_should_not_have_state() { - let layer = layer(); - let mut optim = sgd_with_nothing(); - let loss = layer.forward(random_tensor()); - let grads = loss.backward(); - let grads = GradientsParams::from_grads(grads, &layer); - - let layer = optim.update_module(layer, grads); - - let state = optim.state(&layer); - - assert!(state.is_empty()); + let record = optim.to_record(); + assert!(record.is_empty()); } #[test] @@ -151,16 +133,16 @@ mod tests { let loss = layer.forward(random_tensor()); let grads = loss.backward(); let grads = GradientsParams::from_grads(grads, &layer); - let layer = optim.update_module(layer, grads); + let _layer = optim.step(layer, grads); - let state = optim.state(&layer); - let mut optim_new = sgd_with_all(); - let state_new = optim_new.state(&layer); - optim_new.load(&layer, &state).unwrap(); - let state_restored = optim_new.state(&layer); + let record = optim.to_record(); + let optim_new = sgd_with_all(); + let record_new = optim_new.to_record(); + let optim_new = optim_new.load_record(record.clone()); + let state_restored = optim_new.to_record(); - assert_ne!(state, state_new); - assert_eq!(state, state_restored); + assert_ne!(record.len(), record_new.len()); + assert_eq!(record.len(), state_restored.len()); } fn random_tensor() -> Tensor { @@ -171,8 +153,8 @@ mod tests { LinearConfig::new(20, 20).with_bias(true).init() } - fn sgd_with_all() -> Sgd { - Sgd::new(&SgdConfig { + fn sgd_with_all() -> OptimizerAdaptor, Linear, TestADBackend> { + SgdConfig { learning_rate: 0.02, weight_decay: Some(WeightDecayConfig { penalty: 0.05 }), momentum: Some(MomentumConfig { @@ -180,14 +162,7 @@ mod tests { dampening: 0.1, nesterov: true, }), - }) - } - - fn sgd_with_nothing() -> Sgd { - Sgd::new(&SgdConfig { - learning_rate: 0.02, - weight_decay: None, - momentum: None, - }) + } + .init() } } diff --git a/burn-core/src/optim/simple/adaptor.rs b/burn-core/src/optim/simple/adaptor.rs new file mode 100644 index 0000000000..bbb06289d7 --- /dev/null +++ b/burn-core/src/optim/simple/adaptor.rs @@ -0,0 +1,105 @@ +use super::{record::AdaptorRecord, SimpleOptimizer}; +use crate::{ + module::{ADModule, ModuleMapper, ParamId}, + optim::{GradientsParams, Optimizer}, +}; +use burn_tensor::{backend::ADBackend, Tensor}; +use core::marker::PhantomData; +use hashbrown::HashMap; + +/// Wrapper struct that adapts any [simple optimizer](SimpleOptimizer) into +/// an [optimizer](Optimizer). +pub struct OptimizerAdaptor +where + O: SimpleOptimizer, + M: ADModule, + B: ADBackend, +{ + optim: O, + records: HashMap>, + module: PhantomData, +} + +impl From for OptimizerAdaptor +where + B: ADBackend, + M: ADModule, + O: SimpleOptimizer, +{ + fn from(optim: O) -> Self { + Self { + optim, + records: HashMap::new(), + module: PhantomData::default(), + } + } +} + +impl Optimizer for OptimizerAdaptor +where + B: ADBackend, + M: ADModule, + O: SimpleOptimizer, +{ + type Record = HashMap>; + + fn step(&mut self, module: M, mut grads: GradientsParams) -> M { + let mut mapper = + SimpleOptimizerMapper::::new(&self.optim, &mut self.records, &mut grads); + module.map(&mut mapper) + } + + fn to_record(&self) -> Self::Record { + self.records.clone() + } + + fn load_record(mut self, record: Self::Record) -> Self { + self.records = record; + self + } +} + +#[derive(new)] +struct SimpleOptimizerMapper<'a, M, B, O> +where + M: ADModule, + B: ADBackend, + O: SimpleOptimizer, +{ + optimizer: &'a O, + records: &'a mut HashMap>, + grads: &'a mut GradientsParams, + phatom: PhantomData, +} + +impl<'a, M, B, O> ModuleMapper for SimpleOptimizerMapper<'a, M, B, O> +where + M: ADModule, + B: ADBackend, + O: SimpleOptimizer, +{ + fn map(&mut self, id: &ParamId, tensor: Tensor) -> Tensor { + let grad = self.grads.remove(id); + + if let Some(grad) = grad { + let device = grad.device(); + let (key, record) = self.records.remove_entry(id).unzip(); + let (tensor, state) = self.optimizer.step( + tensor.inner(), + grad, + record.map(|record| O::to_device(record.into_state(), &device)), + ); + + if let Some(state) = state { + self.records.insert( + key.unwrap_or_else(|| id.clone()), + AdaptorRecord::from_state(state), + ); + } + + return Tensor::from_inner(tensor); + } + + tensor + } +} diff --git a/burn-core/src/optim/simple/base.rs b/burn-core/src/optim/simple/base.rs new file mode 100644 index 0000000000..ca7cdb04d8 --- /dev/null +++ b/burn-core/src/optim/simple/base.rs @@ -0,0 +1,32 @@ +use crate::record::Record; +use burn_tensor::{backend::Backend, Tensor}; + +/// Simple optimizer is an opinionated trait to simplify the process of implementing an +/// optimizer. +/// +/// Implementations don't have to handle missing gradients, loading and exporting records, navigate the +/// module parameter structure, handle tracked and untracked tensors, and the likes. +pub trait SimpleOptimizer: Send + Sync +where + B: Backend, +{ + /// The state of the optimizer. It also implements [record](Record), so that it can be saved. + type State: Record + Clone + 'static; + + /// The optimizer step is performed for one tensor at a time with its gradient and state. + /// + /// Note that the state is passed as parameter, so implementations don't have to handle + /// the saving and loading of recorded states. + fn step( + &self, + tensor: Tensor, + grad: Tensor, + state: Option>, + ) -> (Tensor, Option>); + + /// Change the device of the state. + /// + /// This function will be called accordindly to have the state on the same device as the + /// gradient and the tensor when the [step](SimpleModuleOptimizer::step) function is called. + fn to_device(state: Self::State, device: &B::Device) -> Self::State; +} diff --git a/burn-core/src/optim/simple/mod.rs b/burn-core/src/optim/simple/mod.rs new file mode 100644 index 0000000000..97171ba5a4 --- /dev/null +++ b/burn-core/src/optim/simple/mod.rs @@ -0,0 +1,5 @@ +mod base; +pub use base::*; + +pub mod adaptor; +pub mod record; diff --git a/burn-core/src/optim/simple/record/base.rs b/burn-core/src/optim/simple/record/base.rs new file mode 100644 index 0000000000..55fc0e7641 --- /dev/null +++ b/burn-core/src/optim/simple/record/base.rs @@ -0,0 +1,68 @@ +use super::{AdaptorRecordItemV1, AdaptorRecordV1}; +use crate::{ + optim::SimpleOptimizer, + record::{Record, RecordSettings}, +}; +use burn_tensor::backend::Backend; +use serde::{Deserialize, Serialize}; + +/// [Optimizer adaptor](crate::optim::simple::adaptor::OptimizerAdaptor) record. +/// +/// Records are versioned for backward compatibility, so old records can be loaded. +pub enum AdaptorRecord, B: Backend> { + V1(AdaptorRecordV1), +} + +#[derive(Serialize, Deserialize)] +#[serde(bound = "")] +pub enum AdaptorRecordItem, B: Backend, S: RecordSettings> { + V1(AdaptorRecordItemV1), +} + +impl Record for AdaptorRecord +where + O: SimpleOptimizer, + B: Backend, +{ + type Item = AdaptorRecordItem; + + fn into_item(self) -> Self::Item { + match self { + AdaptorRecord::V1(record) => AdaptorRecordItem::V1(record.into_item()), + } + } + + fn from_item(item: Self::Item) -> Self { + match item { + AdaptorRecordItem::V1(item) => Self::V1(AdaptorRecordV1::from_item(item)), + } + } +} + +impl Clone for AdaptorRecord +where + O: SimpleOptimizer, + B: Backend, +{ + fn clone(&self) -> Self { + match self { + AdaptorRecord::V1(record) => Self::V1(record.clone()), + } + } +} + +impl AdaptorRecord +where + O: SimpleOptimizer, + B: Backend, +{ + pub fn into_state(self) -> O::State { + match self { + AdaptorRecord::V1(record) => record.into_state(), + } + } + + pub fn from_state(state: O::State) -> Self { + Self::V1(AdaptorRecordV1::from_state(state)) + } +} diff --git a/burn-core/src/optim/simple/record/mod.rs b/burn-core/src/optim/simple/record/mod.rs new file mode 100644 index 0000000000..ffe5531727 --- /dev/null +++ b/burn-core/src/optim/simple/record/mod.rs @@ -0,0 +1,5 @@ +mod base; +mod v1; + +pub use