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Mila::Dnn::Loss< TDeviceType, TPrecision > Class Template Referenceexport
module Dnn.Loss
Abstract base class for neural network loss functions. More...
Inheritance diagram for Mila::Dnn::Loss< TDeviceType, TPrecision >:
Collaboration diagram for Mila::Dnn::Loss< TDeviceType, TPrecision >:
Public Types | |
| using | ExecutionContextType = ExecutionContext<TDeviceType> |
| using | MR = typename DeviceTypeTraits<TDeviceType>::memory_resource |
| using | TensorType = Tensor<TPrecision, MR> |
Public Member Functions | |
| virtual | ~Loss ()=default |
| Public Member Functions inherited from Mila::Dnn::Component< TDeviceType, TPrecision > | |
| Component (const std::string &name) | |
| Construct component with required name identifier. | |
| virtual | ~Component ()=default |
| virtual void | build (const BuildContext &context) final |
| Build the component with the provided BuildContext (canonical overload). | |
| virtual DeviceId | getDeviceId () const =0 |
| Get the compute device id associated with this component. | |
| virtual std::vector< ITensor * > | getGradients () const =0 |
| Return non-owning pointers to parameter gradient tensors. | |
| virtual MemoryStats | getMemoryStats () const =0 |
| Return the current memory allocation breakdown for this component. | |
| const std::string | getName () const |
| Get the component's name identifier. | |
| virtual std::vector< std::string > | getParameterNames () const |
| List all available parameter names for this component. | |
| virtual std::vector< ITensor * > | getParameters () const =0 |
| Return non-owning pointers to parameter tensors. | |
| RuntimeMode | getRuntimeMode () const noexcept |
| Convenience accessor — true if currently in Eval mode. | |
| TrainingMode | getTrainingMode () const noexcept |
| The current runtime behavioral mode of this Component. | |
| virtual const ComponentType | getType () const =0 |
| Get the component type identifier. | |
| virtual bool | isBuilt () const final |
| Returns true if build() has completed successfully. | |
| bool | isInferenceMode () const noexcept |
| bool | isTrainingMode () const noexcept |
| virtual void | loadParameter (const std::string &name, const Serialization::ITensorBlob &blob) |
| Load a parameter from serialized tensor data. | |
| virtual size_t | parameterCount () const =0 |
| Return number of trainable parameters. | |
| virtual void | save_ (ModelArchive &archive, SerializationMode mode) const =0 |
| void | setTrainingMode (TrainingMode mode) |
| Set the runtime behavioral mode for this Component. | |
| virtual void | synchronize ()=0 |
| Wait for outstanding device work submitted by this component. | |
| virtual std::string | toString () const =0 |
| Produce a short, human-readable description of the component. | |
| virtual void | zeroGradients () |
| Clear all model-owned gradients for this component. | |
Additional Inherited Members | |
| Static Public Member Functions inherited from Mila::Dnn::Component< TDeviceType, TPrecision > | |
| static constexpr DeviceType | getDeviceType () |
| Compile-time device type for this component instance. | |
| static constexpr TensorDataType | getPrecision () noexcept |
| Compile-time tensor precision for this component instance. | |
| Protected Member Functions inherited from Mila::Dnn::Component< TDeviceType, TPrecision > | |
| IExecutionContext * | getExecutionContext () const |
| Get the shared execution context. | |
| bool | hasExecutionContext () const noexcept |
| Check if execution context has been set. | |
| template<TensorDataType TParameterPrecision, typename TMemoryResource> | |
| void | loadParameterFromBlob (const std::string ¶m_name, const Serialization::ITensorBlob &blob, Tensor< TParameterPrecision, TMemoryResource > &target, const shape_t &expected_shape) |
| Load a tensor blob into a parameter tensor with validation. | |
| virtual void | onBuilding (const BuildContext &config) |
| Hook invoked by build() to allocate component buffers. | |
| virtual void | onExecutionContextSet () |
| Lifecycle hook: Called immediately after ExecutionContext is set. | |
| virtual void | onTrainingModeChanging (TrainingMode mode) |
| Hook called before TrainingMode transitions. | |
| void | setExecutionContext (IExecutionContext *context) |
| Set the execution context for this component. | |
| Protected Attributes inherited from Mila::Dnn::Component< TDeviceType, TPrecision > | |
| BuildContext | build_context_ { shape_t{ 1 }, RuntimeMode::Training } |
| The BuildContext stored at build time. | |
Detailed Description
template<Compute::DeviceType TDeviceType, TensorDataType TPrecision>
requires PrecisionSupportedOnDevice<TPrecision, TDeviceType>
class Mila::Dnn::Loss< TDeviceType, TPrecision >
requires PrecisionSupportedOnDevice<TPrecision, TDeviceType>
class Mila::Dnn::Loss< TDeviceType, TPrecision >
Abstract base class for neural network loss functions.
- Template Parameters
-
TDeviceType Compile-time device identifier for this loss. TPrecision Data type used for computations.
Loss functions compute a scalar loss value given model predictions and target values. They may also provide hooks for optimizing the network graph and configuring reduction modes.
Member Typedef Documentation
◆ ExecutionContextType
template<Compute::DeviceType TDeviceType, TensorDataType TPrecision>
| using Mila::Dnn::Loss< TDeviceType, TPrecision >::ExecutionContextType = ExecutionContext<TDeviceType> |
◆ MR
template<Compute::DeviceType TDeviceType, TensorDataType TPrecision>
| using Mila::Dnn::Loss< TDeviceType, TPrecision >::MR = typename DeviceTypeTraits<TDeviceType>::memory_resource |
◆ TensorType
template<Compute::DeviceType TDeviceType, TensorDataType TPrecision>
| using Mila::Dnn::Loss< TDeviceType, TPrecision >::TensorType = Tensor<TPrecision, MR> |
Constructor & Destructor Documentation
◆ ~Loss()
template<Compute::DeviceType TDeviceType, TensorDataType TPrecision>
|
virtualdefault |
The documentation for this class was generated from the following file:
- /__w/Mila/Mila/Mila/Src/Dnn/Core/Loss.ixx
