初始化项目，由ModelHub XC社区提供模型
Model: flax-community/gpt-2-tamil Source: Original Platform
This commit is contained in:
ModelHub XC
8
.flake8
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8
.flake8
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[flake8]
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exclude = venv
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ignore = E501, W503, E226, E203
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max-line-length = 85
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# E501: Line too long
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# W503: Line break occurred before binary operator
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# E226: Missing white space around arithmetic operator
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.gitattributes
vendored
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18
.gitattributes
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*.bin.* filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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*.tar.gz filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.ftz filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.log filter=lfs diff=lfs merge=lfs -text
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*.wandb filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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28
.pre-commit-config.yaml
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28
.pre-commit-config.yaml
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# See https://pre-commit.com for more information
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# See https://pre-commit.com/hooks.html for more hooks
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v3.4.0
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hooks:
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- id: trailing-whitespace
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- id: check-yaml
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- id: check-ast
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- id: check-json
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- id: check-merge-conflict
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- id: detect-private-key
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- repo: https://github.com/psf/black
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rev: 21.6b0
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hooks:
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- id: black
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args: []
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files: .
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- repo: https://gitlab.com/PyCQA/flake8
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rev: 3.9.2
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hooks:
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- id: flake8
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- repo: https://github.com/PyCQA/isort
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rev: 5.9.1
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hooks:
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- id: isort
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args: []
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files: .
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69
README.md
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69
README.md
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---
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language: ta
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datasets:
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- oscar
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- IndicNLP
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widget:
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- text: 'ஒரு ஊரிலே ஒரு காக்கைக்கு'
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---
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# GPT2-Tamil
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This repository is created as part of the Flax/Jax community week by Huggingface. The aim of this project is to pretrain a language model using GPT-2 specifically for Tamil language.
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## Setup:
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To setup the project, run the following command,
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```python
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pip install -r requirements.txt
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```
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## Model:
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Pretrained model on Tamil language using a causal language modeling (CLM) objective.
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## Dataset Used:
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The GTP-2 model is trained on [oscar dataset - ta](https://huggingface.co/datasets/oscar)
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## Intended uses & limitations:
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You can use the raw model for next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt) to look for fine-tuned versions on a task that interests you.
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## How to pretrain the model:
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To perform training, do the following steps,
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- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)
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```python
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>>> export MODEL_DIR=<model_dir>
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```
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- Create the config.json by running the following command,
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```python
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>>> python src/create_config.py
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```
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- Create the tokenizer by running the following command,
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```python
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>>> python src/train_tokenizer.py
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```
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- Once the config and tokenizer is created, run the following script to start training the flax model
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```python
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>>> python scripts/train_gpt2-oscar-tamil.sh
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```
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## How to use:
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To perform language generation using the model, pipeline can be used directly.
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- First convert the flax model to pytorch using the following command,
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```python
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python src/convert_flax_to_pytorch.py
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```
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- Use the following snippet to perform language generation,
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```python
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>>> from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
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>>> model_name = 'abinayam/gpt-2-tamil'
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>>> model = AutoModelWithLMHead.from_pretrained(model_name)
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>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
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>>> set_seed(42)
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>>> input_text = "ஒரு ஊரிலே ஒரு காக்கைக்கு"
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>>> max_len = 300
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>>> no_seq = 5
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>>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
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>>> sequence = generator(input_text, max_length=max_len, num_return_sequences=no_seq)
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```
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38
config.json
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38
config.json
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{
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"_name_or_path": "../gpt-2-tamil",
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"activation_function": "gelu_new",
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"architectures": [
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"GPT2LMHeadModel"
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],
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"attn_pdrop": 0.0,
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"bos_token_id": 50256,
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"embd_pdrop": 0.0,
|
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"eos_token_id": 50256,
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"gradient_checkpointing": false,
|
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt2",
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"n_ctx": 1024,
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"n_embd": 768,
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"n_head": 12,
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 1024,
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"resid_pdrop": 0.0,
|
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
|
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"summary_use_proj": true,
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"task_specific_params": {
|
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"text-generation": {
|
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"do_sample": true,
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"max_length": 50
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}
|
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},
|
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"torch_dtype": "float32",
|
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"transformers_version": "4.9.0.dev0",
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"use_cache": true,
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"vocab_size": 50257
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}
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1
dataset/README.md
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1
dataset/README.md
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Details of the dataset can go here. The folder can also contain dataset (downloaded locally).
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1
demo/README.md
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1
demo/README.md
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Streamlit demo can go here.
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||||
36
gpt-2-tamil/config.json
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36
gpt-2-tamil/config.json
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{
|
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"activation_function": "gelu_new",
|
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"architectures": [
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"GPT2LMHeadModel"
|
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],
|
||||
"attn_pdrop": 0.0,
|
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"bos_token_id": 50256,
|
||||
"embd_pdrop": 0.0,
|
||||
"eos_token_id": 50256,
|
||||
"gradient_checkpointing": false,
|
||||
"initializer_range": 0.02,
|
||||
"layer_norm_epsilon": 1e-05,
|
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"model_type": "gpt2",
|
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"n_ctx": 1024,
|
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"n_embd": 768,
|
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"n_head": 12,
|
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"n_inner": null,
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"n_layer": 12,
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"n_positions": 1024,
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"resid_pdrop": 0.0,
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"scale_attn_weights": true,
|
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"summary_activation": null,
|
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"summary_first_dropout": 0.1,
|
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"summary_proj_to_labels": true,
|
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"summary_type": "cls_index",
|
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"summary_use_proj": true,
|
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"task_specific_params": {
|
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"text-generation": {
|
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"do_sample": true,
|
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"max_length": 50
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}
|
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},
|
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"transformers_version": "4.9.0.dev0",
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"use_cache": true,
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"vocab_size": 50257
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}
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||||
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||||
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||||
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size 19735799
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||||
3
gpt-2-tamil/flax_model.msgpack
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3
gpt-2-tamil/flax_model.msgpack
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||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:f15aa88a1b0381444c39e9e70f17a82751f7c317d7be7e22cc9707527f9a8c27
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||||
size 497764120
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||||
1
gpt-2-tamil/tokenizer.json
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1
gpt-2-tamil/tokenizer.json
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1
model/README.md
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1
model/README.md
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@@ -0,0 +1 @@
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Model card details can go here.
