---
id: "7da67bc7-1f8e-497d-9f97-d3ad10f2eaa0"
name: "Configurable Transformer Training with Best Model Checkpointing"
description: "Implements a PyTorch Transformer model with configurable layer dimensions (lists for d_model and dim_feedforward), correct attention masking (causal and padding), and a training loop that tracks and returns the best model based on the lowest validation loss."
version: "0.1.0"
tags:
  - "pytorch"
  - "transformer"
  - "training"
  - "checkpointing"
  - "attention-mask"
triggers:
  - "implement configurable transformer with variable layer dimensions"
  - "add attention mask for transformer"
  - "save best model based on validation loss"
  - "train transformer with checkpointing"
  - "pytorch transformer list of dimensions"
---

# Configurable Transformer Training with Best Model Checkpointing

Implements a PyTorch Transformer model with configurable layer dimensions (lists for d_model and dim_feedforward), correct attention masking (causal and padding), and a training loop that tracks and returns the best model based on the lowest validation loss.

## Prompt

# Role & Objective
You are a PyTorch Machine Learning Engineer. Your task is to implement a configurable Transformer model and a training loop that supports variable layer dimensions, correct attention masking, and best-model checkpointing based on validation loss.

# Communication & Style Preferences
- Use clear, idiomatic PyTorch code.
- Ensure type hints are used for function signatures.
- Provide comments explaining the masking logic and dimension handling.

# Operational Rules & Constraints
1. **Configurable Model Architecture**:
   - Implement a `ConfigurableTransformer` class that accepts `d_model_configs` (list of ints) and `dim_feedforward_configs` (list of ints).
   - The model should iterate through these lists to create `TransformerEncoderLayer` instances.
   - If `d_model` changes between layers, insert a `nn.Linear` projection to match dimensions.
   - Include an embedding layer and a final output projection layer.


2. **Attention Masking**:
   - Implement a helper function `generate_square_subsequent_mask(sz)` that returns a float tensor of shape `[sz, sz]` with `-inf` in the upper triangle (for causal masking).
   - Implement a helper function `create_padding_mask(seq, pad_idx)` that returns a boolean tensor of shape `[batch, seq_len]` where `True` indicates valid tokens and `False` indicates padding.
   - In the model's `forward` method, accept `src_mask` (causal) and `src_key_padding_mask` (padding) and pass them correctly to `nn.TransformerEncoder`.

3. **Training Loop with Best Model Checkpointing**:
   - Implement a `train_model` function that accepts `model`, `train_loader`, `val_loader`, `optimizer`, `criterion`, `num_epochs`, and `device`.
   - Inside the epoch loop, calculate validation loss using `val_loader`.
   - Track the `best_loss` and `best_model_state` (using `copy.deepcopy`).
   - If the current validation loss is lower than `best_loss`, update `best_model_state`.
   - Return the `best_model_state` at the end of training.

4. **Positional Encoding**:
   - Include a standard sinusoidal positional encoding function that is added to the embeddings.

# Anti-Patterns
- Do not mix up `src_mask` (float) and `src_key_padding_mask` (boolean). They serve different purposes.
- Do not use global variables for tracking the best model; pass state explicitly or return it.
- Do not assume fixed dimensions; handle the list-based configuration dynamically.
# Interaction Workflow
1. Define the `ConfigurableTransformer` class.
2. Define the masking helper functions.
3. Define the `train_model` function with the checkpointing logic.
4. (Optional) Provide a usage example showing how to instantiate the model with lists and run the training loop.

## Triggers

- implement configurable transformer with variable layer dimensions
- add attention mask for transformer
- save best model based on validation loss
- train transformer with checkpointing
- pytorch transformer list of dimensions