Configure and instantiate SydsGPT (345M-style)¶

This cell prepares a ready-to-use SydsGPT model with a GPT-2–345M–like configuration.

It imports PyTorch and the model class, defines a configuration dictionary, sets a random seed for reproducibility, instantiates the model, and switches it to evaluation mode.

What the code does¶

• import torch: Loads PyTorch for tensors, RNG control, and device utilities.

• from model.SydsGPT import SydsGPT: Imports the model class from the local model package.

• SYDSGPT_CONFIG_345M = {...}: Defines key hyperparameters:

  • vocab_size (int): Size of the tokenizer vocabulary the model predicts over.

  • context_length (int): Maximum sequence length (number of time steps) the model attends to.

  • embedding_dim (int): Hidden size / channel dimension of token and position embeddings and transformer layers.

  • num_heads (int): Number of attention heads per transformer block. Should divide embedding_dim evenly.

  • num_layers (int): Number of stacked transformer blocks (depth).

  • dropout (float): Dropout probability used in training; disabled automatically in eval mode.

  • qkv_bias (bool): Whether to include bias terms in Q/K/V projection layers.

• torch.manual_seed(246): Sets the RNG seed for reproducibility (affects random init, sampling, etc.).

• model = SydsGPT(SYDSGPT_CONFIG_345M): Builds the model using the provided config.

• model.eval(): Puts the model in inference mode (disables dropout; batch-norm-like training behavior is not used in this architecture).

Expected inputs and outputs¶

• Input: integer token IDs shaped (batch_size, seq_len) with 0 <= token_id < vocab_size.

• Output: logits shaped (batch_size, seq_len, vocab_size) ready for softmax or sampling.

Device and performance tips¶

• To use GPU if available:

  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  
  model = model.to(device)
  
  x = x.to(device)
  

• For memory-sensitive scenarios, consider bfloat16/float16 inference (on supported hardware):

  model = model.to(device).to(dtype=torch.bfloat16)
  
  x = x.to(device)
  
  with torch.autocast(device_type=device.type, dtype=torch.bfloat16):
  
      logits = model(x)
  

Common pitfalls¶

• ModuleNotFoundError: Ensure you run the notebook from the repo root (where this notebook lives) and that model/ and modules/ contain __init__.py.

• num_heads must evenly divide embedding_dim.

• Using model.train() during inference will enable dropout and change outputs; keep model.eval() for deterministic behavior (given a fixed seed and inputs).

Simple text generation with tiktoken¶

This cell demonstrates a minimal text-generation loop using the model and GPT‑2 BPE tokenization.

It converts input text to token IDs, generates a few new tokens with generate_simple, then decodes the tokens back to text.

What the code does¶

• from modules.GenerateSimple import generate_simple: Imports a helper that appends new tokens to the context.

• import tiktoken: Loads the GPT‑2 tokenizer implementation used for encoding/decoding.

• text_to_tokens(text, tokenizer): Encodes a string into token IDs and wraps them into a batch of size 1 with shape (1, seq_len).

• tokens_to_text(tokens, tokenizer): Decodes a tensor of token IDs back to a string.

• tokenizer = tiktoken.get_encoding("gpt2"): Uses the GPT‑2 BPE vocabulary (vocab size 50257).

• input_text = "Once upon a time": Seed prompt for generation.

• input_tokens = text_to_tokens(...): Converts the prompt to (1, seq_len) tensor.

• output_tokens = generate_simple(model, input_tokens, 10, context_length): Generates 10 new tokens (total length increases by up to 10, subject to context length).

• print(...): Shows the decoded text after generation.

Inputs and outputs¶

• Input text: a Python string prompt.

• Encoded input: input_tokens with dtype torch.long and shape (1, T) where 0 <= token_id < vocab_size.

• Output tokens: a tensor typically shaped (1, T + N) where N is max_new_tokens (here 10), but may be shorter if the function stops early (e.g., on end‑of‑text).

• Decoded output: a Python string built from output_tokens.

Tokenization notes¶

• This example allows the special token <|endoftext|> during encoding:

  tokenizer.encode(text, allowed_special={"<|endoftext|>"})
  

• Decoding uses tokenizer.decode(...) to reconstruct readable text.

Batch inference and greedy token selection (baseline)¶

This cell runs a batched forward pass on two prompts, converts logits to probabilities, and selects the most likely token at each position via greedy argmax. It then decodes these per‑position predictions to text for a quick qualitative check.

What the code does¶

• Defines two short input prompts and two short “target” snippets for comparison.

• Encodes strings to token IDs with text_to_tokens and batches them using torch.cat to form tensors of shape (batch_size=2, seq_len).

• Computes model logits with model(batch_input_tokens) → shape (B, T, V) where V = vocab_size.

• Converts logits to probabilities with softmax and picks the most likely token id at each position using argmax over the vocab dimension.

• Decodes the resulting token IDs back to text with tokens_to_text for a quick sanity check.

Shapes and dtypes¶

• batch_input_tokens: (B, T) of dtype torch.long.

• logits: (B, T, V) of dtype torch.float32 (or your model’s dtype).

• probs: (B, T, V) after softmax.

• generated_tokens: (B, T, 1) after argmax(..., keepdim=True).

Selecting target probabilities and estimating a simple loss¶

This cell extracts probabilities assigned to the target tokens, converts them to log‑probs, and computes an averaged negative log‑probability (a proxy for loss). It demonstrates indexing into a (batch, time, vocab) probability tensor and aggregates results across two examples.

Tensors and shapes¶

• probs: shape (B, T, V) — probabilities over the vocabulary at every time step for each example.

• batch_target_tokens: shape (B, T) — integer token IDs for the target sequence per example.

• batch_index: scalar int selecting which example (0 or 1 here).

What each line does¶

1. batch_index = 0 (and later 1): chooses which example in the batch to analyze.

2. target_probs_1 = probs[batch_index, [0,1,2,3], batch_target_tokens[batch_index]]:

   • Narrows to the chosen example → probs[batch_index] has shape (T, V).

   • Uses advanced indexing with two indexers:

     • Time indices [0,1,2,3] select 4 positions from the time dimension.

