Pre-processing Pipelines in Python: The Production Foundry

We have learned all the individual pieces:

1. Tokenization: Turning text into numbers.
2. Special Tokens: Managing BOS/EOS boundaries.
3. Truncation: Cutting data to fit the context window.
4. Padding & Masking: Formatting batches and focusing model learning.

Now, it’s time to build the Pipeline. In a real-world scenario, you don't do these steps manually for every example. You use a pipeline that processes thousands of rows of data in parallel, saving the results to a "Cache" so you don't have to repeat the work every time you start a training job.

In this final lesson of Module 7, we will build a production-ready pre-processing pipeline using the Hugging Face datasets library.

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1. The Pipeline Architecture

A good pipeline is Idempotent (doing it twice gives the same result) and Efficient (it uses all your CPU cores).

graph LR
    A["Raw JSONL File"] --> B["Mapped Processing Function"]
    B --> C["Tokenization"]
    B --> D["Prompt Masking"]
    B --> E["Length Validation"]
    D & E --> F["Pre-processed Dataset (PyTorch Tensors)"]
    F --> G["Disk Cache / Training Loop"]
    
    subgraph "The 'Worker' Core"
    B
    C
    D
    end

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2. Implementation: The tokenize_and_mask Function

This is the "Brain" of your pipeline. It takes a raw conversation and produces the input_ids, attention_mask, and labels.

from transformers import AutoTokenizer
from datasets import load_dataset
import torch

# 1. Setup
MODEL_NAME = "mistralai/Mistral-7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
tokenizer.pad_token = tokenizer.eos_token # Standard practice for Mistral/Llama

def preprocess_function(examples):
    """
    Standard SFT Preprocessing:
    - Combines messages into a single string
    - Tokenizes
    - Masks the USER portion of the loss
    """
    # a. Combine messages into text (Simplified ChatML)
    texts = []
    for msgs in examples["messages"]:
        text = f"USER: {msgs[0]['content']}\nASSISTANT: {msgs[1]['content']}"
        texts.append(text)
        
    # b. Tokenize everything
    tokenized = tokenizer(
        texts,
        truncation=True,
        max_length=1024,
        padding="max_length"
    )
    
    # c. Create labels (Clone of input_ids)
    labels = []
    for i, input_id in enumerate(tokenized["input_ids"]):
        label = list(input_id)
        
        # d. FIND the boundary of the Assistant response
        # In a real scenario, you'd find the index of the 'ASSISTANT:' token
        # For this demo, we assume the first 50 tokens are the prompt
        prompt_len = 50 
        for j in range(prompt_len):
            label[j] = -100 # Mask the prompt
            
        labels.append(label)
        
    tokenized["labels"] = labels
    return tokenized

# 3. Running the Pipeline at Scale
dataset = load_dataset("json", data_files="my_data.jsonl", split="train")

# batched=True allows processing 1000s of rows in parallel on CPU
processed_dataset = dataset.map(
    preprocess_function,
    batched=True,
    num_proc=4, # Use 4 CPU cores
    remove_columns=dataset.column_names # Keep only tensors
)

print(f"Dataset Ready: {len(processed_dataset)} examples")

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3. The Performance Bottleneck: num_proc

Fine-tuning is a GPU-intensive task, but Pre-processing is a CPU-intensive task. If you have 100,000 examples, tokenizing them on a single CPU core will take hours. By using the num_proc argument in the map function, you can distribute the work across all available cores, reducing processing time from hours to minutes.

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4. Saving the "Artifacts"

Once your pipeline finishes, you should save the processed data to a binary format like Apache Arrow. This allows the training loop to instantly "memory-map" the data without re-tokenizing.

processed_dataset.save_to_disk("./data/tokenized_v1")
# Next time, just load it:
# from datasets import load_from_disk
# dataset = load_from_disk("./data/tokenized_v1")

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Summary and Key Takeaways

• Pipelines automate the complex dance of mapping text to model-ready tensors.
• Batch Processing: Use batched=True to significantly speed up your data preparation.
• Tensors Only: The final output of your pipeline should only contain input_ids, attention_mask, and labels. No raw text strings should remain in the GPU samples.
• Caching: Always save your pre-processed data to disk to avoid repeated computation.

Congratulations! You have completed Module 7. You have moved from "Raw Data" to "GPU-Ready Binary Data."

In Module 8, we will finally start the training: Supervised Fine-Tuning Workflow (End to End), where we look at the hyperparameters that turn these tensors into intelligence.

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Reflection Exercise

1. Why is it better to tokenize your data before you start the training loop, rather than inside the training loop? (Hint: Think about GPU idle time).
2. If you change your tokenizer (e.g., from Llama 2 to Llama 3), do you need to rerun your entire pipeline? Why?

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