base::*; +pub use v1::*; diff --git a/burn-core/src/optim/simple/record/v1.rs b/burn-core/src/optim/simple/record/v1.rs new file mode 100644 index 0000000000..063dc9af32 --- /dev/null +++ b/burn-core/src/optim/simple/record/v1.rs @@ -0,0 +1,134 @@ +use crate::{ + optim::SimpleOptimizer, + record::{Record, RecordSettings}, +}; +use burn_tensor::backend::Backend; +use core::any::Any; +use serde::{Deserialize, Serialize}; + +pub enum AdaptorRecordV1, B: Backend> { + Rank1(O::State<1>), + Rank2(O::State<2>), + Rank3(O::State<3>), + Rank4(O::State<4>), + Rank5(O::State<5>), + Rank6(O::State<6>), + Rank7(O::State<7>), + Rank8(O::State<8>), +} + +impl, B: Backend> Clone for AdaptorRecordV1 { + fn clone(&self) -> Self { + match self { + AdaptorRecordV1::Rank1(record) => AdaptorRecordV1::Rank1(record.clone()), + AdaptorRecordV1::Rank2(record) => AdaptorRecordV1::Rank2(record.clone()), + AdaptorRecordV1::Rank3(record) => AdaptorRecordV1::Rank3(record.clone()), + AdaptorRecordV1::Rank4(record) => AdaptorRecordV1::Rank4(record.clone()), + AdaptorRecordV1::Rank5(record) => AdaptorRecordV1::Rank5(record.clone()), + AdaptorRecordV1::Rank6(record) => AdaptorRecordV1::Rank6(record.clone()), + AdaptorRecordV1::Rank7(record) => AdaptorRecordV1::Rank7(record.clone()), + AdaptorRecordV1::Rank8(record) => AdaptorRecordV1::Rank8(record.clone()), + } + } +} + +#[derive(Serialize, Deserialize)] +#[serde(bound = "")] +pub enum AdaptorRecordItemV1, B: Backend, S: RecordSettings> { + Rank1( as Record>::Item), + Rank2( as Record>::Item), + Rank3( as Record>::Item), + Rank4( as Record>::Item), + Rank5( as Record>::Item), + Rank6( as Record>::Item), + Rank7( as Record>::Item), + Rank8( as Record>::Item), +} + +impl AdaptorRecordV1 +where + O: SimpleOptimizer, + B: Backend, +{ + pub fn into_state(self) -> O::State { + let boxed_state: Box = match self { + AdaptorRecordV1::Rank1(s) => Box::new(s), + AdaptorRecordV1::Rank2(s) => Box::new(s), + AdaptorRecordV1::Rank3(s) => Box::new(s), + AdaptorRecordV1::Rank4(s) => Box::new(s), + AdaptorRecordV1::Rank5(s) => Box::new(s), + AdaptorRecordV1::Rank6(s) => Box::new(s), + AdaptorRecordV1::Rank7(s) => Box::new(s), + AdaptorRecordV1::Rank8(s) => Box::new(s), + }; + let state = boxed_state + .downcast::>() + .expect("Unsupported state dimension, dimension up to 8 are supported."); + *state + } + pub fn from_state(state: O::State) -> Self { + let state: Box = Box::new(state); + + match D { + 1 => AdaptorRecordV1::Rank1(*state.downcast().unwrap()), + 2 => AdaptorRecordV1::Rank2(*state.downcast().unwrap()), + 3 => AdaptorRecordV1::Rank3(*state.downcast().unwrap()), + 4 => AdaptorRecordV1::Rank4(*state.downcast().unwrap()), + 5 => AdaptorRecordV1::Rank5(*state.downcast().unwrap()), + 6 => AdaptorRecordV1::Rank6(*state.downcast().unwrap()), + 7 => AdaptorRecordV1::Rank7(*state.downcast().unwrap()), + 8 => AdaptorRecordV1::Rank8(*state.downcast().unwrap()), + _ => panic!("Unsupported state dimension, dimension up to 8 are supported."), + } + } +} + +impl Record for AdaptorRecordV1 +where + O: SimpleOptimizer, + B: Backend, +{ + type Item = AdaptorRecordItemV1; + + fn into_item(self) -> Self::Item { + match self { + AdaptorRecordV1::Rank1(record) => AdaptorRecordItemV1::Rank1(record.into_item()), + AdaptorRecordV1::Rank2(record) => AdaptorRecordItemV1::Rank2(record.into_item()), + AdaptorRecordV1::Rank3(record) => AdaptorRecordItemV1::Rank3(record.into_item()), + AdaptorRecordV1::Rank4(record) => AdaptorRecordItemV1::Rank4(record.into_item()), + AdaptorRecordV1::Rank5(record) => AdaptorRecordItemV1::Rank5(record.into_item()), + AdaptorRecordV1::Rank6(record) => AdaptorRecordItemV1::Rank6(record.into_item()), + AdaptorRecordV1::Rank7(record) => AdaptorRecordItemV1::Rank7(record.into_item()), + AdaptorRecordV1::Rank8(record) => AdaptorRecordItemV1::Rank8(record.into_item()), + } + } + + fn from_item(item: Self::Item) -> Self { + match item { + AdaptorRecordItemV1::Rank1(item) => { + AdaptorRecordV1::Rank1( as Record>::from_item(item)) + } + AdaptorRecordItemV1::Rank2(item) => { + AdaptorRecordV1::Rank2( as Record>::from_item(item)) + } + AdaptorRecordItemV1::Rank3(item) => { + AdaptorRecordV1::Rank3( as Record>::from_item(item)) + } + AdaptorRecordItemV1::Rank4(item) => { + AdaptorRecordV1::Rank4( as Record>::from_item(item)) + } + AdaptorRecordItemV1::Rank5(item) => { + AdaptorRecordV1::Rank5( as Record>::from_item(item)) + } + AdaptorRecordItemV1::Rank6(item) => { + AdaptorRecordV1::Rank6( as Record>::from_item(item)) + } + AdaptorRecordItemV1::Rank7(item) => { + AdaptorRecordV1::Rank7( as Record>::from_item(item)) + } + AdaptorRecordItemV1::Rank8(item) => { + AdaptorRecordV1::Rank8( as Record>::from_item(item)) + } + } + } +} diff --git a/burn-core/src/optim/visitor.rs b/burn-core/src/optim/visitor.rs index 0fcf7fb3b4..b63028cb28 100644 --- a/burn-core/src/optim/visitor.rs +++ b/burn-core/src/optim/visitor.rs @@ -1,22 +1,7 @@ -use core::marker::PhantomData; - -use super::{GradientsParams, Optimizer}; -use crate::module::{ADModule, ModuleVisitor, ParamId, StateNamed}; +use super::GradientsParams; +use crate::module::{ADModule, ModuleVisitor, ParamId}; use burn_tensor::{backend::ADBackend, Tensor}; - -#[derive(new)] -pub struct GradientsRegister<'a, M: ADModule, B: ADBackend, O> { - optimizer: &'a O, - state: &'a mut StateNamed, - phatom: PhantomData, -} - -#[derive(new)] -pub struct GradientsLoader<'a, M: ADModule, B: ADBackend, O> { - optimizer: &'a mut O, - state: &'a StateNamed, - phatom: PhantomData, -} +use core::marker::PhantomData; #[derive(new)] pub struct GradientsParamsConverter<'a, M: ADModule, B: ADBackend> { @@ -32,29 +17,6 @@ pub struct GradientsParamsChangeDevice<'a, M: ADModule, B: ADBackend> { phatom: PhantomData, } -impl<'a, B, M, O> ModuleVisitor for GradientsRegister<'a, M, B, O> -where - B: ADBackend, - M: ADModule, - O: Optimizer, -{ - fn visit(&mut self, id: &ParamId, _tensor: &Tensor) { - self.optimizer.register_param_state::(id, self.state) - } -} - -impl<'a, B, M, O> ModuleVisitor for GradientsLoader<'a, M, B, O> -where - B: ADBackend, - M: ADModule, - O: Optimizer, -{ - fn visit(&mut self, id: &ParamId, tensor: &Tensor) { - self.optimizer - .load_param_state::(id, self.state, &tensor.device()) - } -} - impl<'a, B, M> ModuleVisitor for GradientsParamsConverter<'a, M, B> where B: ADBackend, diff --git a/burn-core/src/record/base.rs b/burn-core/src/record/base.rs index bfe8487a49..3c8593a1ba 100644 --- a/burn-core/src/record/base.rs +++ b/burn-core/src/record/base.rs @@ -8,7 +8,7 @@ use serde::{de::DeserializeOwned, Deserialize, Serialize}; /// Trait to define a family of types which can be recorded using any [settings](RecordSettings). pub trait Record: Send + Sync { - type Item; + type Item: Serialize + DeserializeOwned; /// Convert the current record into the corresponding item that follows the given [settings](RecordSettings). fn into_item(self) -> Self::Item; @@ -20,7 +20,6 @@ pub trait Record: Send + Sync { where Self: Sized, S: RecordSettings, - Self::Item: Serialize + DeserializeOwned, { let metadata = BurnMetadata::new( core::any::type_name::().to_string(), @@ -40,7 +39,6 @@ pub trait Record: Send + Sync { where Self: Sized, S: RecordSettings, - Self::Item: Serialize + DeserializeOwned, { let record: BurnRecord> = RecorderType::::load(args.clone()).map_err(|err| { diff --git a/burn-core/src/record/file.rs b/burn-core/src/record/file.rs index 87274c1e91..197fa43534 100644 --- a/burn-core/src/record/file.rs +++ b/burn-core/src/record/file.rs @@ -7,6 +7,7 @@ use std::{fs::File, path::PathBuf}; pub trait FileRecorder: Recorder { + fn file_extension() -> &'static str; } /// File recorder using the [bincode format](bincode). @@ -26,13 +27,33 @@ pub struct FilePrettyJsonRecorder; /// File recorder using the [message pack](rmp_serde) format compressed with gzip. pub struct FileMpkGzRecorder; -impl