Forward and Backward Passes
The forward and backward passes are fundamental components of the training process for small language models (SLMs). They involve the flow of data through the model and the adjustment of model parameters based on the computed gradients. Here’s a comprehensive overview of both processes.
Forward Pass
The forward pass is the initial phase in the training of a neural network where input data is processed through the model to generate predictions. This involves several key steps:
Input Representation: The input text is tokenized and converted into numerical representations (embeddings) that the model can process.
Layer Processing: The input embeddings are fed into the model's architecture, typically composed of multiple layers (e.g., transformer layers in SLMs). Each layer applies a series of transformations, including linear transformations, activation functions (like ReLU or softmax), and normalization processes.
Output Generation: The final layer produces the model's output, which could be a probability distribution over the vocabulary for the next token prediction in language modeling tasks. This output is compared to the ground truth labels to compute the loss, which quantifies the difference between the predicted and actual values.
Loss Calculation: A loss function (such as cross-entropy loss) is used to evaluate the model's performance. This loss serves as a feedback signal for the subsequent backward pass.
Backward Pass
The backward pass is the second phase, where the model learns from the errors made during the forward pass. This process involves:
Gradient Calculation: Using the computed loss, the model calculates gradients for each parameter with respect to the loss. This is typically done using the backpropagation algorithm, which applies the chain rule to compute the gradient efficiently across all layers.
Weight Update: Once the gradients are calculated, the model's parameters (weights) are updated to minimize the loss. This is done using an optimization algorithm (like Stochastic Gradient Descent or Adam), which adjusts the weights in the opposite direction of the gradients.
Parameter Adjustment: The learning rate, a hyperparameter that determines the size of the weight updates, plays a crucial role in this step. Proper tuning of the learning rate is essential to ensure effective learning without overshooting the optimal values.
Iteration: The forward and backward passes are repeated for multiple epochs, allowing the model to iteratively improve its predictions as it learns from the training data.
Summary
In summary, the forward pass is responsible for generating predictions and calculating the loss, while the backward pass focuses on updating the model parameters based on the loss gradients. Together, these processes enable small language models to learn from data, refine their predictions, and ultimately improve their performance across various language tasks. This training cycle is essential for developing effective SLMs that can operate efficiently, even in resource-constrained environments.
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