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||||
1
notebook/README.md
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1
notebook/README.md
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Notebook can go here.
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||||
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pyproject.toml
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pyproject.toml
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# Black formatting
|
||||
[tool.black]
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||||
line-length = 85
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||||
include = '\.pyi?$'
|
||||
exclude = '''
|
||||
/(
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||||
\.eggs # exclude a few common directories in the
|
||||
| \.git # root of the project
|
||||
| \.hg
|
||||
| \.mypy_cache
|
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| \.tox
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| \.venv
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| _build
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| buck-out
|
||||
| build
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||||
| dist
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||||
| wandb
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||||
| model
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||||
| dataset
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||||
| notebook
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||||
)/
|
||||
'''
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||||
|
||||
# iSort
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||||
[tool.isort]
|
||||
profile = "black"
|
||||
line_length = 85
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||||
multi_line_output = 3
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include_trailing_comma = true
|
||||
skip_gitignore = true
|
||||
virtual_env = "venv"
|
||||
3
pytorch_model.bin
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3
pytorch_model.bin
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||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:47900fbb69b5f35793a23a8c8fff732430962b0f3acce89ccdc2e439c8a0bdca
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||||
size 510401385
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||||
7
requirements.txt
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7
requirements.txt
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tqdm
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transformers
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datasets
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jax
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jaxlib
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flax
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optax
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0
scripts/run.log
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0
scripts/run.log
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25
scripts/train_gpt2-oscar-tamil.sh
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scripts/train_gpt2-oscar-tamil.sh
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|
||||
#!/usr/bin/env bash
|
||||
python ../src/run_clm_flax.py \
|
||||
--output_dir="${MODEL_DIR}" \
|
||||
--model_type="gpt2" \
|
||||
--config_name="${MODEL_DIR}" \
|
||||
--tokenizer_name="${MODEL_DIR}" \
|
||||
--dataset_name="oscar" \
|
||||
--dataset_config_name="unshuffled_deduplicated_ta" \
|
||||
--do_train --do_eval \
|
||||
--block_size="512" \
|
||||
--per_device_train_batch_size="64" \
|
||||
--per_device_eval_batch_size="64" \
|
||||
--learning_rate="3e-5" \
|
||||
--warmup_steps="1000" \
|
||||
--adam_beta1="0.9" --adam_beta2="0.98" --weight_decay="0.01" \
|
||||
--overwrite_output_dir \
|
||||
--num_train_epochs="25" \
|
||||
--report_to wandb \
|
||||
--run_name trial \
|
||||
--logging_steps="500" \
|
||||
--save_steps="2500" \
|
||||
--eval_steps="2500" \
|
||||
--preprocessing_num_workers="90" \
|
||||
#--push_to_hub
|
||||
2>&1 | tee run.log
|
||||
1
scripts/wandb/debug-internal.log
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scripts/wandb/debug-internal.log
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run-20210712_164633-1ddv4131/logs/debug-internal.log
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scripts/wandb/debug.log
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scripts/wandb/debug.log
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run-20210712_164633-1ddv4131/logs/debug.log
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||||
1
scripts/wandb/latest-run
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scripts/wandb/latest-run
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|
||||
run-20210712_164633-1ddv4131
|
||||
301
scripts/wandb/run-20210712_044248-12kjsz9i/files/config.yaml
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scripts/wandb/run-20210712_044248-12kjsz9i/files/config.yaml
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wandb_version: 1
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||||
|
||||
__cached__setup_devices:
|
||||
desc: null
|
||||
value: cpu
|
||||
_n_gpu:
|
||||
desc: null
|
||||
value: 0
|
||||
_wandb:
|
||||
desc: null
|
||||
value:
|
||||
cli_version: 0.10.33
|
||||
framework: huggingface
|
||||
huggingface_version: 4.9.0.dev0
|
||||
is_jupyter_run: false
|
||||
is_kaggle_kernel: false
|
||||
python_version: 3.8.10
|
||||
t:
|
||||
1:
|
||||
- 1
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||||
- 3
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||||
- 11
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||||
4: 3.8.10
|
||||
5: 0.10.33
|
||||
6: 4.9.0.dev0
|
||||
8:
|
||||
- 5
|
||||
adafactor:
|
||||
desc: null
|
||||
value: false
|
||||
adam_beta1:
|
||||
desc: null
|
||||
value: 0.9
|
||||
adam_beta2:
|
||||
desc: null
|
||||
value: 0.98
|
||||
adam_epsilon:
|
||||
desc: null
|
||||
value: 1.0e-08
|
||||
block_size:
|
||||
desc: null
|
||||
value: 512
|
||||
cache_dir:
|
||||
desc: null
|
||||
value: null
|
||||
config_name:
|
||||
desc: null
|
||||
value: ../gpt-2-tamil/
|
||||
dataloader_drop_last:
|
||||
desc: null
|
||||
value: false
|
||||
dataloader_num_workers:
|
||||
desc: null
|
||||
value: 0
|
||||
dataloader_pin_memory:
|
||||
desc: null
|
||||
value: true
|
||||
dataset_config_name:
|
||||
desc: null
|
||||
value: unshuffled_deduplicated_ta
|
||||
dataset_name:
|
||||
desc: null
|
||||
value: oscar
|
||||
ddp_find_unused_parameters:
|
||||
desc: null
|
||||
value: null
|
||||
debug:
|
||||
desc: null
|
||||
value: []
|
||||
deepspeed:
|
||||
desc: null
|
||||
value: null
|
||||
disable_tqdm:
|
||||
desc: null
|
||||
value: false
|
||||
do_eval:
|
||||
desc: null
|
||||
value: true
|
||||
do_predict:
|
||||
desc: null
|
||||
value: false
|
||||
do_train:
|
||||
desc: null
