This allows the inference code to start up with a prompt, instead of
waiting for user input from stdin. Allows easier scripting, useful for
batch generation, benchmarking etc.

Inference example code. At the moment, the code simply loads a model
state file and generates text using that. Parameters like max sequence
length, whether training used fp16, what the tokenizer used for training
is etc., need to be passed manually by the user (there's a lot of room
for error here). To be improved.

Merges changes from !14
Closes !14

This reverts commit cb893907, reversing
changes made to 83f7b518.

Restructure project

See merge request !13

Add inference code

See merge request !10

Alexandru-Mihai GHERGHESCU authored 1 year ago and Vlad-Andrei BĂDOIU (78692) committed 1 year ago

Reorganize the folder structure to make the project look like an actual
library.

Move training example outside of framework code.

Add merge request template

See merge request !12

Fix datasets memory issues

See merge request !9

Fix a number of issues with the infrastructure, no major rework

See merge request !11

Add a merge request template which aids in contributing to the codebase.

Also see https://docs.gitlab.com/ee/user/project/description_templates.html.

Visual change, correctly display final training loss.

The final training loss didn't account for gradient accumulation, and
was therefore much smaller than it should've been in reality.

Fix the estimation interval, which was also not properly calculated due
to gradient accumulation.

There was a corner case when the shape of the predictions y of the
dataset would not be correct, due to the fact that the number of batches
was miscalculated.

This happened when `batch_len` was exactly divisible by `seq_len`, since
the predictions, which are simply the text shifted once to the right,
would not have that extra column at the end.

Fix the above issue by decrementing the number of available batches with
1 when `batch_len` exactly divides by `seq_len`.

Visual change. This only changes what the trainer reports as the final
training loss.

Not quite sure if the value before was accurate anyway, since gradient
accumulation would not let the optimizer step every batch anyway.

For a big enough dataset, this should not have any impact at all.

The final loss value will be reported based on the last calculation of
the loss, correctly taking into consideration gradient accumulation as
well.

This takes into account endlines too. Just a visual accuracy change.

Fix problems with some types. This enables Python's static type checks
to correctly identify some issues before runtime.

Add model constructor arguments (n_layers, n_heads, dim etc.) as pytorch
buffers. This packs them together with the model weights when calling
`torch.save()`, and loads them back in when calling `torch.load()`.

Eventually, these should be saved separately, however this will do for
now.

Visual change.

Add note in the README file about using the environment variable
CUDA_VISIBLE_DEVICES, which lets the user choose which GPU to run
training on.

The normalization layer returned float32 tensors, instead of fp16
tensors, which should've been the case when training with mixed
precision. This raised a runtime error of incompatible types. Rearrange
the operations to properly compute the norm in float32, but return the
value in fp16.

Total loss wasn't properly initialized, leading to a runtime error.

Fix an issue where the whole files were read in memory at once. E.g.
reading TinyStories train dataset (a 2.2GB file) would fill up 20GB of
RAM due to variable allocation inside Python.

The fix uses I/O buffering and reads lines one by one, processing them
at the same time. This leads to a much lower RAM usage (around the
size of the file), and also increases processing speed.

Fix tokenizer typos, add newlines

See merge request !8

Gradient accumulation

See merge request !6

32K vocab -> 16K vocab

Fix some typos in the tokenizer file.

Add newlines and whitespaces to the tokenizer model. Previously, all the
whitespace was stripped and joined into a single blank. This allows for
better tokenization for things like wikitext103, which has articles
containing newlines, with relevance.

Fix small typos in the model architecture

See merge request !7

Add gradient accumulation to the training loop. The number of gradient
accumulation steps is exposed by the trainer.

Make the gradient clipping norm value a parameter fed to the trainer.

Fix some issues with the wikitext103 dataset

See merge request !4

Add tinystories dataset

See merge request !3

Add progress bar display for training

See merge request !2

Couple of things:
- rewrite code to better check when the dataset is downloaded
- better cleanup after download + unzip
- more aggresive exit on checksum mismatch
- rewrite __main__