Practice 1

Foundations & Basic PyTorch Operations

1. Basic Tensor Creation:

   Question: Write a PyTorch code snippet to create a 3×3 tensor of random numbers and multiply it by 2.

   Why: To ensure you understand tensor creation, basic arithmetic, and using built‑in functions.

2. Tensor from Python Data Structures:

   Question: How do you create a tensor from a list of lists and compute its element‑wise square?

   Why: It practices converting Python lists to tensors and applying element‑wise operations.

3. Tensor Indexing & Slicing:

   Question: Write code to slice a given 4×4 tensor to extract the middle 2×2 sub-tensor.

   Why: It reinforces your ability to index and manipulate tensor subsets.

4. Broadcasting in PyTorch:

   Question: Demonstrate broadcasting by adding a 1×3 tensor to a 4×3 tensor.

   Why: To understand how PyTorch automatically expands dimensions during operations.

5. Matrix Multiplication:

   Question: Write a snippet that computes the matrix product of two tensors using torch.matmul.

   Why: To become comfortable with linear algebra operations that are foundational in deep learning.

6. Understanding Data Types:

   Question: How do you change a tensor’s data type (e.g., from float32 to int64) and why might this be important?

   Why: It’s critical to know how data types affect model computations and performance.

Autograd & Custom Gradients

7. Gradient Computation Basics:

   Question: Write a code snippet to compute the gradient of a simple scalar function (e.g., f(x)=x²) using torch.autograd.

   Why: To understand how PyTorch tracks operations and computes gradients automatically.

8. Disabling Gradients:

   Question: How would you disable gradient calculations during inference using PyTorch? Write an example.

   Why: This practice is essential for saving memory and speeding up inference.

9. Custom Autograd Function:

   Question: Create a custom PyTorch autograd Function for a simple operation (for example, a custom square function) that implements both forward and backward passes.

   Why: To deepen your understanding of the autograd system and custom gradient computation.

10. In-place vs. Out-of-place Operations:

    Question: Explain the difference between in-place and out-of-place tensor operations and demonstrate with a code example.

    Why: To learn how in-place operations can affect gradient tracking and memory usage.

Building Neural Network Modules

11. Simple Linear Model:

    Question: Implement a linear regression model using nn.Module in PyTorch.

    Why: To practice building a custom model and understand module structure.

12. Feedforward Neural Network:

    Question: Code a two-layer MLP for a simple classification task and explain each component.

    Why: It builds your skills in structuring multi-layer networks.

13. Activation Functions:

    Question: Write a custom PyTorch module that applies ReLU or GELU activation and explain why non-linearities are essential.

    Why: To appreciate the role of activation functions in deep networks.

14. Building a Convolutional Layer:

    Question: Create a custom convolutional layer using PyTorch’s functional API.

    Why: To understand low-level implementation details of convolution, a building block for many models.

15. Dropout Implementation:

    Question: Write code to add dropout in a neural network module and explain its role during training vs. inference.

    Why: Dropout is crucial for regularization, and you should know how to integrate it properly.

16. Batch Normalization:

    Question: Implement a simple network that uses BatchNorm, and explain how it improves training stability.

    Why: Batch normalization is a widely used technique to accelerate convergence.

Training Loop & Model Optimization

17. Custom Training Loop:

    Question: Write a complete training loop (forward pass, loss computation, backpropagation, and optimizer step) for a simple neural network.

    Why: To master the end-to-end process of training models in PyTorch.

18. Using GPU Acceleration:

    Question: How do you modify your training loop to leverage GPU acceleration in PyTorch? Provide a code example.

    Why: To ensure you can transfer models and data to GPU for faster computation.

19. Learning Rate Scheduling:

    Question: Write code to implement a learning rate scheduler using torch.optim.lr_scheduler and explain its benefit.

    Why: Adaptive learning rates are important for effective training.

20. Gradient Clipping:

    Question: How do you apply gradient clipping in a training loop? Write a code snippet.

    Why: It prevents exploding gradients, which is especially useful in training deep or recurrent networks.

21. Saving and Loading Models:

    Question: Write functions to save a model’s state and then load it back.

    Why: To understand model serialization and checkpointing for production deployment.

22. Custom Loss Function:

    Question: Create and implement a custom loss function in PyTorch (e.g., a weighted mean squared error).

    Why: Building custom loss functions deepens your understanding of how optimization objectives affect training.

23. Early Stopping Mechanism:

    Question: Write code to implement early stopping in a training loop.

    Why: Early stopping is a practical strategy to prevent overfitting and save resources.

24. Model Evaluation:

    Question: Code a validation loop that computes accuracy and loss on a validation set, and discuss its integration in training.

    Why: To ensure you can evaluate your models effectively during training.

25. Data Augmentation & Custom DataLoader:

    Question: Write a custom PyTorch Dataset class and DataLoader that applies data augmentation on-the-fly.

    Why: Data loading and augmentation are key to building robust models.

Deep Dive into Transformers & LLMs

26. Implementing Positional Encoding:

    Question: Write a PyTorch module to compute sinusoidal positional encodings for sequence data.