     • batch_target_tokens[batch_index] is a length‑T vector of token IDs and indexes the vocab dimension.

   • Because these two indexers have different shapes (4,) and (T,), PyTorch broadcasts them, producing a matrix of shape (4, T). Each row corresponds to a chosen time step; each column corresponds to a target token at some position in the sequence. This yields a grid of probabilities, not a 1‑to‑1 per‑step alignment.

3. Repeat for batch_index = 1 to get target_probs_2 with shape (4, T) as well.

4. log_probs = torch.log(torch.cat((target_probs_1, target_probs_2))): concatenates along the first dimension → shape (8, T) and applies natural log to get log‑probs.

5. mean_log_probs = torch.mean(log_probs): averages all selected log‑probs.

6. negative_mean_log_probs = mean_log_probs * -1: converts to a positive quantity akin to an average negative log‑probability.

Inspecting logits/targets and computing cross‑entropy loss¶

This cell prints tensor shapes/values, flattens logits and targets to a 2‑D/1‑D form, and computes a categorical cross‑entropy loss. It’s a quick way to sanity‑check model outputs against targets and to illustrate how classification loss is applied to sequence models.

What the code does¶

• Prints logits.shape and batch_target_tokens.shape to verify expected dimensions:

  • logits: (B, T, V) — unnormalized scores over the vocabulary for each batch/time position.

  • batch_target_tokens: (B, T) — integer token IDs (class labels) for each batch/time position.

• Flattens tensors for loss computation:

  • flat_logits = logits.flatten(0, 1) → shape ((B*T), V).

  • flat_targets = batch_target_tokens.flatten() → shape ((B*T),).

• Computes cross‑entropy using torch.nn.functional.cross_entropy on raw logits and integer class targets.

Why flatten?¶

Most loss functions in PyTorch expect 2‑D logits (N, C) and 1‑D targets (N,). Flattening the batch and time dimensions treats every time step in the batch as an independent classification example, which is equivalent to computing the mean loss over all (b, t) positions.

Dtype, device, and numerical stability¶

• logits should be floating point (e.g., float32), and targets must be integer type (torch.long).

• Ensure logits and targets reside on the same device (CPU/GPU) to avoid runtime errors.

• cross_entropy is numerically stable; you rarely need to clamp or add epsilons yourself.

Conclusion¶

• We get the same Cross Entropy loss as our manual calculation in the previous step.

Load raw corpus and estimate token count¶

This cell reads a text corpus from disk and reports:

• Total number of characters in the file (useful for sanity checks and throughput planning).

• Total number of tokens after GPT‑2 BPE encoding using tiktoken (this approximates the training token budget).

What the code does¶

• Opens data/all_books.txt with UTF‑8 encoding and loads it into memory as a single string.

• Uses the previously created tokenizer = tiktoken.get_encoding("gpt2") to encode the entire string to token IDs.

• Prints character and token counts.

Prerequisites¶

• The file data/all_books.txt should exist relative to this notebook’s working directory (the repository root).

• A tokenizer must be available in the notebook scope (earlier cells set tokenizer = tiktoken.get_encoding("gpt2")).

Notes on GPT‑2 BPE tokenization¶

• GPT‑2 uses a byte‑level BPE with a vocabulary size of 50,257; token count ≠ word count.

• Token counts depend on punctuation, whitespace, and casing; the same characters can yield different tokenizations if the text changes slightly.

Create training/validation DataLoaders from raw text¶

This cell splits the raw corpus into train/validation subsets and builds batched (input, target) sequences for next‑token prediction using a fixed context window.

What the code does¶

• Splits text_data with training_ratio = 0.9:

  • training_dataset = first 90% of characters

  • validation_dataset = remaining 10%

• Uses modules.DataLoader.create_dataloader(...) to produce iterable loaders that yield (x, y) batches.

• Reports the number of batches and prints the shape of one batch from each loader for a quick sanity check.

Key parameters¶

• max_length = SYDSGPT_CONFIG_345M['context_length']:

  • Sequence length per sample (context window size).

• step_size = SYDSGPT_CONFIG_345M['context_length'] // 2:

  • Half‑overlapping windows (stride is half the window). This increases dataset size vs non‑overlapping windows and improves sample diversity at the cost of more compute.

• batch_size = 64:

  • Number of sequences per batch. Tune based on GPU/CPU memory; larger batches increase throughput but require more memory.

• shuffle = True:

  • Randomizes sample order to improve training stability.

• drop_last = True:

  • Drops incomplete final batch to keep shapes consistent.

• num_workers = 0:

  • Data loading in the main process. Increase (e.g., 2–8) to parallelize preprocessing if your implementation supports it.

Expected batch shapes and semantics¶

• x: LongTensor (batch_size, max_length) — input token IDs.

• y: LongTensor (batch_size, max_length) — target token IDs.

• Language modeling convention: logits at time t are trained to predict y at time t+1 (a one‑token shift). Your create_dataloader should either prepare y accordingly or you can apply the shift when computing loss.

Assumptions (API contract)¶

• create_dataloader(text, max_length, step_size, ...) tokenizes text (using the same tokenizer as earlier), slices into windows of length max_length with stride step_size, and returns batches of (x, y) suitable for a next‑token objective.

• If your implementation expects pre‑tokenized data or returns different shapes, adjust parameters or downstream code accordingly.

Tips and troubleshooting¶

• Throughput vs redundancy:

  • Smaller step_size → more overlapping windows → more training samples but higher redundancy. Start with max_length//2 and tune.

• Memory pressure:

  • Reduce batch_size if you hit OOM; alternatively, keep batch_size and reduce max_length.

Mini utility: per-batch loss computation¶

This helper computes the cross‑entropy loss for a single batch. It moves tensors to the desired device, runs the model forward pass, flattens logits/targets into the expected shapes, and returns a scalar loss suitable for backward().

Function signature¶

• calc_batch_loss(input_batch, target_batch, model, device) -> torch.Tensor

  • Returns a scalar 0‑D tensor (the mean cross‑entropy over all tokens in the batch).

Inputs and shapes¶

• input_batch: LongTensor (B, T) — token IDs fed into the model.

• target_batch: LongTensor (B, T) — token IDs used as labels.

