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add docs for input_output format (#1367) [skip ci]

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README.md
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</details> </details>
##### Template-Free
- `input_output`: template-free prompt construction
```json
{"segments": [{"label": true|false, "text": "..."}]}
```
This is a special format that allows you to construct prompts without using templates. This is for advanced users who want more freedom with prompt construction. See [these docs](docs/input_output.md) for more details.
##### Conversation ##### Conversation
- `sharegpt`: conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt) - `sharegpt`: conversations where `from` is `human`/`gpt`. (optional: first row with role `system` to override default system prompt)

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# Template-free prompt construction with the `input_output` format
<!-- TOC -->
- [Background](#background)
- [Masking Inputs](#masking-inputs)
- [You may not want prompt templates](#you-may-not-want-prompt-templates)
- [The `input_output` format](#the-input_output-format)
- [Usage](#usage)
- [1. Prepare Data](#1-prepare-data)
- [2. Use `type: input_output`](#2-use-type-input_output)
- [3. Check the prompts](#3-check-the-prompts)
<!-- /TOC -->
<a id="markdown-background" name="background"></a>
## Background
<a id="markdown-masking-inputs" name="masking-inputs"></a>
### Masking Inputs
One of the most popular features of
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) is
setting the following configuration value:
```yaml
train_on_inputs: false
```
If you declare a [dataset formats](https://github.com/OpenAccess-AI-Collective/axolotl?tab=readme-ov-file#dataset)
such as `alpaca` or `chatml`, axolotl knows what is an input
(i.e. human) vs. an output (i.e. the assistant) and masks the input
labels so that your model can focus on predicting the outputs only.
<a id="markdown-you-may-not-want-prompt-templates" name="you-may-not-want-prompt-templates"></a>
### You may not want prompt templates
However, there are many situations where you don't want to use one of
these formats or templates (I usually don't!). This is because they can:
- Add unnecessary boilerplate to your prompts.
- Create artifacts like special delimiters `<|im_start|>` that can
quickly become footguns if you don't include them correctly at
inference time.
- Enforce a *chat* interface when you do not want one. Sometimes you
just want to fine-tune a model to a very specific task and do NOT
want multi-turn conversations, roles, etc.
- Limit you to only certain roles that the template allows.
<a id="markdown-the-inputoutput-format" name="the-inputoutput-format"></a>
### The `input_output` format
You can construct your prompts without a template by using the
`input_output` format, by setting `type: input_output` in your
configuration file like this:
**config.yml**
```yaml
train_on_inputs: false # Mask segments of your data
datasets:
- path: output.jsonl
type: input_output # use template free prompt construction
```
Unlike `type: completion`, which is also template-free,
`type: input_output` allows you to mask segments of your text. More
details on how this works are described below.
<a id="markdown-usage" name="usage"></a>
## Usage
This is how you can use the `input_output` format:
<a id="markdown-1-prepare-data" name="1-prepare-data"></a>
### 1. Prepare Data
To use the `input_output` format, collect your data in the following
format into a jsonl file (below is the first row from the file
`output`.jsonl` pretty printed):
```bash
$ head -n1 output.jsonl | python -m json.tool
{.cell-output .cell-output-stdout}
{
"segments": [
{
"label": true,
"text": "<s>Hello\n"
},
{
"label": true,
"text": "hi there!. "
},
{
"label": false,
"text": "goodbye "
},
{
"label": true,
"text": "farewell</s>"
}
]
}
```
Set `label:false` when you want to mask a segment of text so that the
model isn't trained on it. Some things to keep in mind:
> [!IMPORTANT]
> 1. **EOS, BOS, spaces, newlines etc. are entirely up to you. Axolotl
concatenates all the segments as-is.** The tokenizer doesn't add
anything additional. Notice how I added spaces, newlines, `<s>`
(BOS), and `</s>` (EOS) myself.
> 2. Make sure you check the materialized output to validate that the
prompt is getting assembled how you like.
<a id="markdown-2-use-type-inputoutput" name="2-use-type-inputoutput"></a>
### 2. Use `type: input_output`
Let's materialize data with our `output.jsonl` file by setting
`type: input_output` in our axolotl config:
```yaml
# training_config.yaml
base_model: mistralai/Mistral-7B-v0.1
data_seed: 49
seed: 49
datasets:
- path: output.jsonl
type: input_output
val_set_size: 0.1
sequence_len: 896
sample_packing: false
micro_batch_size: 2
gradient_accumulation_steps: 3
eval_batch_size: 2
num_epochs: 1
learning_rate: 0.0002
train_on_inputs: false
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
You can use the following command to materialize your data. The
`--debug` flag will print the tokens, along with the labels so you can
verify that the correct items are being ignored:
```bash
$ python -m axolotl.cli.preprocess training_config.yaml --debug
...
[2024-03-05 23:36:46,969] [INFO] [axolotl.check_example_labels:35] [PID:607731] [RANK:0] <s>(1, 1) Hello(22557, 22557)
(13, 13) hi(12014, 12014) there(736, 736) !(28808, 28808) .(28723, 28723) (28705, 28705) good(-100, 1179) bye(-100, 17664) (-100, 28705) fare(19111, 19111) well(5458, 5458) </s>(2, 2)
```
The format is `decoded_token`(`label`, `token_id`), for example,
`<s>(1, 1)` means that the token is `<s>`, the label is `1` and the
token_id is `1`. When the label is `-100` then that token is ignored for
training.
<a id="markdown-3-check-the-prompts" name="3-check-the-prompts"></a>
### 3. Check the prompts
Here is another way to check the materialized output:
```python
from transformers import AutoTokenizer
from datasets import load_from_disk
import yaml
directory = !ls last_run_prepared/
with open('training_config.yaml', 'r') as f:
cfg = yaml.safe_load(f)
model_id = cfg['base_model']
tok = AutoTokenizer.from_pretrained(model_id)
ds = load_from_disk(f'last_run_prepared/{directory[0]}/')
```
```python
>>> row = ds[0]
>>> print(tok.decode(row['input_ids']))
<s> Hello
hi there!. goodbye farewell</s>
```
We can check that the right tokens are ingored by comparing the labels
to each token:
```python
import pandas as pd
pd.DataFrame([{'token': tok.decode(i), 'label': l, 'id':i} for i,l in
zip(row['input_ids'], row['labels'])])
```
| token | label | id |
|-------|-------|-------|
| 0 | \<s\> | 1 |
| 1 | Hello | 22557 |
| 2 | \\n | 13 |
| 3 | hi | 12014 |
| 4 | there | 736 |
| 5 | ! | 28808 |
| 6 | . | 28723 |
| 7 | | 28705 |
| 8 | good | -100 |
| 9 | bye | -100 |
| 10 | | -100 |
| 11 | fare | 19111 |
| 12 | well | 5458 |
| 13 | \</s\>| 2 |
If we look at the input data, the above table seems correct! (The jsonl
version is repeated below for reference):
```bash
$ head -n1 output.jsonl | python -m json.tool
{.cell-output .cell-output-stdout}
{
"segments": [
{
"label": true,
"text": "<s>Hello\n"
},
{
"label": true,
"text": "hi there!. "
},
{
"label": false,
"text": "goodbye "
},
{
"label": true,
"text": "farewell</s>"
}
]
}
```