|
||||
value: true
|
||||
dtype:
|
||||
desc: null
|
||||
value: float32
|
||||
eval_accumulation_steps:
|
||||
desc: null
|
||||
value: null
|
||||
eval_steps:
|
||||
desc: null
|
||||
value: 500
|
||||
evaluation_strategy:
|
||||
desc: null
|
||||
value: IntervalStrategy.NO
|
||||
fp16:
|
||||
desc: null
|
||||
value: false
|
||||
fp16_backend:
|
||||
desc: null
|
||||
value: auto
|
||||
fp16_full_eval:
|
||||
desc: null
|
||||
value: false
|
||||
fp16_opt_level:
|
||||
desc: null
|
||||
value: O1
|
||||
gradient_accumulation_steps:
|
||||
desc: null
|
||||
value: 1
|
||||
greater_is_better:
|
||||
desc: null
|
||||
value: null
|
||||
group_by_length:
|
||||
desc: null
|
||||
value: false
|
||||
ignore_data_skip:
|
||||
desc: null
|
||||
value: false
|
||||
label_names:
|
||||
desc: null
|
||||
value: null
|
||||
label_smoothing_factor:
|
||||
desc: null
|
||||
value: 0.0
|
||||
learning_rate:
|
||||
desc: null
|
||||
value: 3.0e-05
|
||||
length_column_name:
|
||||
desc: null
|
||||
value: length
|
||||
load_best_model_at_end:
|
||||
desc: null
|
||||
value: false
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
mp_parameters:
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
desc: null
|
||||
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|
||||
overwrite_cache:
|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
value: 64
|
||||
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|
||||
desc: null
|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
value: false
|
||||
push_to_hub_model_id:
|
||||
desc: null
|
||||
value: gpt-2-tamil
|
||||
push_to_hub_organization:
|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
report_to:
|
||||
desc: null
|
||||
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|
||||
- wandb
|
||||
resume_from_checkpoint:
|
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|
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|
||||
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|
||||
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|
||||
save_on_each_node:
|
||||
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|
||||
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|
||||
save_steps:
|
||||
desc: null
|
||||
value: 2500
|
||||
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|
||||
desc: null
|
||||
value: IntervalStrategy.STEPS
|
||||
save_total_limit:
|
||||
desc: null
|
||||
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|
||||
seed:
|
||||
desc: null
|
||||
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|
||||
sharded_ddp:
|
||||
desc: null
|
||||
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|
||||
skip_memory_metrics:
|
||||
desc: null
|
||||
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|
||||
tokenizer_name:
|
||||
desc: null
|
||||
value: ../gpt-2-tamil/
|
||||
tpu_metrics_debug:
|
||||
desc: null
|
||||
value: false
|
||||
tpu_num_cores:
|
||||
desc: null
|
||||
value: null
|
||||
train_file:
|
||||
desc: null
|
||||
value: null
|
||||
use_fast_tokenizer:
|
||||
desc: null
|
||||
value: true
|
||||
use_legacy_prediction_loop:
|
||||
desc: null
|
||||
value: false
|
||||
validation_file:
|
||||
desc: null
|
||||
value: null
|
||||
validation_split_percentage:
|
||||
desc: null
|
||||
value: 5
|
||||
warmup_ratio:
|
||||
desc: null
|
||||
value: 0.0
|
||||
warmup_steps:
|
||||
desc: null
|
||||
value: 1000
|
||||
weight_decay:
|
||||
desc: null
|
||||
value: 0.01
|
||||
@@ -0,0 +1 @@
|
||||
/home/tweety_abi/GPT2-Tamil/gpt-2-tamil/events.out.tfevents.1626108395.t1v-n-ebe36c53-w-0.486342.3.v2
|
||||
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||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:7d9761e7442f5b6b99224ee68ee38f3b7e486ead79f4e390bf7e258dc16de973
|
||||
size 4407657
|
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@@ -0,0 +1,123 @@
|
||||
absl-py==0.13.0
|
||||
aiohttp==3.7.4.post0
|
||||
appdirs==1.4.4
|
||||
astunparse==1.6.3
|
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async-timeout==3.0.1
|
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attrs==21.2.0
|
||||
backcall==0.2.0
|
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black==21.6b0
|
||||
cachetools==4.2.2
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||||
certifi==2021.5.30
|
||||
cfgv==3.3.0
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||||
chardet==4.0.0
|
||||
chex==0.0.7
|
||||
click==8.0.1
|
||||
configparser==5.0.2
|
||||
cycler==0.10.0
|
||||
datasets==1.8.1.dev0
|
||||
decorator==5.0.9
|
||||
dill==0.3.4
|
||||
distlib==0.3.2
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||||
dm-tree==0.1.6
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||||
docker-pycreds==0.4.0
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filelock==3.0.12
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flake8==3.9.2
|
||||
flatbuffers==1.12
|
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flax==0.3.4
|
||||
fsspec==2021.6.1
|
||||
gast==0.4.0
|
||||
gitdb==4.0.7
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gitpython==3.1.18
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google-auth-oauthlib==0.4.4
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google-auth==1.32.1
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google-pasta==0.2.0
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grpcio==1.34.1
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h5py==3.1.0
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huggingface-hub==0.0.12
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identify==2.2.10
|
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idna==2.10
|
||||
ipython-genutils==0.2.0
|
||||
ipython==7.25.0
|
||||
isort==5.9.1
|
||||
jax==0.2.16
|
||||
jaxlib==0.1.68
|
||||
jedi==0.18.0
|
||||
joblib==1.0.1
|
||||
keras-nightly==2.5.0.dev2021032900
|
||||
keras-preprocessing==1.1.2
|
||||
kiwisolver==1.3.1
|
||||
libtpu-nightly==0.1.dev20210615
|
||||
markdown==3.3.4
|
||||
matplotlib-inline==0.1.2
|
||||
matplotlib==3.4.2
|
||||
mccabe==0.6.1
|
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msgpack==1.0.2
|
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multidict==5.1.0
|
||||
multiprocess==0.70.12.2
|
||||
mypy-extensions==0.4.3
|
||||
nodeenv==1.6.0
|
||||
numpy==1.19.5
|
||||
oauthlib==3.1.1
|
||||
opt-einsum==3.3.0
|
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optax==0.0.8
|
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packaging==20.9
|
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pandas==1.2.5
|
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parso==0.8.2
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pathspec==0.8.1
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pathtools==0.1.2
|
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pexpect==4.8.0
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pickleshare==0.7.5
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pillow==8.3.0
