初始化项目，由ModelHub XC社区提供模型

Model: abinayam/gpt-2-tamil
Source: Original Platform

.flake8

@@ -0,0 +1,8 @@
+[flake8]
+exclude = venv
+ignore = E501, W503, E226, E203
+max-line-length = 85
+
+# E501: Line too long
+# W503: Line break occurred before binary operator
+# E226: Missing white space around arithmetic operator

.gitattributes

@@ -0,0 +1,19 @@
+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.log filter=lfs diff=lfs merge=lfs -text
+*.wandb filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+model.safetensors filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,129 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/

.pre-commit-config.yaml

@@ -0,0 +1,28 @@
+# See https://pre-commit.com for more information
+# See https://pre-commit.com/hooks.html for more hooks
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v3.4.0
+    hooks:
+    -   id: trailing-whitespace
+    -   id: check-yaml
+    -   id: check-ast
+    -   id: check-json
+    -   id: check-merge-conflict
+    -   id: detect-private-key
+-   repo: https://github.com/psf/black
+    rev: 21.6b0
+    hooks:
+    -   id: black
+        args: []
+        files: .
+-   repo: https://gitlab.com/PyCQA/flake8
+    rev: 3.9.2
+    hooks:
+    -   id: flake8
+-   repo: https://github.com/PyCQA/isort
+    rev: 5.9.1
+    hooks:
+    -   id: isort
+        args: []
+        files: .

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 Abinaya Mahendiran
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -0,0 +1,69 @@
+---
+
+language: ta
+datasets:
+- oscar
+- IndicNLP
+widget:
+- text: 'ஒரு ஊரிலே ஒரு காக்கைக்கு'
+
+---
+# GPT2-Tamil
+
+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. 
+
+## Setup:
+To setup the project, run the following command,
+```python
+pip install -r requirements.txt
+```
+
+## Model:
+Pretrained model on Tamil language using a causal language modeling (CLM) objective.
+ 
+## Dataset Used:
+The GTP-2 model is trained on [oscar dataset - ta](https://huggingface.co/datasets/oscar) and [IndicNLP dataset - ta](https://indicnlp.ai4bharat.org/corpora/)
+
+## Intended uses & limitations:
+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=gpt2) to look for fine-tuned versions on a task that interests you.
+
+## How to pretrain the model:
+To perform training, do the following steps,
+
+- Export the model directory (where you want to store the model artifacts like config, tokenizer, etc.)
+```python
+>>> export MODEL_DIR=<model_dir>
+```
+- Create the config.json by running the following command,
+```python
+>>> python src/create_config.py
+```
+- Create the tokenizer by running the following command,
+```python
+>>> python src/train_tokenizer.py
+```
+- Once the config and tokenizer is created, run the following script to start training the flax model
+```python
+>>> python scripts/train_gpt2-oscar-tamil.sh
+```
+
+## How to use:
+To perform language generation using the model, pipeline can be used directly.
+
+- First convert the flax model to pytorch using the following command,
+```python
+python src/convert_flax_to_pytorch.py
+```
+- Use the following snippet to perform language generation,
+```python
+ >>> from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
+ >>> model_name = 'abinayam/gpt-2-tamil'
+ >>> model = AutoModelWithLMHead.from_pretrained(model_name)
+ >>> tokenizer = AutoTokenizer.from_pretrained(model_name)
+ >>> set_seed(42)
+ >>> input_text = "ஒரு ஊரிலே ஒரு காக்கைக்கு"
+ >>> max_len = 300
+ >>> no_seq = 5
+ >>> generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+ >>> sequence = generator(input_text, max_length=max_len, num_return_sequences=no_seq)
+```

config.json

@@ -0,0 +1,38 @@
+{
+  "_name_or_path": "../gpt-2-tamil",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "resid_pdrop": 0.0,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 300
+    }
+  },
+  "torch_dtype": "float32",
+  "transformers_version": "4.9.0.dev0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

dataset/README.md

@@ -0,0 +1 @@
+Details of the dataset can go here. The folder can also contain dataset (downloaded locally).

demo/README.md

@@ -0,0 +1 @@
+Streamlit demo can go here.