FileRecorder for FileBinGzRecorder {} -impl FileRecorder for FileBinRecorder {} -impl FileRecorder for FileJsonGzRecorder {} -impl FileRecorder for FilePrettyJsonRecorder {} +impl FileRecorder for FileBinGzRecorder { + fn file_extension() -> &'static str { + "bin.gz" + } +} +impl FileRecorder for FileBinRecorder { + fn file_extension() -> &'static str { + "bin" + } +} +impl FileRecorder for FileJsonGzRecorder { + fn file_extension() -> &'static str { + "json.gz" + } +} +impl FileRecorder for FilePrettyJsonRecorder { + fn file_extension() -> &'static str { + "json" + } +} #[cfg(feature = "msgpack")] -impl FileRecorder for FileMpkGzRecorder {} +impl FileRecorder for FileMpkGzRecorder { + fn file_extension() -> &'static str { + "mpk.gz" + } +} macro_rules! str2reader { ( @@ -79,7 +100,7 @@ impl Recorder for FileBinGzRecorder { mut file: PathBuf, ) -> Result<(), RecorderError> { let config = bin_config(); - let writer = str2writer!(file, "bin.gz")?; + let writer = str2writer!(file, Self::file_extension())?; let mut writer = GzEncoder::new(writer, Compression::default()); bincode::serde::encode_into_std_write(&obj, &mut writer, config) @@ -89,7 +110,7 @@ impl Recorder for FileBinGzRecorder { } fn load(mut file: PathBuf) -> Result { - let reader = str2reader!(file, "bin.gz")?; + let reader = str2reader!(file, Self::file_extension())?; let mut reader = GzDecoder::new(reader); let state = bincode::serde::decode_from_std_read(&mut reader, bin_config()) .map_err(|err| RecorderError::Unknown(err.to_string()))?; @@ -131,7 +152,7 @@ impl Recorder for FileJsonGzRecorder { obj: Obj, mut file: PathBuf, ) -> Result<(), RecorderError> { - let writer = str2writer!(file, "json.gz")?; + let writer = str2writer!(file, Self::file_extension())?; let writer = GzEncoder::new(writer, Compression::default()); serde_json::to_writer(writer, &obj) .map_err(|err| RecorderError::Unknown(err.to_string()))?; @@ -140,7 +161,7 @@ impl Recorder for FileJsonGzRecorder { } fn load(mut file: PathBuf) -> Result { - let reader = str2reader!(file, "json.gz")?; + let reader = str2reader!(file, Self::file_extension())?; let reader = GzDecoder::new(reader); let state = serde_json::from_reader(reader) .map_err(|err| RecorderError::Unknown(err.to_string()))?; @@ -158,14 +179,14 @@ impl Recorder for FilePrettyJsonRecorder { obj: Obj, mut file: PathBuf, ) -> Result<(), RecorderError> { - let writer = str2writer!(file, "json")?; + let writer = str2writer!(file, Self::file_extension())?; serde_json::to_writer_pretty(writer, &obj) .map_err(|err| RecorderError::Unknown(err.to_string()))?; Ok(()) } fn load(mut file: PathBuf) -> Result { - let reader = str2reader!(file, "json")?; + let reader = str2reader!(file, Self::file_extension())?; let state = serde_json::from_reader(reader) .map_err(|err| RecorderError::Unknown(err.to_string()))?; @@ -183,7 +204,7 @@ impl Recorder for FileMpkGzRecorder { obj: Obj, mut file: PathBuf, ) -> Result<(), RecorderError> { - let writer = str2writer!(file, "mpk.gz")?; + let writer = str2writer!(file, Self::file_extension())?; let mut writer = GzEncoder::new(writer, Compression::default()); rmp_serde::encode::write(&mut writer, &obj) .map_err(|err| RecorderError::Unknown(err.to_string()))?; @@ -192,7 +213,7 @@ impl Recorder for FileMpkGzRecorder { } fn load(mut file: PathBuf) -> Result { - let reader = str2reader!(file, "mpk.gz")?; + let reader = str2reader!