    Why: Positional encoding is vital for Transformers to capture sequence order.

27. Scaled Dot-Product Attention:

    Question: Code the scaled dot-product attention mechanism and explain its components (queries, keys, values, scaling).

    Why: Attention mechanisms are at the heart of Transformers.

28. Multi-head Attention Module:

    Question: Implement a multi-head attention module in PyTorch, including splitting and concatenating heads.

    Why: Multi-head attention enhances the model’s ability to capture diverse features.

29. Transformer Encoder Block:

    Question: Build a single Transformer encoder block that includes multi-head attention, layer normalization, and a feedforward network.

    Why: It’s a core building block for LLMs and advanced NLP models.

30. Masked Self-Attention:

    Question: Code a masked self-attention layer for auto-regressive generation, explaining how and why the mask is applied.

    Why: To understand how Transformers handle sequential data during generation.

31. Feedforward Network in Transformer:

    Question: Implement the position-wise feedforward network used in Transformer layers.

    Why: It complements the attention mechanism and is essential for learning non-linear transformations.

32. Layer Normalization from Scratch:

    Question: Write your own PyTorch implementation of layer normalization and compare it to nn.LayerNorm.

    Why: Understanding normalization techniques helps optimize model training.

33. Custom Transformer Decoder Layer:

    Question: Build a Transformer decoder layer, incorporating masked attention and encoder-decoder attention mechanisms.

    Why: Decoders are critical for sequence-to-sequence tasks like translation.

34. Implementing a Simple Transformer Model:

    Question: Assemble a full Transformer model for a language modeling task, including embeddings, positional encoding, encoder/decoder stacks, and a linear output layer.

    Why: To integrate all components into a functioning model from scratch.

35. Handling Variable-length Sequences:

    Question: Write code to implement padding and create attention masks for variable-length sequences in a Transformer model.

    Why: Proper masking is key to processing batches with varying sequence lengths.

36. Custom Activation Functions:

    Question: Implement the GELU activation function in PyTorch without using built-in functions.

    Why: To deepen your understanding of non-linear activations and their numerical implementations.

Advanced Topics & Production-level Coding

37. Gradient Accumulation:

    Question: Write a training loop that uses gradient accumulation to simulate larger batch sizes on limited GPU memory.

    Why: It helps when hardware constraints limit batch size.

38. Mixed-Precision Training:

    Question: Modify your training loop to use PyTorch’s AMP for mixed-precision training.

    Why: Mixed-precision can significantly speed up training while conserving memory.

39. Custom Collate Function:

    Question: Write a custom collate function for a DataLoader that dynamically pads sequences for a batch.

    Why: To efficiently handle variable-length data during batching.

40. Integrating TensorBoard:

    Question: Write a snippet that logs loss and accuracy during training using TensorBoard.

    Why: Monitoring training metrics is critical for debugging and optimizing performance.

41. Implementing Beam Search:

    Question: Code a basic beam search algorithm for sequence generation using a Transformer model.

    Why: Beam search is a common technique to improve generation quality in LLMs.

42. Caching in Auto-Regressive Generation:

    Question: Implement a caching mechanism to store and reuse key/value pairs during Transformer inference.

    Why: This optimization is crucial for efficient text generation.

43. Visualization of Attention Weights:

    Question: Write a function that extracts and visualizes attention weights from a Transformer layer.

    Why: Visualization helps you understand and debug model behavior.

44. Model Parallelism Basics:

    Question: Explain and demonstrate how to split a model’s layers across multiple GPUs using PyTorch’s distributed features.

    Why: To handle large models that exceed the memory of a single GPU.

45. Implementing a Custom Optimizer/Modifier:

    Question: Write code that customizes an existing optimizer (e.g., by modifying learning rates per layer) or implements a simple custom optimizer.

    Why: To explore and understand optimization strategies beyond standard implementations.

46. Integrating PyTorch Lightning:

    Question: Refactor one of your training scripts using PyTorch Lightning and discuss the benefits.

    Why: Lightning helps to simplify and structure training code for scalability and reproducibility.

47. Custom Callbacks in Lightning:

    Question: Create a custom PyTorch Lightning callback for early stopping or dynamic learning rate adjustments.

    Why: Custom callbacks further automate training management and improve model performance.

48. Distributed Data Parallel (DDP):

    Question: Write a sample script that trains a model using PyTorch’s Distributed Data Parallel (DDP) on multiple GPUs.

    Why: Understanding DDP is crucial for scaling training on multi-GPU systems.

49. Profiling GPU Memory Usage:

    Question: Develop a small utility that profiles and logs GPU memory usage during model training/inference.

    Why: Profiling helps you optimize memory consumption and debug performance issues.

50. End-to-End LLM Implementation:

    Question: Implement a Transformer-based language model from scratch—including training, evaluation, and inference scripts—that can be fine-tuned on a sample dataset.

    Why: This capstone challenge integrates all components (tensor operations, autograd, custom modules, advanced optimization, and distributed training) to simulate a production-level LLM development process.

Happy coding and good luck with your preparation! Kummesey!