• model: a nn.Module with forward(input_batch) -> logits of shape (B, T, V).

• device: target device (e.g., torch.device("cuda") or "cpu").

What happens inside¶

1. Moves input_batch and target_batch to device for consistency.

2. Computes logits = model(input_batch) with shape (B, T, V).

3. Flattens for loss: logits.flatten(0, 1) → ((B*T), V) and target_batch.flatten() → ((B*T),).

4. Applies torch.nn.functional.cross_entropy to raw logits and integer targets (no softmax needed).

5. Returns the mean loss across all (B*T) token positions as a scalar.

Compute average loss over a DataLoader (validation helper)¶

This utility iterates over a DataLoader, computes the per‑batch cross‑entropy via calc_batch_loss, and returns the mean of batch losses. It’s intended for quick validation/evaluation, not training.

Function signature¶

• calc_loader_loss(data_loader, model, device, num_batches=None) -> float

  • Returns a Python float: the average of num_batches batch losses.

Parameters¶

• data_loader: Iterable yielding (input_batch, target_batch) tensors, typically shaped (B, T) each.

• model: The language model (nn.Module). For evaluation, call model.eval() beforehand.

• device: torch.device or string identifying the device (e.g., "cuda", "cpu").

• num_batches (optional): If provided, caps the number of batches processed (useful for fast estimates). Defaults to the full length of the loader when available.

What the code does¶

1. Initializes total_loss = 0.

2. Handles an empty loader by returning nan early.

3. Determines how many batches to process:

   • If num_batches is None, uses len(data_loader).

   • Else takes min(num_batches, len(data_loader)).

4. Iterates over the loader and calls calc_batch_loss(...) for each batch until the cap is reached.

5. Sums batch_loss.item() and returns total_loss / num_batches.

Averaging semantics and weighting¶

• calc_batch_loss uses cross_entropy(..., reduction='mean') on flattened (B*T) elements, so each batch loss is the mean per token within that batch.

• This helper averages those batch means equally across batches. If all batches are the same size (common when drop_last=True), this equals the dataset mean.

Performance tips¶

• Wrap calls in torch.no_grad() or torch.inference_mode() to disable autograd and save memory/time during validation.

• Increase num_workers in your dataloader (if supported) to accelerate data prep.

• Ensure batches and model are on the same device to avoid implicit transfers.

Evaluate initial training/validation loss (no grad)¶

This cell runs a quick, read-only evaluation to get baseline losses on the training and validation loaders. It’s useful for sanity checking your pipeline before training (e.g., confirming shapes/devices, dataloader yield, and a reasonable initial loss).

What the code does¶

• Picks a device: cuda if available, else cpu.
• Prints the chosen device for visibility.
• Moves the model to that device: model.to(device).
• Disables autograd with torch.no_grad() and computes:
  • training_loss = calc_loader_loss(training_dataloader, model, device)
  • validation_loss = calc_loader_loss(validation_dataloader, model, device)
• Prints both losses.

Interpreting the numbers¶

• The loss is the mean cross‑entropy over all tokens seen by the helper.
• With random initialization and a vocab of 50,257, a loss near ln(50257) ≈ 10.82 is expected. Lower is better; after training, you should see this decrease.

Example options:

• Fast estimate: calc_loader_loss(training_dataloader, model, device, num_batches=50)
• Exact: calc_loader_loss(training_dataloader, model, device, num_batches=num_train_batches) where num_train_batches = count_batches_streaming(...).

Helper: generate_sample_text (quick qualitative checkpoint)¶

This helper function produces a short sample completion from the current model state after (or during) training. It lets you visually inspect language quality and drift without exporting or writing a separate script.

Function Signature¶

def generate_sample_text(model, tokenizer, device, start_context):
    ...

Parameters¶

• model (nn.Module): Your SydsGPT instance (already moved to device). Should be in training or eval mode depending on outer loop.
• tokenizer (tiktoken.Encoding): GPT‑2 BPE tokenizer used for encoding the prompt and decoding output tokens.
• device (torch.device): Target compute device (e.g., cuda or cpu). Ensures input tokens match model placement.
• start_context (str): The initial prompt (seed text) to condition generation. Can be a sentence fragment, instruction, or domain phrase.

Returns¶

• No explicit return; prints a one‑line generated string (newlines collapsed to spaces). You could refactor to return the string if needed.

Step‑by‑Step Workflow¶

1. Switches the model to eval() to:
   • Disable dropout.
   • Avoid gradient tracking (paired with an explicit torch.no_grad() block).
2. Determines context_size from the model config (SYDSGPT_CONFIG_345M['context_length']). This sets the max total token length (prompt + generated) the function will attempt.
3. Encodes start_context into token IDs and wraps into a batch of size 1; moves tensor to device.
4. Calls generate_simple(model, input_tokens, 100, context_size):
   • Greedy extension up to max_new_tokens=100 or until context limit reached.
   • generate_simple internally ensures input tokens are on the same device as model (device‑safety fix applied earlier).
5. Decodes resulting tokens back to text via the tokenizer.
6. Prints the generated text with newlines replaced by spaces for cleaner logging.
7. Restores model.train() so the outer training loop can continue collecting gradients.

Usage Patterns¶

Typical placement:

• End of each epoch (qualitative progress checkpoint).
• After major learning rate changes.
• Before and after loading a checkpoint to verify restoration.

Example invocation inside a loop:

generate_sample_text(model, tokenizer, device, start_context="Once upon a time")

Design Considerations¶

• Non‑Determinism: If you include dropout or sample stochastically (e.g., top‑k, temperature), results change across calls. Here, greedy decoding + eval() yields deterministic output given fixed weights.
• Prompt Length vs. Context: If start_context is already near context_length, generation will be short or zero. Consider truncating long prompts.
• Performance: 100 tokens is modest; for faster feedback on large models reduce to 32 or 64.
• Mode Switching: Ensures the model returns to training mode automatically—prevents accidental dropout disablement mid‑training.

Summary¶

generate_sample_text is a lightweight, deterministic snapshot tool: it briefly transitions the model to evaluation, performs greedy decoding for a fixed number of tokens, prints the result, and returns the model to training mode—giving you rapid human feedback on training progress without disrupting optimization.

