Struct RunningState 

pub struct RunningState<V> { /* private fields */ }

A state that can be updated during the forward pass while being thread safe.

Note

The state value is the average of all updates on all threads.

Implementations

impl<const D: usize> RunningState<Tensor<D>>

pub fn new(value: Tensor<D>) -> RunningState<Tensor<D>>

Create a new running state.

pub fn with_id(id: ParamId, value: Tensor<D>) -> RunningState<Tensor<D>>

Create a new running state.

pub fn from_record(record: Param<Tensor<D>>) -> RunningState<Tensor<D>>

Create a new running state from a record.

pub fn update(&self, value: Tensor<D>)

Update the value on the current thread.

pub fn value(&self) -> Tensor<D>

Get the current value,

Note

The current value might be outdated by one update.

pub fn value_sync(&self) -> Tensor<D>

Get the current value and make sure it is sync.

Note

Don’t use this function after an update on the same thread where other threads might have to register their update before the actual synchronization needs to happen.

Trait Implementations

impl<V> Clone for RunningState<V>

where V: Clone,

fn clone(&self) -> RunningState<V>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<V> Debug for RunningState<V>

where V: Debug,

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<V> Display for RunningState<V>

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<const D: usize> Module for RunningState<Tensor<D>>

fn visit<V>(&self, visitor: &mut V)

where V: ModuleVisitor,

Visit each parameter and module-owned control value with a visitor.

fn map<M>(self, mapper: &mut M) -> RunningState<Tensor<D>>

where M: ModuleMapper,

Map each parameter and module-owned control value with a mapper.

fn to_device(self, device: &Device) -> RunningState<Tensor<D>>

Move the module and all of its sub-modules to the given device.

fn fork(self, device: &Device) -> RunningState<Tensor<D>>

Fork the module and all of its sub-modules to the given device.

fn collect_devices(&self, devices: Vec<Device>) -> Vec<Device>

Return all the devices found in the underneath module tree added to the given vector without duplicates.

fn materialize(self) -> RunningState<Tensor<D>>

Fold parameter reparameterizations into their effective values and remove them.

fn valid(&self) -> RunningState<Tensor<D>>

Returns a validation snapshot without autodiff or active training flags.

fn train(self) -> RunningState<Tensor<D>>

Enables autodiff and restores configured training state throughout the module.

fn devices(&self) -> Vec<Device>

Return all the devices found in the underneath module tree without duplicates.

fn set_require_grad(self, require_grad: bool) -> Self

Set whether every floating-point tensor parameter in the module tree requires gradients.

fn set_require_grad_group(self, group: ParamGroup, require_grad: bool) -> Self

Set whether matched floating-point tensor parameters require gradients.

fn no_grad(self) -> Self

Disable gradients for every floating-point tensor parameter in the module tree.

fn freeze(self) -> Self

Freeze the whole module tree.

fn unfreeze(self) -> Self

Unfreeze the whole module tree.

fn freeze_group(self, group: ParamGroup) -> Self

Freeze every module-owned value in the given group, leaving the rest of the module untouched.

fn unfreeze_group(self, group: ParamGroup) -> Self

Unfreeze every module-owned value in the given group, leaving the rest of the module untouched.

fn num_params(&self) -> usize

Get the number of parameters the module has, including all of its sub-modules, without initializing the ones not initialized yet.

fn quantize_weights(self, quantizer: &mut Quantizer) -> Self

Quantize the weights of the module.

fn quantize_weights_group( self, quantizer: &mut Quantizer, group: ParamGroup, ) -> Self

Quantize the weights of the given parameter group.

fn apply_reparameterization<R>(self, reparameterizer: R) -> Self

where Self: Sized, R: Reparameterizer,

Attach reparameterizations using the given Reparameterizer.

fn apply_lora(self, lora: Lora) -> Self

where Self: Sized,

Attach LoRA adapters to the module’s 2-D weights, freezing all base tensor parameters.

fn apply_qlora(self, qlora: QLora) -> Self

where Self: Sized,

Apply QLoRA to the module: quantize the (frozen) base tensor parameters and attach trainable LoRA adapters to 2-D weights.

fn into_record(self) -> ModuleRecord

where Self: Sized,

Collect this module’s tensor parameters into a ModuleRecord.

fn into_record_group(self, group: ParamGroup) -> ModuleRecord

where Self: Sized,

Collect the tensor parameters group names into a ModuleRecord.

fn try_load_record(self, record: ModuleRecord) -> Result<Self, RecordError>

where Self: Sized,

Apply a ModuleRecord to this module, returning the loaded module.

fn load_record(self, record: ModuleRecord) -> Self

where Self: Sized,

Apply a ModuleRecord to this module, consuming and returning it.

fn save_file<P>(self, path: P) -> Result<(), RecordError>

where P: AsRef<Path>, Self: Sized,

Save this module’s parameters to a burnpack file on disk.

fn load_file<P>(self, path: P) -> Self

where P: AsRef<Path>, Self: Sized,

Load this module’s parameters from a burnpack file on disk, returning the loaded module.

fn try_load_file<P>(self, path: P) -> Result<Self, RecordError>

where P: AsRef<Path>, Self: Sized,

Fallible variant of load_file.

impl<V> ModuleDisplay for RunningState<V>

fn format(&self, passed_settings: DisplaySettings) -> String

Formats the module with provided display settings.

fn custom_settings(&self) -> Option<DisplaySettings>

Custom display settings for the module.

fn custom_content(&self, _content: Content) -> Option<Content>

Custom attributes for the module.

impl<V> ModuleDisplayDefault for RunningState<V>

fn content(&self, content: Content) -> Option<Content>

Attributes of the module used for display purposes.

fn num_params(&self) -> usize

Gets the number of the parameters of the module.