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pip==20.0.2
|
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pkg-resources==0.0.0
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pre-commit==2.13.0
|
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promise==2.3
|
||||
prompt-toolkit==3.0.19
|
||||
protobuf==3.17.3
|
||||
psutil==5.8.0
|
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ptyprocess==0.7.0
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pyarrow==4.0.1
|
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pyasn1-modules==0.2.8
|
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pyasn1==0.4.8
|
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pycodestyle==2.7.0
|
||||
pyflakes==2.3.1
|
||||
pygments==2.9.0
|
||||
pyparsing==2.4.7
|
||||
python-dateutil==2.8.1
|
||||
pytz==2021.1
|
||||
pyyaml==5.4.1
|
||||
regex==2021.7.1
|
||||
requests-oauthlib==1.3.0
|
||||
requests==2.25.1
|
||||
rsa==4.7.2
|
||||
sacremoses==0.0.45
|
||||
scipy==1.7.0
|
||||
sentry-sdk==1.3.0
|
||||
setuptools==44.0.0
|
||||
shortuuid==1.0.1
|
||||
six==1.15.0
|
||||
smmap==4.0.0
|
||||
subprocess32==3.5.4
|
||||
tensorboard-data-server==0.6.1
|
||||
tensorboard-plugin-wit==1.8.0
|
||||
tensorboard==2.5.0
|
||||
tensorflow-estimator==2.5.0
|
||||
tensorflow==2.5.0
|
||||
termcolor==1.1.0
|
||||
tokenizers==0.10.3
|
||||
toml==0.10.2
|
||||
toolz==0.11.1
|
||||
torch==1.9.0
|
||||
tqdm==4.61.1
|
||||
traitlets==5.0.5
|
||||
transformers==4.9.0.dev0
|
||||
typing-extensions==3.7.4.3
|
||||
urllib3==1.26.6
|
||||
virtualenv==20.4.7
|
||||
wandb==0.10.33
|
||||
wcwidth==0.2.5
|
||||
werkzeug==2.0.1
|
||||
wheel==0.36.2
|
||||
wrapt==1.12.1
|
||||
xxhash==2.0.2
|
||||
yarl==1.6.3
|
||||
@@ -0,0 +1,49 @@
|
||||
{
|
||||
"os": "Linux-5.4.0-1043-gcp-x86_64-with-glibc2.29",
|
||||
"python": "3.8.10",
|
||||
"heartbeatAt": "2021-07-12T16:46:35.350252",
|
||||
"startedAt": "2021-07-12T16:46:33.416306",
|
||||
"docker": null,
|
||||
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|
||||
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|
||||
"args": [
|
||||
"--output_dir=../gpt-2-tamil/",
|
||||
"--model_type=gpt2",
|
||||
"--config_name=../gpt-2-tamil/",
|
||||
"--tokenizer_name=../gpt-2-tamil/",
|
||||
"--dataset_name=oscar",
|
||||
"--dataset_config_name=unshuffled_deduplicated_ta",
|
||||
"--do_train",
|
||||
"--do_eval",
|
||||
"--block_size=512",
|
||||
"--per_device_train_batch_size=64",
|
||||
"--per_device_eval_batch_size=64",
|
||||
"--learning_rate=3e-5",
|
||||
"--warmup_steps=1000",
|
||||
"--adam_beta1=0.9",
|
||||
"--adam_beta2=0.98",
|
||||
"--weight_decay=0.01",
|
||||
"--overwrite_output_dir",
|
||||
"--num_train_epochs=25",
|
||||
"--report_to",
|
||||
"wandb",
|
||||
"--run_name",
|
||||
"trial",
|
||||
"--logging_steps=500",
|
||||
"--save_steps=2500",
|
||||
"--eval_steps=2500",
|
||||
"--preprocessing_num_workers=90"
|
||||
],
|
||||
"state": "running",
|
||||
"program": "../src/run_clm_flax.py",
|
||||
"codePath": "src/run_clm_flax.py",
|
||||
"git": {
|
||||
"remote": "https://github.com/AbinayaM02/GPT2-Tamil.git",
|
||||
"commit": "5d59c6a635e952a0f51ef33ed713960a04e9dcb6"
|
||||
},
|
||||
"email": "abinaya.m02@mphasis.com",
|
||||
"root": "/home/tweety_abi/GPT2-Tamil",
|
||||
"host": "t1v-n-ebe36c53-w-0",
|
||||
"username": "tweety_abi",
|
||||
"executable": "/home/tweety_abi/gpt2_env/bin/python"
|
||||
}
|
||||
@@ -0,0 +1 @@
|
||||
{"global_step": 132500, "_timestamp": 1626248099.379086, "train_time": 743654.875, "train_learning_rate": 1.1402963906448349e-08, "_step": 264206, "train_loss": 1.1299134492874146, "eval_loss": 1.1545542478561401, "eval_perplexity": 3.1726088523864746}
|
||||
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:748fffc8fe7bbd39d404a1bae61d5711a3e098491142dc28b16d5d75e32dc937
|
||||
size 97283434
|
||||
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|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:36e11410ff19af1db092231a1450397dffb80ef21248540b6d372dcf5606559c
|
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size 8797
|
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version https://git-lfs.github.com/spec/v1
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oid sha256:d8211487b4d0a0489ae4728120abad1be7ee4190520afc47fdae166087ae6068
|
||||
size 60817322
|
||||
4
src/convert_flax_to_pytorch.py
Normal file
4
src/convert_flax_to_pytorch.py
Normal file
@@ -0,0 +1,4 @@
|
||||
from transformers import GPT2LMHeadModel
|
||||
|
||||
model = GPT2LMHeadModel.from_pretrained("../gpt-2-tamil", from_flax=True)
|
||||
model.save_pretrained("../")
|
||||
8
src/create_config.py
Normal file
8
src/create_config.py
Normal file
@@ -0,0 +1,8 @@
|
||||
from transformers import GPT2Config
|
||||
|
||||
model_dir = "../gpt-2-tamil" # ${MODEL_DIR}
|
||||
|
||||
config = GPT2Config.from_pretrained(
|
||||
"gpt2", resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0
|
||||
)
|
||||
config.save_pretrained(model_dir)
|
||||
651
src/run_clm_flax.py
Executable file
651
src/run_clm_flax.py
Executable file
@@ -0,0 +1,651 @@
|
||||
#!/usr/bin/env python
|
||||
# coding=utf-8
|
||||
# Copyright 2021 The HuggingFace Team All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""
|
||||
Pre-training/Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
|
||||
|
||||
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
|
||||
https://huggingface.co/models?filter=causal-lm
|
||||
"""
|
||||
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
|
||||
|
||||
import logging
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from dataclasses import dataclass, field
|
||||
from pathlib import Path
|
||||
from typing import Callable, Optional
|
||||
|
||||
import datasets
|
||||
from datasets import Dataset, load_dataset
|
||||
from tqdm import tqdm
|
||||
|
||||
import jax
|
||||
import jax.numpy as jnp
|
||||
import optax
|
||||
import transformers
|
||||
import wandb
|
||||
from flax import jax_utils, traverse_util
|
||||
from flax.jax_utils import unreplicate
|
||||
from flax.training import train_state
|
||||
from flax.training.common_utils import get_metrics, onehot, shard, shard_prng_key
|
||||
from transformers import (
|
||||
CONFIG_MAPPING,
|
||||
FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
|
||||
AutoConfig,
|
||||
AutoTokenizer,
|
||||
FlaxAutoModelForCausalLM,
|
||||
HfArgumentParser,
|
||||
TrainingArguments,
|
||||
is_tensorboard_available,
|
||||
)
|
||||
from transformers.testing_utils import CaptureLogger
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
|
||||
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
|
||||
|
||||
|
||||
@dataclass
|
||||
class ModelArguments:
|
||||
"""
|
||||
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
|
||||
"""
|
||||
|
||||
model_name_or_path: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The model checkpoint for weights initialization."
