exclude some objc files
This commit is contained in:
rachguo
@@ -1,9 +0,0 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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// this header contains the entire ONNX Runtime training Objective-C API
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// the headers below can also be imported individually
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#import "onnxruntime.h"
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#import "ort_checkpoint.h"
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#import "ort_training_session.h"
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@@ -1,119 +0,0 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#import <Foundation/Foundation.h>
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#include <stdint.h>
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NS_ASSUME_NONNULL_BEGIN
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/**
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* An ORT checkpoint is a snapshot of the state of a model at a given point in time.
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*
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* This class holds the entire training session state that includes model parameters,
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* their gradients, optimizer parameters, and user properties. The `ORTTrainingSession` leverages the
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* `ORTCheckpoint` by accessing and updating the contained training state.
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*
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* Available since 1.16.
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*
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* @note This class is only available when the training APIs are enabled.
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*/
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@interface ORTCheckpoint : NSObject
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- (instancetype)init NS_UNAVAILABLE;
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/**
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* Creates a checkpoint from directory on disk.
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*
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* @param path The path to the checkpoint directory.
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* @param error Optional error information set if an error occurs.
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* @return The instance, or nil if an error occurs.
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*
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* @warning The construction of the checkpoint state requires instantiation of `ORTEnv`.
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* The intialization will fail if the `ORTEnv` is not properly initialized.
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*/
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- (nullable instancetype)initWithPath:(NSString*)path
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error:(NSError**)error NS_DESIGNATED_INITIALIZER;
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/**
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* Saves a checkpoint to directory on disk.
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*
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* @param path The path to the checkpoint directory.
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* @param includeOptimizerState Flag to indicate whether to save the optimizer state or not.
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* @param error Optional error information set if an error occurs.
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* @return Whether the checkpoint was saved successfully.
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*/
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- (BOOL)saveCheckpointToPath:(NSString*)path
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withOptimizerState:(BOOL)includeOptimizerState
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error:(NSError**)error;
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/**
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* Adds an int property to this checkpoint.
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*
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* @param name The name of the property.
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* @param value The value of the property.
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* @param error Optional error information set if an error occurs.
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* @return Whether the property was added successfully.
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*/
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- (BOOL)addIntPropertyWithName:(NSString*)name
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value:(int64_t)value
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error:(NSError**)error;
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/**
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* Adds a float property to this checkpoint.
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*
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* @param name The name of the property.
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* @param value The value of the property.
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* @param error Optional error information set if an error occurs.
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* @return Whether the property was added successfully.
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*/
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- (BOOL)addFloatPropertyWithName:(NSString*)name
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value:(float)value
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error:(NSError**)error;
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/**
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* Adds a string property to this checkpoint.
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*
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* @param name The name of the property.
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* @param value The value of the property.
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* @param error Optional error information set if an error occurs.
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* @return Whether the property was added successfully.
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*/
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- (BOOL)addStringPropertyWithName:(NSString*)name
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value:(NSString*)value
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error:(NSError**)error;
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/**
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* Gets an int property from this checkpoint.
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*
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* @param name The name of the property.
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* @param error Optional error information set if an error occurs.
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* @return The value of the property or 0 if an error occurs.
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*/
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- (int64_t)getIntPropertyWithName:(NSString*)name
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error:(NSError**)error __attribute__((swift_error(nonnull_error)));
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/**
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* Gets a float property from this checkpoint.
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*
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* @param name The name of the property.
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* @param error Optional error information set if an error occurs.
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* @return The value of the property or 0.0f if an error occurs.
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*/
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- (float)getFloatPropertyWithName:(NSString*)name
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error:(NSError**)error __attribute__((swift_error(nonnull_error)));
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/**
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*
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* Gets a string property from this checkpoint.
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*
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* @param name The name of the property.
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* @param error Optional error information set if an error occurs.
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* @return The value of the property.
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*/
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- (nullable NSString*)getStringPropertyWithName:(NSString*)name
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error:(NSError**)error __attribute__((swift_error(nonnull_error)));
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@end
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NS_ASSUME_NONNULL_END
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@@ -1,263 +0,0 @@
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// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#import <Foundation/Foundation.h>
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#include <stdint.h>
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NS_ASSUME_NONNULL_BEGIN
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@class ORTCheckpoint;
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@class ORTEnv;
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@class ORTValue;
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@class ORTSessionOptions;
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/**
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* Trainer class that provides methods to train, evaluate and optimize ONNX models.
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*
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* The training session requires four training artifacts:
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* 1. Training onnx model
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* 2. Evaluation onnx model (optional)
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* 3. Optimizer onnx model
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* 4. Checkpoint directory
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*
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* [onnxruntime-training python utility](https://github.com/microsoft/onnxruntime/blob/main/orttraining/orttraining/python/training/onnxblock/README.md)
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* can be used to generate above training artifacts.
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*
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* Available since 1.16.
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*
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* @note This class is only available when the training APIs are enabled.