Training Loop: train_model_v1 (core optimization engine)¶

This section documents the training function responsible for iterating over data, computing gradients, updating model weights, periodically evaluating progress, checkpointing state, and logging token throughput.

Function Signature¶

def train_model_v1(model,
                   training_dataloader,
                   validation_dataloader,
                   optimizer,
                   device,
                   num_epochs,
                   evaluation_frequency,
                   evaluation_iterations,
                   start_context,
                   tokenizer,
                   checkpoint_interval=500):
    ...

Arguments¶

Returns¶

A tuple (training_losses, validation_losses, total_tokens_processed):

• training_losses: List of sampled training loss values (one per evaluation event).
• validation_losses: List of sampled validation loss values aligned with training_losses indices.
• total_tokens_processed: Cumulative token counts (one entry per training step logged) enabling plotting loss vs. tokens.

Internal State Variables¶

• tokens_processed: Running count of all token positions used in gradient steps across steps and epochs. Incremented by input_batch.numel() (which = batch_size * sequence_length).
• global_step: Counts optimization steps across epochs (starts at -1, incremented at each batch). Used to trigger evaluations and checkpoints. Starting at -1 ensures after first increment the first batch is step 0 (aligns with modulo logic for early evaluation/checkpoint if desired).

Step-by-Step Flow¶

1. Epoch Loop: Repeats num_epochs times.
2. Set model.train(): Ensures dropout (if any) and gradient accumulation are active.
3. Batch Loop (Training):
   • Zero gradients: optimizer.zero_grad() (baseline approach; could switch to gradient accumulation strategy if needed).
   • Forward + Loss: Uses calc_batch_loss which (a) moves tensors to device, (b) computes logits, (c) applies cross-entropy across flattened (B*T) tokens.
   • Backprop: loss.backward() computes gradients.
   • Optimizer Step: optimizer.step() applies parameter updates.
   • Update token counter: tokens_processed += input_batch.numel().
   • Increment global step.
   • Append the new cumulative token count to total_tokens_processed.
   • Progress print: provides epoch, step, and cumulative tokens.
4. Conditional Evaluation (if global_step % evaluation_frequency == 0):
   • Switch to model.eval().
   • Wrap in torch.no_grad() to disable gradient tracking.
   • Compute approximate training loss: calc_loader_loss(training_dataloader, ..., num_batches=evaluation_iterations).
   • Compute approximate validation loss similarly.
   • Append both losses to respective lists and print a concise summary.
   • Return to model.train().
5. Conditional Checkpoint (if global_step % checkpoint_interval == 0):
   • Save a dictionary with both model and optimizer state dicts to autosave_sydsgpt_345m_trained_model_optimizer.pth.
6. End of Epoch Qualitative Sample: Calls generate_sample_text using start_context to qualitatively track improvements.
7. Loop Continue or Exit: Repeats until all epochs complete; returns logged metrics.

Evaluation Strategy Rationale¶

• Using a small evaluation_iterations drastically reduces overhead, enabling frequent snapshots (e.g., every 100 steps) without stalling training.
• For a precise validation curve later, run a dedicated full evaluation pass using the batch counting helpers from the streaming dataloader (if integrated) or iterate fully with drop_last=False.

Checkpointing Notes¶

• Each autosave overwrites the same filename, minimizing disk usage.
• For resilience, consider timestamped or step‑indexed filenames, e.g.: autosave_step_{global_step}.pth.
• Include tokens_processed and global_step in future checkpoint metadata to support exact resumption.

Logging & Monitoring¶

• Current approach: simple print statements.
• Optional enhancements:
  • Use tqdm progress bars (wrap dataloader) for ETA visibility.
  • Log scalars (loss, tokens) to TensorBoard or a tracking service.
  • Store (step, training_loss, validation_loss, tokens_processed) as rows in a CSV for later analysis.

Performance Tips¶

• Prefer larger batch sizes if GPU memory permits—improves arithmetic intensity.
• If dataloader becomes bottleneck: increase num_workers, enable pin_memory=True, pre‑tokenize (if not already streaming efficiently).
• Track tokens_processed vs. wall clock time to estimate throughput (tokens/sec) for capacity planning.

Usage Example¶

training_losses, validation_losses, token_counts = train_model_v1(
    model, training_dataloader, validation_dataloader, optimizer, device,
    num_epochs=5, evaluation_frequency=100, evaluation_iterations=2,
    start_context="Once upon a time", tokenizer=tokenizer, checkpoint_interval=500)

Summary¶

train_model_v1 provides a clear, modular training scaffold: it tracks tokens, performs scheduled lightweight evaluations, produces qualitative samples, and saves recoverable checkpoints. This makes it a solid foundation for iterative experimentation and scaling with minimal friction.

Initial training run (from scratch)¶

This cell wires everything together to kick off a multi‑epoch training run using the previously defined train_model_v1 loop. It selects a device, seeds RNG for reproducibility, instantiates a fresh model, configures an optimizer, and starts training with periodic evaluations and qualitative sampling.

What the code does¶

1. Chooses a compute device (cuda if available, else cpu) and prints it.
2. Sets a fixed torch seed (246) so initialization and early steps are repeatable.
3. Creates a new SydsGPT model with the 345M‑style config and moves it to the selected device.
4. Constructs an AdamW optimizer over model parameters with:
   • Learning rate: 2e-4
   • Weight decay: 0.05
5. Sets num_epochs = 5 and calls train_model_v1(...) with:
   • evaluation_frequency=100: run a quick loss snapshot every 100 optimization steps.
   • evaluation_iterations=2: evaluate only 2 mini‑batches per snapshot to keep it fast.
   • start_context="Once upon a time": prompt for qualitative samples at epoch ends.
6. Captures outputs:
   • training_losses: sampled training losses (one per evaluation event)
   • validation_losses: sampled validation losses aligned with training snapshots
   • total_tokens_processed: cumulative token count across steps (for plotting)

Inputs and assumptions¶

• training_dataloader and validation_dataloader yield (input_batch, target_batch) with dtype torch.long and shape (B, T) each.
• The model’s vocab size and context_length match the tokenizer/loader.
• The training loop handles device placement for batches and uses cross_entropy on flattened logits/targets.