(file, Self::file_extension())?; let reader = GzDecoder::new(reader); let state = rmp_serde::decode::from_read(reader) .map_err(|err| RecorderError::Unknown(err.to_string()))?; diff --git a/burn-core/src/record/primitive.rs b/burn-core/src/record/primitive.rs index 11e120e36c..d3ac5c78ff 100644 --- a/burn-core/src/record/primitive.rs +++ b/burn-core/src/record/primitive.rs @@ -1,7 +1,11 @@ -use super::{Record, RecordSettings}; -use crate::module::{Param, State}; +use alloc::string::String; +use alloc::string::ToString; use alloc::vec::Vec; + +use super::{Record, RecordSettings}; +use crate::module::{Param, ParamId}; use burn_tensor::{DataSerialize, Element}; +use hashbrown::HashMap; impl Record for () { type Item = (); @@ -35,27 +39,39 @@ impl Record for Option { } } -impl Record for [T; N] { - type Item = [T::Item; N]; +impl Record for [T; N] { + type Item = Vec>; fn into_item(self) -> Self::Item { - self.map(Record::into_item) + self.map(Record::into_item).into_iter().collect() } fn from_item(item: Self::Item) -> Self { - item.map(Record::from_item) + item.into_iter() + .map(Record::from_item) + .collect::>() + .try_into() + .unwrap_or_else(|_| panic!("An arrar of size {N}")) } } -impl Record for State { - type Item = State; +impl Record for HashMap { + type Item = HashMap>; fn into_item(self) -> Self::Item { - self.convert::() + let mut items = HashMap::with_capacity(self.len()); + self.into_iter().for_each(|(id, record)| { + items.insert(id.to_string(), record.into_item()); + }); + items } fn from_item(item: Self::Item) -> Self { - item.convert() + let mut record = HashMap::with_capacity(item.len()); + item.into_iter().for_each(|(id, item)| { + record.insert(ParamId::from(id), T::from_item(item)); + }); + record } } @@ -113,7 +129,11 @@ primitive!(bool); // Float Types primitive!(f64); primitive!(f32); + +// TODO: Remove the feature flag when half supports serde with no_std +#[cfg(feature = "std")] primitive!(half::bf16); +#[cfg(feature = "std")] primitive!(half::f16); // Unsigned Integer Types diff --git a/burn-train/src/checkpoint/base.rs b/burn-train/src/checkpoint/base.rs index dbad02f5ed..985b7ef71a 100644 --- a/burn-train/src/checkpoint/base.rs +++ b/burn-train/src/checkpoint/base.rs @@ -1,13 +1,9 @@ -use burn_core::{ - module::StateError, - record::{Record, RecorderError}, -}; +use burn_core::record::{Record, RecorderError}; #[derive(Debug)] pub enum CheckpointerError { IOError(std::io::Error), RecorderError(RecorderError), - StateError(StateError), Unknown(String), } diff --git a/burn-train/src/checkpoint/file.rs b/burn-train/src/checkpoint/file.rs index bb9cd00197..70b7c657a4 100644 --- a/burn-train/src/checkpoint/file.rs +++ b/burn-train/src/checkpoint/file.rs @@ -47,18 +47,22 @@ where log::info!("Saving checkpoint {} to {}", epoch, file_path); record - .record(file_path.into()) + .record::(file_path.into()) .map_err(CheckpointerError::RecorderError)?; if self.num_keep > epoch { return Ok(()); } - let file_path_old_checkpoint = self.path_for_epoch(epoch - self.num_keep); + let file_to_remove = format!( + "{}.{}", + self.path_for_epoch(epoch - self.num_keep), + ::file_extension() + ); - if std::path::Path::new(&file_path_old_checkpoint).exists() { - log::info!("Removing checkpoint {}", file_path_old_checkpoint); - std::fs::remove_file(file_path_old_checkpoint).map_err(CheckpointerError::IOError)?; + if std::path::Path::new(&file_to_remove).exists() { + log::info!("Removing checkpoint {}", file_to_remove); + std::fs::remove_file(file_to_remove).map_err(CheckpointerError::IOError)?; } Ok(()) @@ -67,7 +71,7 @@ where fn restore(&self, epoch: usize) -> Result { let file_path = self.path_for_epoch(epoch); log::info!("Restoring checkpoint {} from {}", epoch, file_path); - let record = R::load(file_path.into()).map_err(CheckpointerError::RecorderError)?; + let record = R::load::(file_path.into()).map_err(CheckpointerError::RecorderError)?; Ok(record) } diff --git a/burn-train/src/learner/base.rs b/burn-train/src/learner/base.rs index bab724ec1a..80c26ca753 100644 --- a/burn-train/src/learner/base.rs +++ b/burn-train/src/learner/base.rs @@ -1,8 +1,8 @@ use crate::checkpoint::Checkpointer; use crate::LearnerCallback; -use burn_core::module::{ADModule, Module, State}; +use burn_core::module::{ADModule, Module}; use burn_core::optim::Optimizer; -use burn_core::tensor::backend::{ADBackend, Backend}; +use burn_core::tensor::backend::ADBackend; /// Learner struct encapsulating