Concat prompt and completion cols for tokenizing

[nitsanluke]

✨ Description

Migrated from #248; this PR allows a dataset with prompt and completion specifically and in general any pair of text columns (eg: question and answer) to be combined and tokenized. (It is limited to two columns of input covering majority of use-cases). Further if the user needs loss mask span for based on prompt (part of the sequence) they can include them as well.
Note: Merge after #255

🔍 Type of change

Select all that apply:

• 🐛 Bug fix (non-breaking change that addresses a specific issue)
• 🚀 New feature (non-breaking change that adds functionality)
• ⚠️ Breaking change (a change that could affect existing functionality)
• 📈 Performance improvement/optimization (improves speed, memory usage, or efficiency)
• 🛠️ Code refactor (non-functional changes that improve code readability, structure, etc.)
• 📦 Dependency bump (updates dependencies, including Dockerfile or package changes)
• 📝 Documentation change (updates documentation, including new content or typo fixes)
• 🔧 Infrastructure/Build change (affects build process, CI/CD, or dependencies)

📝 Changes

List the key changes introduced in this PR:

1. Create a new data source schema for prompt & completion style datasets
2. Include concat func. and loss masking span creation

✅ Checklist

Make sure the following tasks are completed before submitting the PR:

General

• 📜 I have read and followed the contributing guidelines.
• 🏷️ I am using a clear and descriptive PR title that summarizes the key change or feature introduced.
• 🎉 The functionality is complete, and I have tested the changes.
• 📝 I have updated the documentation if needed.
• ⚠️ The change does not introduce any new issues (e.g., runtime warnings, type checker errors, linting problems, unhandled edge cases).
• 🧩 I have commented my code, especially in hard-to-understand areas.

Sample config:

loading_workers: 1
tokenize_workers: 1
saving_workers: 1
output_path: ./debug_test/gsm8k
dataset:
  path: openai/gsm8k
  config_name: main
  split: train
  trust_remote_code: true
  source_schema:
    prompt_column: question
    completion_column: answer
    delimiter: " "

tokenizer:
  path: /mnt/slam_checkpoints/upstream/Mistral-Nemo-Base-2407/

[sohamparikh]

same comment as above about num_proc

[tscholak]

@sohamparikh @nitsanluke, I was thinking about the tradeoff we're making here between these two options:

1. (right now) merge the two columns into one, add spans, then split it up again to be tokenized individually. the benefit is that we can reuse the processing for the default schema, so it's a form of input normalization. the downside is that this seems to be not very straightforward or explicit.
2. (could be) tokenize the two columns individually, then merge.

I feel like with more input schemas being added, some refactor of what we introduce here is unavoidable. Can we be more forward-looking?

[sohamparikh]

I agree with tokenizing columns individually, can do the same when we support chat templates
#262