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*/
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@interface ORTTrainingSession : NSObject
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- (instancetype)init NS_UNAVAILABLE;
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/**
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* Creates a training session from the training artifacts that can be used to begin or resume training.
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*
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* The initializer instantiates the training session based on provided env and session options, which can be used to
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* begin or resume training from a given checkpoint state. The checkpoint state represents the parameters of training
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* session which will be moved to the device specified in the session option if needed.
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*
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* @param env The `ORTEnv` instance to use for the training session.
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* @param sessionOptions The `ORTSessionOptions` to use for the training session.
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* @param checkpoint Training states that are used as a starting point for training.
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* @param trainModelPath The path to the training onnx model.
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* @param evalModelPath The path to the evaluation onnx model.
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* @param optimizerModelPath The path to the optimizer onnx model used to perform gradient descent.
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* @param error Optional error information set if an error occurs.
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* @return The instance, or nil if an error occurs.
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*
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* @note Note that the training session created with a checkpoint state uses this state to store the entire training
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* state (including model parameters, its gradients, the optimizer states and the properties). The training session
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* keeps a strong (owning) pointer to the checkpoint state.
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*/
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- (nullable instancetype)initWithEnv:(ORTEnv*)env
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sessionOptions:(ORTSessionOptions*)sessionOptions
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checkpoint:(ORTCheckpoint*)checkpoint
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trainModelPath:(NSString*)trainModelPath
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evalModelPath:(nullable NSString*)evalModelPath
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optimizerModelPath:(nullable NSString*)optimizerModelPath
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error:(NSError**)error NS_DESIGNATED_INITIALIZER;
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/**
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* Performs a training step, which is equivalent to a forward and backward propagation in a single step.
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*
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* The training step computes the outputs of the training model and the gradients of the trainable parameters
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* for the given input values. The train step is performed based on the training model that was provided to the training session.
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* It is equivalent to running forward and backward propagation in a single step. The computed gradients are stored inside
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* the training session state so they can be later consumed by `optimizerStep`. The gradients can be lazily reset by
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* calling `lazyResetGrad` method.
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*
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* @param inputs The input values to the training model.
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* @param error Optional error information set if an error occurs.
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* @return The output values of the training model.
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*/
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- (nullable NSArray<ORTValue*>*)trainStepWithInputValues:(NSArray<ORTValue*>*)inputs
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error:(NSError**)error;
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/**
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* Performs a evaluation step that computes the outputs of the evaluation model for the given inputs.
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* The eval step is performed based on the evaluation model that was provided to the training session.
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*
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* @param inputs The input values to the eval model.
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* @param error Optional error information set if an error occurs.
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* @return The output values of the eval model.
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*
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*/
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- (nullable NSArray<ORTValue*>*)evalStepWithInputValues:(NSArray<ORTValue*>*)inputs
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error:(NSError**)error;
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/**
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* Reset the gradients of all trainable parameters to zero lazily.
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*
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* Calling this method sets the internal state of the training session such that the gradients of the trainable parameters
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* in the ORTCheckpoint will be scheduled to be reset just before the new gradients are computed on the next
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* invocation of the `trainStep` method.
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*
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* @param error Optional error information set if an error occurs.
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* @return YES if the gradients are set to reset successfully, NO otherwise.
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*/
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- (BOOL)lazyResetGradWithError:(NSError**)error;
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/**
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* Performs the weight updates for the trainable parameters using the optimizer model. The optimizer step is performed
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* based on the optimizer model that was provided to the training session. The updated parameters are stored inside the
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* training state so that they can be used by the next `trainStep` method call.
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*
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* @param error Optional error information set if an error occurs.
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* @return YES if the optimizer step was performed successfully, NO otherwise.
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*/
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- (BOOL)optimizerStepWithError:(NSError**)error;
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/**
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* Returns the names of the user inputs for the training model that can be associated with
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* the `ORTValue` provided to the `trainStep`.
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*
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* @param error Optional error information set if an error occurs.
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* @return The names of the user inputs for the training model.
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*/
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- (nullable NSArray<NSString*>*)getTrainInputNamesWithError:(NSError**)error;
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/**
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* Returns the names of the user inputs for the evaluation model that can be associated with
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* the `ORTValue` provided to the `evalStep`.
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*
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* @param error Optional error information set if an error occurs.
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* @return The names of the user inputs for the evaluation model.
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*/
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- (nullable NSArray<NSString*>*)getEvalInputNamesWithError:(NSError**)error;
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/**
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* Returns the names of the user outputs for the training model that can be associated with
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* the `ORTValue` returned by the `trainStep`.
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*
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* @param error Optional error information set if an error occurs.
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* @return The names of the user outputs for the training model.
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*/
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- (nullable NSArray<NSString*>*)getTrainOutputNamesWithError:(NSError**)error;
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/**
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* Returns the names of the user outputs for the evaluation model that can be associated with
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* the `ORTValue` returned by the `evalStep`.
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*
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* @param error Optional error information set if an error occurs.