|
||||
"Don't set if you want to train a model from scratch."
|
||||
},
|
||||
)
|
||||
model_type: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
|
||||
)
|
||||
config_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
|
||||
)
|
||||
tokenizer_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
|
||||
)
|
||||
cache_dir: Optional[str] = field(
|
||||
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}
|
||||
)
|
||||
use_fast_tokenizer: bool = field(
|
||||
default=True,
|
||||
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
|
||||
)
|
||||
dtype: Optional[str] = field(
|
||||
default="float32",
|
||||
metadata={
|
||||
"help": "Floating-point format in which the model weights should be initialized and trained. Choose one of `[float32, float16, bfloat16]`."
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
class DataTrainingArguments:
|
||||
"""
|
||||
Arguments pertaining to what data we are going to input our model for training and eval.
|
||||
"""
|
||||
|
||||
dataset_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
||||
)
|
||||
dataset_config_name: Optional[str] = field(
|
||||
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
||||
)
|
||||
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
|
||||
validation_file: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
|
||||
)
|
||||
max_train_samples: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
|
||||
"value if set."
|
||||
},
|
||||
)
|
||||
max_eval_samples: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
||||
"value if set."
|
||||
},
|
||||
)
|
||||
overwrite_cache: bool = field(
|
||||
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
||||
)
|
||||
validation_split_percentage: Optional[int] = field(
|
||||
default=5,
|
||||
metadata={
|
||||
"help": "The percentage of the train set used as validation set in case there's no validation split"
|
||||
},
|
||||
)
|
||||
block_size: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "Optional input sequence length after tokenization. "
|
||||
"The training dataset will be truncated in block of this size for training. "
|
||||
"Default to the model max input length for single sentence inputs (take into account special tokens)."
|
||||
},
|
||||
)
|
||||
overwrite_cache: bool = field(
|
||||
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
||||
)
|
||||
preprocessing_num_workers: Optional[int] = field(
|
||||
default=None,
|
||||
metadata={"help": "The number of processes to use for the preprocessing."},
|
||||
)
|
||||
|
||||
def __post_init__(self):
|
||||
if self.dataset_name is None and self.train_file is None and self.validation_file is None:
|
||||
raise ValueError("Need either a dataset name or a training/validation file.")
|
||||
else:
|
||||
if self.train_file is not None:
|
||||
extension = self.train_file.split(".")[-1]
|
||||
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
|
||||
if self.validation_file is not None:
|
||||
extension = self.validation_file.split(".")[-1]
|
||||
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
|
||||
|
||||
|
||||
class TrainState(train_state.TrainState):
|
||||
dropout_rng: jnp.ndarray
|
||||
|
||||
def replicate(self):
|
||||
return jax_utils.replicate(self).replace(dropout_rng=shard_prng_key(self.dropout_rng))
|
||||
|
||||
|
||||
def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuffle: bool = False):
|
||||
"""
|
||||
Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
|
||||
Shuffle batches if `shuffle` is `True`.
|
||||
"""
|
||||
steps_per_epoch = len(dataset) // batch_size
|
||||
|
||||
if shuffle:
|
||||
batch_idx = jax.random.permutation(rng, len(dataset))
|
||||
else:
|
||||
batch_idx = jnp.arange(len(dataset))
|
||||
|
||||
batch_idx = batch_idx[: steps_per_epoch * batch_size] # Skip incomplete batch.
|
||||
batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
|
||||
|
||||
for idx in batch_idx:
|
||||
batch = dataset[idx]
|
||||
batch = {k: jnp.array(v) for k, v in batch.items()}
|
||||
|
||||
batch = shard(batch)
|
||||
|
||||
yield batch
|
||||
|
||||
|
||||
def write_train_metric(summary_writer, train_metrics, train_time, step):
|
||||
summary_writer.scalar("train_time", train_time, step)
|
||||
|
||||
train_metrics = get_metrics(train_metrics)
|
||||
for key, vals in train_metrics.items():
|
||||
tag = f"train_{key}"
|
||||
for i, val in enumerate(vals):
|
||||
summary_writer.scalar(tag, val, step - len(vals) + i + 1)
|
||||
|
||||
|
||||
def write_eval_metric(summary_writer, eval_metrics, step):
|
||||
for metric_name, value in eval_metrics.items():
|
||||
summary_writer.scalar(f"eval_{metric_name}", value, step)
|
||||
|
||||
|
||||
def create_learning_rate_fn(
|
||||
train_ds_size: int, train_batch_size: int, num_train_epochs: int, num_warmup_steps: int, learning_rate: float
|
||||
) -> Callable[[int], jnp.array]:
|
||||
"""Returns a linear warmup, linear_decay learning rate function."""
