Transformer Model Pre-training Testing Suite SOP

This Standard Operating Procedure (SOP) outlines the steps and checkpoints needed to evaluate and test a Language Learning Model (LLM) based on a Transformer architecture prior to pre-training.

1. Model Architecture Review

Confirm model architecture aligns with the specific NLP task.
Ensure configuration parameters (number of layers, dimensions, heads, etc.) are set correctly.
Validate selection of activation functions, loss functions, and optimization methods.

2. Forward Pass Test

Use sample input to perform a forward pass and verify the output.
Ensure output shape matches the expected shape.

3. Backward Pass Test

Conduct a backward pass to validate model's capability to calculate gradients correctly.
Confirm that gradients are not null, NaN, or infinite.

4. Parameter Initialization Test

Check correct initialization of all layers and their parameters.
Inspect weights before and after a forward and backward pass to verify their correct updating.

5. Optimizer and Loss Function Test

Confirm appropriateness of optimizer and loss function for the task.
Validate reduction of loss and learning of model during initial training phases.

6. Data Loader Test

Ensure data loaders supply data in the correct format and batch size for the model.
Validate any data augmentation procedures used.

7. Learning Rate Scheduler Test

If used, verify correct setup and functionality of the learning rate scheduler.

8. Hardware Compatibility Test

Confirm model, data, and all necessary components are correctly moved to the desired device (CPU, GPU, or TPU).

9. Reproducibility Test

Set random seeds for all components that introduce randomness to ensure reproducibility of model training.

Important Metrics to Check

1. Accuracy Metrics

Perplexity: Lower values indicate better model prediction of a sample.
BLEU Score: Assesses overlap of words in predicted and actual outputs, with emphasis on word order. Particularly useful in translation tasks.
ROUGE Score: Evaluates quality of summaries by counting overlapping units (n-grams, word sequences, word pairs) between source and target text.
F1 Score: Harmonic mean of precision and recall.

2. Speed and Resource Metrics

Latency: Time it takes to generate a response post-input.
Throughput: Number of tasks the model can complete in a set time period.
Memory Consumption: Quantity of RAM consumed during prediction.

3. Qualitative Metrics

Coherence: Assessment of whether output makes sense.
Relevance: Assessment of whether output is relevant to the input query.
Versatility: Assessment of model's ability to handle diverse input types and produce coherent, relevant output.

It's important to note that there are no specific tests for accuracy metrics such as perplexity, BLEU score, ROUGE score, or F1 score, as these are often task-specific and need to be evaluated on a task-by-task basis. Furthermore, ensure to conduct manual tests for coherence, relevance, and versatility, in addition to benchmarking speed (latency and throughput) and memory consumption.