Tuning knobs (quick guidance)¶

• Batch size vs. context length: If you see CUDA OOM, reduce batch size first; then consider lowering context_length.
• Learning rate: 2e-4 is a reasonable starting point for AdamW; try 1e‑4 to 3e‑4 depending on stability.
• Weight decay: 0.05 encourages generalization; adjust if you notice under/over‑regularization.
• Evaluation cadence: Increase evaluation_iterations once runs are stable to get more precise estimates (at a compute cost).

Expected outputs¶

• Console logs for steps, tokens processed, and periodic training/validation losses.
• A short generated sample at the end of each epoch to qualitatively gauge progress.
• On completion, you can optionally save a final checkpoint in the next cell (separate from autosaves inside the loop).

Troubleshooting¶

• Device mismatch: Ensure model.to(device) ran before training; the loop moves batches to device internally.
• Empty/short dataloader: If your dataset is tiny, evaluation_frequency may fire too often; increase it or reduce epochs.
• Slow evaluations: Lower evaluation_iterations or run snapshots less frequently.

With this cell, you should see both quantitative (loss) and qualitative (sample text) signals to verify your pipeline is training correctly.

Final checkpoint save (explicit milestone)¶

This cell performs an explicit, manual save of the model and optimizer state at the current training milestone. Although the training loop has already produced periodic autosaves, this dedicated checkpoint is useful for tagging a known-good completion of an initial training phase (e.g., after all planned epochs).

What gets saved¶

The file sydsgpt_345m_trained_model_optimizer.pth contains a Python dictionary with two keys:

{
    "model_state_dict": model.state_dict(),
    "optimizer_state_dict": optimizer.state_dict()
}

• model_state_dict: Parameter tensors (weights, biases, embeddings, layer norms, attention projections, etc.).
• optimizer_state_dict: Internal optimizer buffers (e.g., AdamW moment estimates exp_avg, exp_avg_sq, learning rate state, step counters).

Including the optimizer state allows seamless continuation of training without losing momentum statistics or adaptive learning rate context. This preserves optimization dynamics and avoids a transient spike in loss that can occur when restarting with a freshly initialized optimizer.

Why take a manual checkpoint here¶

• Marks the end of a planned training segment (e.g., initial 5 epochs) distinctly from autosaves that may overwrite.
• Ensures you have a stable artifact before experimenting with new hyperparameters, architectures, or data.
• Provides a rollback point if subsequent fine‑tuning or continued training degrades performance.

Reloading later¶

To resume training or run inference:

checkpoint = torch.load("sydsgpt_345m_trained_model_optimizer.pth", map_location=device)
model.load_state_dict(checkpoint["model_state_dict"])
optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
# Move optimizer state tensors to device (see later reload cell pattern)

After loading, move any optimizer tensors to the target device so continued training doesn’t trigger device mismatch errors.

Differences vs. autosave¶

Consider versioning milestone checkpoints (e.g., append _epoch5.pth, _tokens{count}.pth) if you plan multiple phases.

Best practices¶

• Keep at least two historical milestone checkpoints in case of accidental corruption.
• Record metadata externally (JSON/YAML): total tokens processed, epoch count, validation loss at save time.
• For inference-only deployment, you can discard optimizer_state_dict to reduce file size (just save model_state_dict).
• Compress large checkpoints when archiving (torch.save already uses pickle; for storage, consider zip or tar.gz).

Common pitfalls¶

Minimal variant for inference¶

If you only need the model weights:

torch.save({"model_state_dict": model.state_dict()}, "sydsgpt_345m_model_only.pth")

This is smaller and faster to load for pure generation tasks.

Summary¶

This cell captures a durable, milestone checkpoint bundling both model parameters and optimizer state—essential for faithful resumption and reproducible experimentation beyond the initial training phase.

Reloading a saved checkpoint (model + optimizer)¶

This markdown explains the checkpoint reload code directly below. Its purpose is to restore both model weights and optimizer state so you can seamlessly continue training or run inference after a previous session ended.

What the code does (line by line)¶

1. Picks a device: cuda if available else cpu.
2. Prints the chosen device for visibility.
3. Instantiates a fresh SydsGPT model with the same config used during training.
4. torch.load(..., map_location=device) loads the serialized checkpoint dict from disk and ensures all tensors are mapped onto the selected device (or CPU fallback if no GPU).
5. model.load_state_dict(checkpoint['model_state_dict']) populates the newly created model with the trained parameters.
6. Creates a new AdamW optimizer with identical hyperparameters (LR, weight decay) used earlier.
7. optimizer.load_state_dict(checkpoint['optimizer_state_dict']) restores optimizer internal buffers (moment estimates, step counters) for continuity.
8. Iterates through every tensor in the optimizer state dict and forces it onto device (sometimes map_location + load_state_dict leave optimizer state tensors on CPU while the model is on GPU—this avoids device mismatch errors during the next optimizer.step()).
9. Moves the model itself to device (model.to(device)).

Why instantiate a new model before loading¶

You need an object with the correct architecture to receive parameters. The state_dict only contains raw tensors keyed by module names; without constructing the model first, there’s nowhere to load them.

Optimizer state importance¶

Restoring optimizer buffers keeps training dynamics smooth:

• AdamW uses first (exp_avg) and second (exp_avg_sq) moment estimates to adapt per‑parameter learning rates.
• Omitting them causes a brief instability phase while moments re‑warm.
• Preserving the step counter ensures scheduler logic (if later added) resumes correctly.

Device handling details¶

• map_location=device ensures checkpoint tensors don’t try to allocate on an unavailable GPU.
• Always move optimizer state tensors explicitly after load_state_dict; some PyTorch versions leave them on the original device regardless of map_location.
• If later using multiple GPUs or DistributedDataParallel, load on CPU first, then wrap/replicate.

Minimal inference-only variant¶

If you only need text generation (no further training):

model = SydsGPT(SYDSGPT_CONFIG_345M)
checkpoint = torch.load("sydsgpt_345m_trained_model_optimizer.pth", map_location=device)
model.load_state_dict(checkpoint["model_state_dict"])
model.to(device)
model.eval()
# Skip optimizer entirely

This loads faster and uses less memory.