|
||||
steps_per_epoch = train_ds_size // train_batch_size
|
||||
num_train_steps = steps_per_epoch * num_train_epochs
|
||||
warmup_fn = optax.linear_schedule(init_value=0.0, end_value=learning_rate, transition_steps=num_warmup_steps)
|
||||
decay_fn = optax.linear_schedule(
|
||||
init_value=learning_rate, end_value=0, transition_steps=num_train_steps - num_warmup_steps
|
||||
)
|
||||
schedule_fn = optax.join_schedules(schedules=[warmup_fn, decay_fn], boundaries=[num_warmup_steps])
|
||||
return schedule_fn
|
||||
|
||||
|
||||
def main():
|
||||
# See all possible arguments in src/transformers/training_args.py
|
||||
# or by passing the --help flag to this script.
|
||||
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
||||
|
||||
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
|
||||
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
||||
# If we pass only one argument to the script and it's the path to a json file,
|
||||
# let's parse it to get our arguments.
|
||||
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
||||
else:
|
||||
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
||||
|
||||
if (
|
||||
os.path.exists(training_args.output_dir)
|
||||
and os.listdir(training_args.output_dir)
|
||||
and training_args.do_train
|
||||
and not training_args.overwrite_output_dir
|
||||
):
|
||||
raise ValueError(
|
||||
f"Output directory ({training_args.output_dir}) already exists and is not empty."
|
||||
"Use --overwrite_output_dir to overcome."
|
||||
)
|
||||
|
||||
# Make one log on every process with the configuration for debugging.
|
||||
logging.basicConfig(
|
||||
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
||||
datefmt="%m/%d/%Y %H:%M:%S",
|
||||
level=logging.INFO,
|
||||
)
|
||||
# Setup logging, we only want one process per machine to log things on the screen.
|
||||
logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
|
||||
if jax.process_index() == 0:
|
||||
datasets.utils.logging.set_verbosity_warning()
|
||||
transformers.utils.logging.set_verbosity_info()
|
||||
else:
|
||||
datasets.utils.logging.set_verbosity_error()
|
||||
transformers.utils.logging.set_verbosity_error()
|
||||
|
||||
# Set the verbosity to info of the Transformers logger (on main process only):
|
||||
logger.info(f"Training/evaluation parameters {training_args}")
|
||||
|
||||
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
|
||||
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
|
||||
# (the dataset will be downloaded automatically from the datasets Hub).
|
||||
#
|
||||
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
|
||||
# 'text' is found. You can easily tweak this behavior (see below).
|
||||
#
|
||||
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
|
||||
# download the dataset.
|
||||
if data_args.dataset_name is not None:
|
||||
# Downloading and loading a dataset from the hub.
|
||||
dataset = load_dataset(
|
||||
data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir, keep_in_memory=False
|
||||
)
|
||||
|
||||
if "validation" not in dataset.keys():
|
||||
dataset["validation"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=f"train[:{data_args.validation_split_percentage}%]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
)
|
||||
dataset["train"] = load_dataset(
|
||||
data_args.dataset_name,
|
||||
data_args.dataset_config_name,
|
||||
split=f"train[{data_args.validation_split_percentage}%:]",
|
||||
cache_dir=model_args.cache_dir,
|
||||
)
|
||||
else:
|
||||
data_files = {}
|
||||
if data_args.train_file is not None:
|
||||
data_files["train"] = data_args.train_file
|
||||
if data_args.validation_file is not None:
|
||||
data_files["validation"] = data_args.validation_file
|
||||
extension = data_args.train_file.split(".")[-1]
|
||||
if extension == "txt":
|
||||
extension = "text"
|
||||
dataset = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
|
||||
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
|
||||
# https://huggingface.co/docs/datasets/loading_datasets.html.
|
||||
|
||||
# Load pretrained model and tokenizer
|
||||
|
||||
# Distributed training:
|
||||
# The .from_pretrained methods guarantee that only one local process can concurrently
|
||||
# download model & vocab.
|
||||
if model_args.config_name:
|
||||
config = AutoConfig.from_pretrained(model_args.config_name, cache_dir=model_args.cache_dir)
|
||||
elif model_args.model_name_or_path:
|
||||
config = AutoConfig.from_pretrained(model_args.model_name_or_path, cache_dir=model_args.cache_dir)
|
||||
else:
|
||||
config = CONFIG_MAPPING[model_args.model_type]()
|
||||
logger.warning("You are instantiating a new config instance from scratch.")
|
||||
|
||||
if model_args.tokenizer_name:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.tokenizer_name, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
)
|
||||
elif model_args.model_name_or_path:
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
model_args.model_name_or_path, cache_dir=model_args.cache_dir, use_fast=model_args.use_fast_tokenizer
|
||||
)
|
||||
else:
|
||||
raise ValueError(
|
||||
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
|
||||
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
|
||||
)
|
||||
|
||||
if model_args.model_name_or_path:
|
||||
model = FlaxAutoModelForCausalLM.from_pretrained(
|
||||
model_args.model_name_or_path, config=config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
||||
)
|
||||
else:
|
||||
model = FlaxAutoModelForCausalLM.from_config(
|
||||
config, seed=training_args.seed, dtype=getattr(jnp, model_args.dtype)
|
||||
)
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# First we tokenize all the texts.
|
||||
if training_args.do_train:
|
||||
column_names = dataset["train"].column_names
|
||||
else:
|
||||
column_names = dataset["validation"].column_names
|
||||
text_column_name = "text" if "text" in column_names else column_names[0]
|
||||
|
||||
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
|
||||
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
|
||||
|
||||
def tokenize_function(examples):
|
||||
with CaptureLogger(tok_logger) as cl:
|
||||
output = tokenizer(examples[text_column_name])
|
||||
# clm input could be much much longer than block_size
|
||||
if "Token indices sequence length is longer than the" in cl.out:
|
||||
tok_logger.warning(
|
||||
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits before being passed to the model."
|
||||
)
|
||||
return output
|
||||
|
||||
tokenized_datasets = dataset.map(
|
||||
tokenize_function,
|
||||
batched=True,
|
||||
num_proc=data_args.preprocessing_num_workers,
|
||||
remove_columns=column_names,
|
||||
load_from_cache_file=not data_args.overwrite_cache,
|
||||
)
|
||||
|
||||
if data_args.block_size is None:
|
||||
block_size = tokenizer.model_max_length
|
||||
if block_size > config.max_position_embeddings:
|
||||
logger.warning(
|
||||
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
|
||||
"Picking 1024 instead. You can change that default value by passing --block_size xxx."