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* @return The names of the user outputs for the evaluation model.
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*/
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- (nullable NSArray<NSString*>*)getEvalOutputNamesWithError:(NSError**)error;
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/**
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* Registers a linear learning rate scheduler for the training session.
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*
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* The scheduler gradually decreases the learning rate from the initial value to zero over the course of the training.
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* The decrease is performed by multiplying the current learning rate by a linearly updated factor.
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* Before the decrease, the learning rate is gradually increased from zero to the initial value during a warmup phase.
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*
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* @param warmupStepCount The number of steps to perform the linear warmup.
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* @param totalStepCount The total number of steps to perform the linear decay.
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* @param initialLr The initial learning rate.
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* @param error Optional error information set if an error occurs.
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* @return YES if the scheduler was registered successfully, NO otherwise.
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*/
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- (BOOL)registerLinearLRSchedulerWithWarmupStepCount:(int64_t)warmupStepCount
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totalStepCount:(int64_t)totalStepCount
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initialLr:(float)initialLr
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error:(NSError**)error;
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/**
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* Update the learning rate based on the registered learning rate scheduler.
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*
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* Performs a scheduler step that updates the learning rate that is being used by the training session.
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* This function should typically be called before invoking the optimizer step for each round, or as necessary
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* to update the learning rate being used by the training session.
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*
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* @note A valid predefined learning rate scheduler must be first registered to invoke this method.
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*
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* @param error Optional error information set if an error occurs.
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* @return YES if the scheduler step was performed successfully, NO otherwise.
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*/
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- (BOOL)schedulerStepWithError:(NSError**)error;
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/**
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* Returns the current learning rate being used by the training session.
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*
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* @param error Optional error information set if an error occurs.
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* @return The current learning rate or 0.0f if an error occurs.
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*/
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- (float)getLearningRateWithError:(NSError**)error __attribute__((swift_error(nonnull_error)));
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/**
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* Sets the learning rate being used by the training session.
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*
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* The current learning rate is maintained by the training session and can be overwritten by invoking this method
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* with the desired learning rate. This function should not be used when a valid learning rate scheduler is registered.
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* It should be used either to set the learning rate derived from a custom learning rate scheduler or to set a constant
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* learning rate to be used throughout the training session.
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*
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* @note It does not set the initial learning rate that may be needed by the predefined learning rate schedulers.
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* To set the initial learning rate for learning rate schedulers, use the `registerLinearLRScheduler` method.
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*
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* @param lr The learning rate to be used by the training session.
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* @param error Optional error information set if an error occurs.
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* @return YES if the learning rate was set successfully, NO otherwise.
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*/
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- (BOOL)setLearningRate:(float)lr
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error:(NSError**)error;
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/**
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* Loads the training session model parameters from a contiguous buffer.
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*
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* @param buffer Contiguous buffer to load the parameters from.
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* @param error Optional error information set if an error occurs.
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* @return YES if the parameters were loaded successfully, NO otherwise.
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*/
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- (BOOL)fromBufferWithValue:(ORTValue*)buffer
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error:(NSError**)error;
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/**
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* Returns a contiguous buffer that holds a copy of all training state parameters.
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*
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* @param onlyTrainable If YES, returns a buffer that holds only the trainable parameters, otherwise returns a buffer
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* that holds all the parameters.
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* @param error Optional error information set if an error occurs.
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* @return A contiguous buffer that holds a copy of all training state parameters.
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*/
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- (nullable ORTValue*)toBufferWithTrainable:(BOOL)onlyTrainable
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error:(NSError**)error;
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/**
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* Exports the training session model that can be used for inference.
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*
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* If the training session was provided with an eval model, the training session can generate an inference model if it
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* knows the inference graph outputs. The input inference graph outputs are used to prune the eval model so that the
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* inference model's outputs align with the provided outputs. The exported model is saved at the path provided and
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* can be used for inferencing with `ORTSession`.
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*
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* @note The method reloads the eval model from the path provided to the initializer and expects this path to be valid.
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*
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* @param inferenceModelPath The path to the serialized the inference model.
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* @param graphOutputNames The names of the outputs that are needed in the inference model.
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* @param error Optional error information set if an error occurs.
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* @return YES if the inference model was exported successfully, NO otherwise.
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*/
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- (BOOL)exportModelForInferenceWithOutputPath:(NSString*)inferenceModelPath
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graphOutputNames:(NSArray<NSString*>*)graphOutputNames
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error:(NSError**)error;
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@end
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#ifdef __cplusplus
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extern "C" {
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#endif
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/**
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* This function sets the seed for generating random numbers.
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* Use this function to generate reproducible results. It should be noted that completely reproducible results are not guaranteed.
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*
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* @param seed Manually set seed to use for random number generation.
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*/
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void ORTSetSeed(int64_t seed);
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#ifdef __cplusplus
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}
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#endif
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NS_ASSUME_NONNULL_END
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Reference in New Issue
Block a user