| # 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. |