|
||||
)
|
||||
block_size = 1024
|
||||
else:
|
||||
if data_args.block_size > tokenizer.model_max_length:
|
||||
logger.warning(
|
||||
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
|
||||
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
|
||||
)
|
||||
block_size = min(data_args.block_size, tokenizer.model_max_length)
|
||||
|
||||
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
|
||||
def group_texts(examples):
|
||||
# Concatenate all texts.
|
||||
concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
|
||||
total_length = len(concatenated_examples[list(examples.keys())[0]])
|
||||
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
|
||||
# customize this part to your needs.
|
||||
if total_length >= block_size:
|
||||
total_length = (total_length // block_size) * block_size
|
||||
# Split by chunks of max_len.
|
||||
result = {
|
||||
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
|
||||
for k, t in concatenated_examples.items()
|
||||
}
|
||||
result["labels"] = result["input_ids"].copy()
|
||||
return result
|
||||
|
||||
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
|
||||
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
|
||||
# to preprocess.
|
||||
#
|
||||
# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
|
||||
# https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map
|
||||
|
||||
lm_datasets = tokenized_datasets.map(
|
||||
group_texts,
|
||||
batched=True,
|
||||
num_proc=data_args.preprocessing_num_workers,
|
||||
load_from_cache_file=not data_args.overwrite_cache,
|
||||
)
|
||||
|
||||
if training_args.do_train:
|
||||
if "train" not in tokenized_datasets:
|
||||
raise ValueError("--do_train requires a train dataset")
|
||||
train_dataset = lm_datasets["train"]
|
||||
if data_args.max_train_samples is not None:
|
||||
train_dataset = train_dataset.select(range(data_args.max_train_samples))
|
||||
|
||||
if training_args.do_eval:
|
||||
if "validation" not in tokenized_datasets:
|
||||
raise ValueError("--do_eval requires a validation dataset")
|
||||
eval_dataset = lm_datasets["validation"]
|
||||
if data_args.max_eval_samples is not None:
|
||||
eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
|
||||
|
||||
# Enable tensorboard only on the master node
|
||||
has_tensorboard = is_tensorboard_available()
|
||||
if has_tensorboard and jax.process_index() == 0:
|
||||
wandb.init(
|
||||
entity='abinayam',
|
||||
project='hf-flax-gpt-2-tamil',
|
||||
sync_tensorboard=True
|
||||
)
|
||||
|
||||
wandb.config.update(training_args) # optional, log your configs
|
||||
wandb.config.update(model_args) # optional, log your configs
|
||||
wandb.config.update(data_args) # optional, log your configs
|
||||
|
||||
try:
|
||||
from flax.metrics.tensorboard import SummaryWriter
|
||||
|
||||
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir))
|
||||
except ImportError as ie:
|
||||
has_tensorboard = False
|
||||
logger.warning(
|
||||
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}"
|
||||
)
|
||||
else:
|
||||
logger.warning(
|
||||
"Unable to display metrics through TensorBoard because the package is not installed: "
|
||||
"Please run pip install tensorboard to enable."
|
||||
)
|
||||
|
||||
# Initialize our training
|
||||
rng = jax.random.PRNGKey(training_args.seed)
|
||||
rng, dropout_rng = jax.random.split(rng)
|
||||
|
||||
# Store some constant
|
||||
num_epochs = int(training_args.num_train_epochs)
|
||||
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count()
|
||||
eval_batch_size = int(training_args.per_device_eval_batch_size) * jax.device_count()
|
||||
steps_per_epoch = len(train_dataset) // train_batch_size
|
||||
total_train_steps = steps_per_epoch * num_epochs
|
||||
|
||||
# Create learning rate schedule
|
||||
linear_decay_lr_schedule_fn = create_learning_rate_fn(
|
||||
len(train_dataset),
|
||||
train_batch_size,
|
||||
training_args.num_train_epochs,
|
||||
training_args.warmup_steps,
|
||||
training_args.learning_rate,
|
||||
)
|
||||
|
||||
# We use Optax's "masking" functionality to not apply weight decay
|
||||
# to bias and LayerNorm scale parameters. decay_mask_fn returns a
|
||||
# mask boolean with the same structure as the parameters.
|
||||
# The mask is True for parameters that should be decayed.
|
||||
# Note that this mask is specifically adapted for FlaxGPT2.
|
||||
# For other models, one should correct the layer norm parameter naming
|
||||
# accordingly.
|
||||
def decay_mask_fn(params):
|
||||
flat_params = traverse_util.flatten_dict(params)
|
||||
flat_mask = {
|
||||
path: (path[-1] != "bias" and path[-2:] not in [("ln_1", "scale"), ("ln_2", "scale"), ("ln_f", "scale")])
|
||||
for path in flat_params
|
||||
}
|
||||
return traverse_util.unflatten_dict(flat_mask)
|
||||
|
||||
# create adam optimizer
|
||||
if training_args.adafactor:
|
||||
# We use the default parameters here to initialize adafactor,
|
||||
# For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
|
||||
optimizer = optax.adafactor(
|
||||
learning_rate=linear_decay_lr_schedule_fn,
|
||||
)
|
||||
else:
|
||||
optimizer = optax.adamw(
|
||||
learning_rate=linear_decay_lr_schedule_fn,
|
||||
b1=training_args.adam_beta1,
|
||||
b2=training_args.adam_beta2,
|
||||
eps=training_args.adam_epsilon,
|
||||
weight_decay=training_args.weight_decay,
|
||||
mask=decay_mask_fn,
|
||||
)
|
||||
|
||||
# Setup train state
|
||||
state = TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer, dropout_rng=dropout_rng)
|
||||
|
||||
def loss_fn(logits, labels):
|
||||
shift_logits = logits[..., :-1, :]
|
||||
shift_labels = labels[..., 1:]
|
||||
loss = optax.softmax_cross_entropy(shift_logits, onehot(shift_labels, shift_logits.shape[-1]))
|
||||
return loss.mean()
|
||||
|
||||
# Define gradient update step fn
|
||||
def train_step(state, batch):
|
||||