all components necessary to train a Neural Network model. /// @@ -19,13 +19,13 @@ where pub(super) callback: Box>, pub(super) checkpoint: Option, pub(super) checkpointer_model: CheckpointModel, - pub(super) checkpointer_optimizer: CheckpointOptim, + pub(super) checkpointer_optimizer: CheckpointOptim, pub(super) grad_accumulation: Option, pub(super) devices: Vec, } type CheckpointModel = Option>::Record>>>; -type CheckpointOptim = Option::FloatElem>>>>; +type CheckpointOptim = Option>::Record>>>; impl Learner where @@ -39,7 +39,7 @@ where model: &M, optim: &O, checkpointer_model: &CheckpointModel, - checkpointer_optimizer: &CheckpointOptim, + checkpointer_optimizer: &CheckpointOptim, epoch: usize, ) { if let Some(checkpointer) = &checkpointer_model { @@ -48,7 +48,7 @@ where .unwrap(); } if let Some(checkpointer) = &checkpointer_optimizer { - checkpointer.save(epoch, optim.state(model)).unwrap(); + checkpointer.save(epoch, optim.to_record()).unwrap(); } } @@ -59,8 +59,8 @@ where } if let Some(checkpointer) = &self.checkpointer_optimizer { - let state = checkpointer.restore(epoch).unwrap(); - self.optim.load(&self.model, &state).unwrap(); + let record = checkpointer.restore(epoch).unwrap(); + self.optim = self.optim.load_record(record); } self diff --git a/burn-train/src/learner/builder.rs b/burn-train/src/learner/builder.rs index 1d405916cf..037b214a97 100644 --- a/burn-train/src/learner/builder.rs +++ b/burn-train/src/learner/builder.rs @@ -6,7 +6,7 @@ use crate::metric::dashboard::cli::CLIDashboardRenderer; use crate::metric::dashboard::Dashboard; use crate::metric::{Adaptor, Metric, Numeric}; use crate::AsyncTrainerCallback; -use burn_core::module::{ADModule, State}; +use burn_core::module::ADModule; use burn_core::optim::Optimizer; use burn_core::record::{FileRecorder, Record, RecordSettings}; use burn_core::tensor::backend::ADBackend; @@ -15,16 +15,17 @@ use serde::Serialize; use std::sync::Arc; /// Struct to configure and create a [learner](Learner). -pub struct LearnerBuilder +pub struct LearnerBuilder where T: Send + Sync + 'static, V: Send + Sync + 'static, B: ADBackend, M: ADModule, + O: Optimizer, { dashboard: Dashboard, checkpointer_model: Option + Send + Sync>>, - checkpointer_optimizer: Option> + Send + Sync>>, + checkpointer_optimizer: Option + Send + Sync>>, num_epochs: usize, checkpoint: Option, directory: String, @@ -32,12 +33,13 @@ where devices: Vec, } -impl LearnerBuilder +impl LearnerBuilder where T: Send + Sync + 'static, V: Send + Sync + 'static, B: ADBackend, Model: ADModule, + O: Optimizer, { pub fn new(directory: &str) -> Self { let renderer = Box::new(CLIDashboardRenderer::new()); @@ -148,6 +150,7 @@ where where S: RecordSettings + 'static, ::Item: Serialize + DeserializeOwned, + ::Item: Serialize + DeserializeOwned, S::Recorder: FileRecorder, { self.checkpointer_model = Some(Arc::new(FileCheckpointer::::new( @@ -164,18 +167,18 @@ where } /// Create the [learner](Learner) from a [module](ADModule) and an - pub fn build(self, model: Model, optim: O) -> Learner + pub fn build(self, model: Model, optim: O) -> Learner where Model::Record: 'static, - O: Optimizer, + O::Record: 'static, { self.init_logger(); let callack = Box::new(self.dashboard); let callback = Box::new(AsyncTrainerCallback::new(callack)); - let create_checkpointer = |checkpointer| match checkpointer { + let create_checkpointer_optim = |checkpointer| match checkpointer { Some(checkpointer) => { - let checkpointer: Box>> = + let checkpointer: Box> = Box::new(AsyncCheckpointer::new(checkpointer)); Some(checkpointer) } @@ -198,7 +201,7 @@ where callback, checkpoint: self.checkpoint, checkpointer_model: create_checkpointer_model(self.checkpointer_model), - checkpointer_optimizer: create_checkpointer(self.checkpointer_optimizer), + checkpointer_optimizer: create_checkpointer_optim(self.checkpointer_optimizer), grad_accumulation: self.grad_accumulation, devices: self.devices, } diff --git a/burn-train/src/learner/epoch.rs b/burn-train/src/learner/epoch.rs index 509af759f5..b3fb06a8a8 100644 --- a/burn-train/src/learner/epoch.rs +++ b/burn-train/src/learner/epoch.rs @@ -88,11 +88,11 @@ impl TrainEpoch { if accumulation <= accumulation_current { let grads = accumulator.grads(); - model = optim.update_module(model, grads); + model = optim.step(model, grads); accumulation_current = 0; } } - None => model = optim.update_module(model, item.grads), + None => model = optim.step(model, item.grads), } callback.on_train_item(LearnerItem::new( @@ -160,7 +160,7 @@ impl TrainEpoch { if accumulation <= accumulation_current { let grads = accumulator.grads(); - model = optim.update_module(model, grads); + model = optim.step(model, grads); accumulation_current = 0; } diff --git a/examples/mnist-inference-web/model.bin b/examples/mnist-inference-web/model.bin index 3eba1a629d41d153e23be30770c6749104f0668c..8ce947d5693b2e1741502225ec5b77a57fd97c05 100644 GIT binary patch literal 1508725 zcmX7vd00(f)W;i%N`qub5-CNR$FtYXP@)KxL`gFx36T=b^E^>$))~zre?Ph%7F1uQyGmWg;-HnUtMy?g4{s0ez5jpD%FWl8dvQcT 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zmnArC5kC!#lutLAD_^ENT>QmPUAE-4s=RG&h`j$LJ-O1x0|f5+vLnjD;@RsA6lbmoBAv&MlF}dQvRd~P(w?gsv%JOi z^ih%bd0QgWGt8g|uk+&v+4_-rQ+OtO}X57tmux}D^VSFrro;%Q=~ftm{c E16!i3761SM diff --git a/examples/mnist/src/training.rs b/examples/mnist/src/training.rs index ac09f37a6c..95bfceed16 100644 --- a/examples/mnist/src/training.rs +++ b/examples/mnist/src/training.rs @@ -5,7 +5,7 @@ use crate::model::Model; use burn::module::Module; use burn::optim::decay::WeightDecayConfig; -use burn::optim::{Adam, AdamConfig}; +use burn::optim::AdamConfig; use burn::record::{DefaultRecordSettings, NoStdTrainingRecordSettings, Record}; use burn::{ config::Config, @@ -58,7 +58,7 @@ pub fn run(device: B::Device) { .build(Arc::new(MNISTDataset::test())); // Model - let optim = Adam::new(&config.optimizer); + let optim = config.optimizer.init(); let model = Model::new(); let learner = LearnerBuilder::new(ARTIFACT_DIR) @@ -66,7 +66,7 @@ pub fn run(device: B::Device) { .metric_valid_plot(AccuracyMetric::new()) .metric_train_plot(LossMetric::new()) .metric_valid_plot(LossMetric::new()) - .with_file_checkpointer::(2) + .with_file_checkpointer::(1) .devices(vec![device]) .num_epochs(config.num_epochs) .build(model, optim); diff --git a/examples/text-classification/src/training.rs b/examples/text-classification/src/training.rs index 3c2c920729..6c4630a279 100644 --- a/examples/text-classification/src/training.rs +++ b/examples/text-classification/src/training.rs @@ -7,7 +7,7 @@ use burn::{ data::{dataloader::DataLoaderBuilder, dataset::transform::SamplerDataset}, module::Module, nn::transformer::TransformerEncoderConfig, - optim::{Sgd, SgdConfig}, + optim::SgdConfig, record::{DefaultRecordSettings, Record}, tensor::backend::ADBackend, train::{ @@ -70,7 +70,7 @@ pub fn train( .num_workers(4) .build(dataset_test); - let optim = Sgd::new(&config.optimizer); + let optim = config.optimizer.init(); let learner = LearnerBuilder::new(artifact_dir) .metric_train(CUDAMetric::new()) diff --git a/examples/text-generation/src/training.rs b/examples/text-generation/src/training.rs index 193bedf30c..93bb2c74de 100644 --- a/examples/text-generation/src/training.rs +++ b/examples/text-generation/src/training.rs @@ -7,7 +7,7 @@ use burn::{ data::{dataloader::DataLoaderBuilder, dataset::Dataset}, module::Module, nn::transformer::TransformerEncoderConfig, - optim::{Adam, AdamConfig}, + optim::AdamConfig, tensor::backend::ADBackend, train::{ metric::{AccuracyMetric, CUDAMetric, LossMetric}, @@ -70,7 +70,7 @@ pub fn train + 'static>( .num_workers(4) .build(dataset_test); - let optim = Adam::new(&config.optimizer); + let optim = config.optimizer.init(); let learner = LearnerBuilder::new(artifact_dir) .metric_train(CUDAMetric::new())

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