Summary¶

This reload cell reconstructs the training state (model + optimizer) on the chosen device, enabling seamless continuation or evaluation. Proper device mapping and optimizer state preservation prevent subtle training regressions and runtime errors.

Post‑reload qualitative sanity check (generate_sample_text call)¶

This cell performs a quick qualitative verification immediately after restoring the model and optimizer from a checkpoint. It doesn’t train or modify weights; it simply generates a short continuation from a prompt to confirm the load succeeded and the model produces coherent text.

Purpose¶

• Validate that model.load_state_dict(...) correctly restored parameters.
• Confirm the model is on the intended device (no silent CPU/GPU mismatch).
• Provide a human‑readable signal (sample text) before investing time in continued training.

What happens¶

1. Calls generate_sample_text(model, tokenizer, device, "once upon a time").
2. Inside the helper:
   • Switches to eval() (disables dropout) and wraps generation in no_grad().
   • Encodes the prompt, performs greedy extension (up to configured max new tokens in the helper), decodes output.
   • Prints the generated string (newlines flattened) and returns the model to train() mode.

Why a qualitative sample here¶

• Faster than computing a full validation loss (no dataloader iteration).
• Immediately surfaces obvious load issues (garbled or purely random tokens vs. plausible language).
• Lets you compare style with samples produced before saving the checkpoint.

Interpreting output¶

Customizing¶

• Change the prompt: "In a distant future", "Chapter 1:", domain‑specific phrase.
• Shorter test: modify helper to use fewer new tokens (e.g., 40) for speed.
• Add timing: wrap call with time.perf_counter() to gauge generation latency.

Summary¶

A lightweight, single‑call checkpoint verification: generate a deterministic sample to ensure the restored model behaves plausibly before resuming expensive training.

Continue training from a saved checkpoint (resume run)¶

This cell resumes optimization from a previously saved checkpoint. It reconstructs the model and optimizer states on the chosen device, then runs a shorter training segment with periodic evaluation and autosaves.

What the code does¶

1. Selects a device (cuda if available, else cpu) and prints it.
2. Instantiates a fresh SydsGPT with the same configuration used earlier.
3. Loads the checkpoint dictionary from disk with map_location=device so tensors land on the intended device even without a GPU.
4. Restores weights: model.load_state_dict(checkpoint["model_state_dict"]).
5. Creates an AdamW optimizer and restores its state with optimizer.load_state_dict(...) (includes moment buffers and step counters).
6. Ensures all optimizer state tensors live on device (prevents CPU/GPU mismatch during optimizer.step()).
7. Moves the model to device and kicks off training for num_epochs = 2 via train_model_v1 with quick evaluations.

Resume semantics and counters¶

• Optimizer continuity: AdamW internal buffers (exp_avg, exp_avg_sq) and state['step'] are restored, preserving learning dynamics.
• Training loop counters: train_model_v1 initializes a fresh global_step = -1 and tokens_processed = 0 each time you call it. That means:
  • Evaluation/checkpoint cadence restarts from step 0 for this resumed segment.
  • If you need a global step across sessions, extend the function to accept and persist global_step/tokens_processed.

Evaluation and checkpoint cadence¶

• evaluation_frequency = 100: samples losses every 100 updates using only evaluation_iterations = 2 mini‑batches for speed.
• Autosave: every checkpoint_interval steps (default set inside train_model_v1), an autosave writes to autosave_sydsgpt_345m_trained_model_optimizer.pth.
  • Note: This filename may overwrite a prior autosave. Use timestamped or step‑indexed names if you want a trail of snapshots.

Tuning knobs¶

• num_epochs: increase for longer continuation runs.
• lr and weight_decay: keep consistent with the original run for stability; adjust cautiously if loss plateaus.
• evaluation_iterations: raise to improve estimate accuracy (at the cost of extra compute).
• Consider enabling mixed precision (AMP) in train_model_v1 for speed on supported GPUs.

Assumptions and safety checks¶

• Model config matches the checkpoint (same embedding_dim, num_layers, num_heads, vocab_size, context_length).
• Checkpoint path sydsgpt_345m_trained_model_optimizer.pth exists in the working directory.
• Tokenizer and dataloaders are unchanged from the original training setup.

Quick verification¶

• Watch early training log lines for decreasing validation loss compared to just‑restored values.
• Generate a short sample at each epoch end (already handled via generate_sample_text inside train_model_v1).

Outputs¶

• training_losses, validation_losses: sampled losses at the chosen evaluation cadence.
• total_tokens_processed: cumulative tokens (within this resumed segment) for plotting progress.

Summary¶

This cell cleanly restores model and optimizer state, then continues training with lightweight periodic evaluations and autosaves—ideal for incremental improvements without losing prior optimization momentum.

Manual generation cell (direct greedy sampling after training)¶

This cell performs a one‑off text generation directly with the trained model—outside the wrapped helper—to give a longer qualitative sample (200 new tokens) from the prompt “once upon a time”.

What the code does (step by step)¶

1. model.eval() switches the model to evaluation mode:
   • Disables dropout.
   • Ensures deterministic output given fixed weights and input.
2. generate_simple(...) is called with:
   • The current model.
   • A tokenized prompt (conversion handled by text_to_tokens).
   • max_new_tokens=200 for a longer continuation.
   • The configured context_length (prevents exceeding the model’s positional window).
3. The returned tensor of token IDs is decoded back to human‑readable text via tokens_to_text.
4. The final string is printed for inspection.

Purpose¶

• Obtain a longer, standalone sample than the shorter (100-token) helper output.
• Compare stylistic/coherence evolution after training or fine‑tuning phases.
• Serve as a quick qualitative regression test if later modifications are made to generation logic.

Inputs and outputs¶

Greedy decoding characteristics¶

• Always selects argmax token; produces deterministic, lower‑entropy completions.
• May become repetitive over very long stretches (lack of sampling diversity). For more natural variation consider top‑k, nucleus (top‑p), or temperature sampling.