dropout_rng, new_dropout_rng = jax.random.split(state.dropout_rng)
|
||||
|
||||
def compute_loss(params):
|
||||
labels = batch.pop("labels")
|
||||
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
|
||||
loss = loss_fn(logits, labels)
|
||||
return loss
|
||||
|
||||
grad_fn = jax.value_and_grad(compute_loss)
|
||||
loss, grad = grad_fn(state.params)
|
||||
grad = jax.lax.pmean(grad, "batch")
|
||||
|
||||
new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
|
||||
|
||||
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
|
||||
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
||||
|
||||
return new_state, metrics
|
||||
|
||||
# Define eval fn
|
||||
def eval_step(params, batch):
|
||||
labels = batch.pop("labels")
|
||||
logits = model(**batch, params=params, train=False)[0]
|
||||
loss = loss_fn(logits, labels)
|
||||
|
||||
# summarize metrics
|
||||
metrics = {"loss": loss}
|
||||
metrics = jax.lax.pmean(metrics, axis_name="batch")
|
||||
return metrics
|
||||
|
||||
# Create parallel version of the train and eval step
|
||||
p_train_step = jax.pmap(train_step, "batch", donate_argnums=(0,))
|
||||
p_eval_step = jax.pmap(eval_step, "batch")
|
||||
|
||||
# Replicate the train state on each device
|
||||
state = state.replicate()
|
||||
|
||||
logger.info("***** Running training *****")
|
||||
logger.info(f" Num examples = {len(train_dataset)}")
|
||||
logger.info(f" Num Epochs = {num_epochs}")
|
||||
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
|
||||
logger.info(f" Total train batch size (w. parallel & distributed) = {train_batch_size}")
|
||||
logger.info(f" Total optimization steps = {total_train_steps}")
|
||||
|
||||
train_time = 0
|
||||
train_metrics = []
|
||||
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
|
||||
for epoch in epochs:
|
||||
# ======================== Training ================================
|
||||
train_start = time.time()
|
||||
|
||||
# Create sampling rng
|
||||
rng, input_rng = jax.random.split(rng)
|
||||
|
||||
# Generate an epoch by shuffling sampling indices from the train dataset
|
||||
train_loader = data_loader(input_rng, train_dataset, train_batch_size, shuffle=True)
|
||||
steps_per_epoch = len(train_dataset) // train_batch_size
|
||||
# train
|
||||
for step in tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False):
|
||||
batch = next(train_loader)
|
||||
state, train_metric = p_train_step(state, batch)
|
||||
train_metrics.append(train_metric)
|
||||
|
||||
cur_step = epoch * (len(train_dataset) // train_batch_size) + step
|
||||
|
||||
if cur_step % training_args.logging_steps == 0 and cur_step > 0:
|
||||
# Save metrics
|
||||
train_metric = unreplicate(train_metric)
|
||||
train_time += time.time() - train_start
|
||||
if has_tensorboard and jax.process_index() == 0:
|
||||
write_train_metric(summary_writer, train_metrics, train_time, cur_step)
|
||||
|
||||
epochs.write(
|
||||
f"Step... ({cur_step} | Loss: {train_metric['loss'].mean()}, Learning Rate: {train_metric['learning_rate'].mean()})"
|
||||
)
|
||||
|
||||
train_metrics = []
|
||||
|
||||
if cur_step % training_args.eval_steps == 0 and cur_step > 0:
|
||||
# ======================== Evaluating ==============================
|
||||
eval_metrics = []
|
||||
eval_loader = data_loader(input_rng, eval_dataset, eval_batch_size)
|
||||
eval_steps = len(eval_dataset) // eval_batch_size
|
||||
for _ in tqdm(range(eval_steps), desc="Evaluating...", position=2, leave=False):
|
||||
# Model forward
|
||||
batch = next(eval_loader)
|
||||
metrics = p_eval_step(state.params, batch)
|
||||
eval_metrics.append(metrics)
|
||||
|
||||
# normalize eval metrics
|
||||
eval_metrics = get_metrics(eval_metrics)
|
||||
eval_metrics = jax.tree_map(jnp.mean, eval_metrics)
|
||||
|
||||
try:
|
||||
eval_metrics["perplexity"] = math.exp(eval_metrics["loss"])
|
||||
except OverflowError:
|
||||
eval_metrics["perplexity"] = float("inf")
|
||||
|
||||
# Print metrics and update progress bar
|
||||
desc = f"Step... ({cur_step} | Eval Loss: {eval_metrics['loss']} | Eval Perplexity: {eval_metrics['perplexity']})"
|
||||
epochs.write(desc)
|
||||
epochs.desc = desc
|
||||
|
||||
# Save metrics
|
||||
if has_tensorboard and jax.process_index() == 0:
|
||||
write_eval_metric(summary_writer, eval_metrics, cur_step)
|
||||
|
||||
if cur_step % training_args.save_steps == 0 and cur_step > 0:
|
||||
# save checkpoint after each epoch and push checkpoint to the hub
|
||||
if jax.process_index() == 0:
|
||||
params = jax.device_get(unreplicate(state.params))
|
||||
model.save_pretrained(
|
||||
training_args.output_dir,
|
||||
params=params,
|
||||
push_to_hub=training_args.push_to_hub,
|
||||
commit_message=f"Saving weights and logs of step {cur_step}",
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
33
src/train_tokenizer.py
Normal file
33
src/train_tokenizer.py
Normal file
@@ -0,0 +1,33 @@
|
||||
from datasets import load_dataset
|
||||
from tokenizers import ByteLevelBPETokenizer # Tokenizer, normalizers, trainers
|
||||
|
||||
model_dir = "../gpt-2-tamil" # ${MODEL_DIR}
|
||||
|
||||
# load dataset
|
||||
dataset = load_dataset("oscar", "unshuffled_deduplicated_ta", split="train")
|
||||
|
||||
# Instantiate tokenizer
|
||||
tokenizer = ByteLevelBPETokenizer()
|
||||
|
||||
|
||||
def batch_iterator(batch_size=1000):
|
||||
for i in range(0, len(dataset), batch_size):
|
||||
yield dataset[i : i + batch_size]["text"]
|
||||
|
||||
|
||||
# Customized training
|
||||
tokenizer.train_from_iterator(
|
||||
batch_iterator(),
|
||||
vocab_size=50265,
|
||||
min_frequency=2,
|
||||
special_tokens=[
|
||||
"<s>",
|
||||
"<pad>",
|
||||
"</s>",
|
||||
"<unk>",
|
||||
"<mask>",
|
||||
],
|
||||
)
|
||||
|
||||
# Save files to disk
|
||||
tokenizer.save(f"{model_dir}/tokenizer.json")
|
||||
1
tokenizer.json
Normal file
1
tokenizer.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user