Adjustments you can make¶

After running¶

You can proceed to additional evaluation, start continuation training, or experiment with alternative decoding strategies for more creative outputs.

Summary¶

A direct, transparent greedy generation producing a longer sample from a fixed prompt—ideal for quick qualitative assessment of model fluency and coherence after training.

Token sampling demo: greedy vs. multinomial and empirical frequency¶

This cell illustrates fundamental next‑token selection strategies on a tiny handcrafted vocabulary. It shows how raw logits become probabilities, how greedy decoding picks the argmax, how stochastic sampling (torch.multinomial) introduces diversity, and how repeated sampling approximates the underlying probability distribution.

What the code builds¶

1. example_vocab: A small mapping from token strings to integer IDs (10 tokens). Acts like a miniature language model vocabulary.
2. inverse_example_vocab: Reverse lookup so we can convert sampled IDs back to readable tokens.
3. example_next_token_logits: A 1‑D tensor of unnormalized scores (logits) for each vocabulary entry.
4. example_next_token_probs = torch.softmax(logits, dim=0): Converts logits to a valid probability distribution (non‑negative, sums to 1).
5. example_greedy_next_token = torch.argmax(...): Deterministic choice of the highest‑probability token.
6. torch.multinomial(example_next_token_probs, num_samples=1): Draws one token according to the categorical distribution—probabilistic sampling.
7. get_sampled_tokens(...): Repeats multinomial sampling 1000 times to empirically estimate how often each token is chosen; prints frequencies.

Why this matters¶

Language models typically output logits over a large vocabulary each step. Generation quality depends heavily on how you transform these logits into a selected next token:

• Greedy decoding maximizes immediate probability but can produce repetitive or bland sequences.
• Stochastic sampling (optionally with temperature/top‑k/top‑p) encourages diversity and can yield more creative or natural continuations.
• Empirical sampling frequency (many draws) converges toward the theoretical probability distribution—helpful for intuition.

Key concepts demonstrated¶

Interpreting results¶

• The printed “Greedy Next Token” is the single most likely token (highest probability after softmax). In this example it should correspond to the largest logit (here before with logit 3.63).
• “Random Next Token” might or might not match the greedy token—depends on one draw from the distribution.
• Frequency table: Tokens with higher true probabilities appear more often; rare tokens may still show up occasionally, demonstrating stochastic exploration.

Quick experiment ideas¶

Try changing one logit dramatically (e.g., raise after from -5.38 to 2.5) and re-run: watch probability mass shift and empirical frequencies respond proportionally.

Summary¶

This micro‑example demystifies next‑token selection: logits → probabilities → deterministic (greedy) vs. stochastic (multinomial) choice, with repeated sampling revealing the underlying distribution. It’s a conceptual foundation for more advanced decoding strategies (top‑k, top‑p, temperature) used in full language model generation.

Temperature scaling demo: controlling randomness in sampling¶

This cell explores how the softmax temperature T reshapes a probability distribution and affects stochastic next‑token sampling. Using the same logits as the previous mini‑vocabulary example, it prints empirical frequencies at multiple temperatures to show how generation becomes more/less diverse.

What the code does¶

1. Defines softmax_with_temperature(logits, temperature):
   • Scales logits by 1/T and applies softmax.
   • For a vector of logits z, probabilities are:
     p_i(T) = exp(z_i / T) / Σ_j exp(z_j / T)
2. Iterates over temperatures = [0.1, 0.5, 1.0, 2.0]:
   • Computes temperature_scaled_probs for each T.
   • Calls get_sampled_tokens(...) (from the previous cell) to draw 1000 samples and print a frequency table for each T.

How temperature changes behavior¶

• T < 1.0 (e.g., 0.5, 0.1): sharpens the distribution
  • Increases contrast between high and low‑probability tokens.
  • Sampling becomes more deterministic; top tokens dominate frequency counts.
• T = 1.0: baseline distribution (no scaling)
  • Frequencies reflect the original softmax over example_next_token_logits.
• T > 1.0 (e.g., 2.0): flattens the distribution
  • Reduces differences between tokens.
  • Increases diversity; lower‑probability tokens appear more often.

Interpreting the output¶

• For each T, you’ll see counts for each token over 1000 draws.
• As T decreases, the most likely token (greedy token) should dominate the histogram.
• As T increases, frequencies spread toward a more uniform distribution.
• Because sampling is stochastic, exact numbers vary between runs; larger sample sizes (e.g., 10k) reduce variance and better reveal trends.

Practical guidance for generation¶

• Start with T in the 0.7–1.2 range; adjust based on desired creativity vs. factuality.
• Keep T consistent across steps within a single generation unless experimenting with annealing strategies.

Summary¶

Temperature scaling is a simple, powerful knob for controlling randomness in language model sampling: low T → precise and repetitive; high T → diverse and creative. Use it alongside top‑k/top‑p for fine‑grained control over generation quality.

Top-k filtering demo: restricting the candidate set before sampling¶

This cell demonstrates a classic decoding refinement: top-k sampling. Instead of sampling from the full vocabulary distribution, we keep only the k highest‑logit tokens, mask the rest to negative infinity (so their post‑softmax probability becomes zero), then sample within that reduced set. This balances diversity and relevance.

What the code does (step by step)¶

1. top_k = 4: Choose how many highest‑scoring tokens to retain.
2. torch.topk(example_next_token_logits, top_k) returns:
   • top_k_logits: The 4 largest logits in descending order.
   • top_k_indices: Their original token indices.
3. torch.where(example_next_token_logits < top_k_logits[-1], -inf, example_next_token_logits):
   • Finds the cutoff logit (the smallest logit among the top-k set: top_k_logits[-1]).
   • Replaces any logit below that cutoff with -inf, effectively zeroing its probability after softmax.
4. top_k_probs = torch.softmax(new_logits, dim=0): Computes normalized probabilities over only the surviving top-k logits (others become exactly 0 probability).
5. get_sampled_tokens(top_k_probs): Samples repeatedly (1000 draws) and prints frequencies among the retained tokens.

Why top-k filtering¶

Compared to pure greedy decoding, top-k can produce more varied yet still on-topic continuations. Compared to temperature-only scaling, it imposes a hard boundary on candidate tokens, preventing very low-probability choices even at higher temperatures.

Alternative masking approaches¶

• Direct index filtering: Gather only top-k indices and sample from that subset array; equivalent probability outcome.
• Top-p (nucleus) sampling: Instead of a fixed k, pick the smallest set of tokens whose cumulative probability ≥ p (e.g., 0.9). Adapts dynamically to distribution shape.
• Temperature + top-k: Sharpen or flatten within the retained set for fine-grained control.

Numerical details¶

• Using -inf (negative infinity) ensures exp(-inf) = 0 in softmax, producing exact zeros without manual renormalization.
• If implementing with large tensors, broadcasting and in-place operations can reduce memory pressure.
• Always compute softmax after masking; masking post-softmax requires renormalization manually.

Practical tips¶

• Typical values: k=20…50 for medium vocabularies (LLM generation often uses k≈40).
• Smaller k increases focus but risks repetition.

Summary¶

Top-k filtering discards the tail of the probability distribution, limiting sampling to the k most promising tokens. It offers a simple, deterministic way to balance diversity and coherence, and serves as a building block for more advanced decoding strategies used in modern language model generation.

Advanced generate function: temperature, top‑k, and EOS handling¶

This cell defines a more flexible text generation routine that supports temperature scaling, optional top‑k filtering, greedy fall‑back, and early stopping on an end‑of‑sequence (EOS) token. It also enforces the model’s context_size window by truncating the input to the most recent tokens at each step.

Function signature¶

def generate(model, input_tokens, max_new_tokens, context_size,
            temperature=1.0, top_k=None, eos_id=None):
    ...

Parameters¶

• model (nn.Module): Autoregressive language model producing logits over the vocab.
• input_tokens (LongTensor, shape (1, T0)): Seed prompt; function appends new tokens to this tensor in place.
• max_new_tokens (int): Maximum number of tokens to generate.
• context_size (int): Maximum sequence length the model attends to; older tokens are truncated beyond this window.
• temperature (float, default 1.0):
  • > 0: Scale logits by 1/temperature and sample via multinomial.
  • == 0: Use greedy argmax (deterministic).
• top_k (int | None): If set, keep only the top‑k logits per step (mask others to -inf), then apply softmax.
• eos_id (int | None): If set, stop generation early when this token is selected.

Returns¶

• LongTensor of shape (1, T0 + N) where 0 ≤ N ≤ max_new_tokens (may stop early on eos_id).

Step‑by‑step flow¶

1. Loop max_new_tokens times.
2. Slice the active context: input_context = input_tokens[:, -context_size:] to obey the model’s positional limit.
3. Forward pass with torch.no_grad(); extract last‑step logits: logits = logits[:, -1, :].
4. Optional top‑k filtering:
   • top_k_logits, _ = torch.topk(logits, top_k) → get per‑batch cutoffs.
   • min_top_k_logit = top_k_logits[:, -1] → the k‑th largest logit per example.
   • Mask all logits below the cutoff to -inf using torch.where (on the same device/dtype as logits).
5. Temperature / decoding mode:
   • If temperature > 0:
     • Scale logits by 1/temperature and compute probs = softmax(logits, dim=-1).
     • Sample next_token = torch.multinomial(probs, num_samples=1).
   • Else (temperature == 0):
     • Greedy: next_token = torch.argmax(logits, dim=-1, keepdim=True).
6. Early stop: If eos_id is provided and the sampled token equals eos_id, break.
7. Append: input_tokens = torch.cat((input_tokens, next_token), dim=1).
8. Return the extended token sequence.

Design choices and rationale¶

• Context truncation ensures compute and memory scale with context_size, not total generated length.
• Top‑k pruning removes the low‑probability tail for safer sampling, especially at higher temperatures.
• Temperature provides a single, intuitive knob for diversity: lower → more deterministic; higher → more varied.
• Greedy fallback via temperature == 0 keeps the API simple without a separate mode switch.
• eos_id allows clean termination when the model emits a special end token.

Usage examples¶

• Pure greedy (deterministic): generate(model, x, 128, context_size, temperature=0.0)
• Temperature‑only sampling: generate(model, x, 128, context_size, temperature=0.8)
• Top‑k sampling: generate(model, x, 128, context_size, temperature=0.8, top_k=40)
• Early stop on EOS: generate(model, x, 256, context_size, temperature=0.7, eos_id=tokenizer.eot_token)

Summary¶

This generate function brings together practical decoding controls—context management, temperature, top‑k, and EOS—into a compact loop suitable for qualitative sampling and quick experiments. It’s a solid baseline to plug into training checkpoints and prompt‑driven evaluations.

Example: temperature + top‑k generation (invocation)¶

This cell runs the advanced generate function on a natural prompt using both temperature scaling and top‑k filtering to produce a diverse yet controlled continuation.

What the code does¶

1. Sets a fixed RNG seed (torch.manual_seed(246)) to make sampling reproducible.
2. Defines a human‑readable prompt: "Once upon a time there was a".
3. Encodes it to token IDs and moves them to device.
4. Calls generate(...) with:
   • max_new_tokens=200: up to 200 tokens of continuation.
   • context_size=SYDSGPT_CONFIG_345M['context_length']: enforces model’s max window.
   • temperature=1.5: flattens probabilities to encourage variety.
   • top_k=30: restricts sampling to the 30 most likely tokens each step.
5. Decodes tokens back to text and prints the result.

Why combine temperature and top‑k¶

• Temperature > 1.0 increases diversity, avoiding overly‑confident loops.
• Top‑k caps the candidate set, preventing extremely unlikely tokens from appearing even when temperature is high.
• Together they provide a practical balance: varied but not nonsensical.

Tuning tips¶

• If output feels chaotic: lower temperature (e.g., 0.8–1.0) or reduce top_k.
• If output is dull/repetitive: raise temperature (1.2–1.8) or increase top_k (e.g., 50).
• Keep the prompt specific to steer the model; generic prompts amplify variance.

After running¶

Skim the output for coherence, repetition, and topic adherence. Adjust temperature and top_k to your preferences, then reuse this pattern for different prompts or integrate it into a qualitative evaluation loop across checkpoints.