demo/tamil_generator.py

@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+import locale
+print(locale.getpreferredencoding())
+
+
+from transformers import AutoConfig, AutoModelForCausalLM,pipeline,AutoTokenizer
+from datasets import load_dataset
+
+MODEL_DIR = "/home/deepak/sources/gpt2-tamil/gpt2-tamil/"
+
+
+
+
+
+#get prompt from dataset, will be replaced by manual prompt once I figure out how to render tamil font
+dataset = load_dataset("oscar", "unshuffled_deduplicated_ta", split="train")
+id =232
+print(dataset[id]['text'])
+tamil_prompt =dataset[id]['text']
+
+# Get configuration and the model
+config = AutoConfig.from_pretrained(MODEL_DIR)
+model = AutoModelForCausalLM.from_config(config)
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
+
+
+generator= pipeline('text-generation', model=model, tokenizer=tokenizer)
+model_output = generator(tamil_prompt, max_length=30, num_return_sequences=5)
+print(model_output)
+

gpt-2-tamil/config.json

@@ -0,0 +1,36 @@
+{
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "resid_pdrop": 0.0,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "transformers_version": "4.9.0.dev0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

gpt-2-tamil/events.out.tfevents.1626336540.t1v-n-ebe36c53-w-0.751183.3.v2

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f1799847ce42c1a5f9fe25dfa8d8da9e1a6ff57595979b2bd0daea658d9ea785
+size 40

gpt-2-tamil/events.out.tfevents.1626339585.t1v-n-ebe36c53-w-0.759145.3.v2

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b47918f07e65192c48181c8f775cbf29f08585ac3a559e67df1e3f13fb1ca01
+size 40

gpt-2-tamil/events.out.tfevents.1626340740.t1v-n-ebe36c53-w-0.765413.3.v2

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5855b0a71977e29453739fe2c5055c32753a62fa6d3db8ea3f105fd8ca75357b
+size 40

gpt-2-tamil/events.out.tfevents.1626341319.t1v-n-ebe36c53-w-0.768105.3.v2

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:938ebc19608236e36e53fd65f7c12c9d7ad0de447d01d60627441645872ef573
+size 22272043

gpt-2-tamil/flax_model.msgpack

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89396995064d16071519a20c2771d661400da8c3d644966f0a586d299d1b2fa3
+size 497764120

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a428e17fe6c41c96b3ac840af045afc1bf45a043e04d220f78944c38168b740
+size 510359598

model/README.md

@@ -0,0 +1 @@
+Model card details can go here.

notebook/README.md

@@ -0,0 +1 @@
+Notebook can go here.

pyproject.toml

@@ -0,0 +1,31 @@
+# Black formatting
+[tool.black]
+line-length = 85
+include = '\.pyi?$'
+exclude = '''
+/(
+      \.eggs         # exclude a few common directories in the
+    | \.git          # root of the project
+    | \.hg
+    | \.mypy_cache
+    | \.tox
+    | \.venv
+    | _build
+    | buck-out
+    | build
+    | dist
+    | wandb
+    | model
+    | dataset
+    | notebook
+  )/
+'''
+
+# iSort
+[tool.isort]
+profile = "black"
+line_length = 85
+multi_line_output = 3
+include_trailing_comma = true
+skip_gitignore = true
+virtual_env = "venv"

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:183e23beb6421156e9472504710d66104d4d43829fb87cffd22d888565f27a3a
+size 510401385

requirements.txt

@@ -0,0 +1,8 @@
+tqdm
+transformers
+datasets
+jax
+jaxlib
+flax
+optax
+wandb

scripts/train_gpt2-oscar-tamil.sh

@@ -0,0 +1,25 @@
+#!/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="10" \
+    --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

scripts/wandb/latest-run

@@ -0,0 +1 @@
+run-20210715_092837-watdq7ib

scripts/wandb/run-20210712_164633-1ddv4131/run-1ddv4131.wandb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d8211487b4d0a0489ae4728120abad1be7ee4190520afc47fdae166087ae6068
+size 60817322

scripts/wandb/run-20210715_080856-2mpx5n1j/files/config.yaml

@@ -0,0 +1,305 @@
+wandb_version: 1
+
+__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
+      - 3
+      - 11
+      2:
+      - 1
+      - 3
+      - 11
+      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: 2500
+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
+local_rank:
+  desc: null
+  value: -1
+log_level:
+  desc: null
+  value: -1
+log_level_replica:
+  desc: null
+  value: -1
+log_on_each_node:
+  desc: null
+  value: true
+logging_dir:
+  desc: null
+  value: ../gpt-2-tamil/runs/Jul15_06-31-48_t1v-n-ebe36c53-w-0
+logging_first_step:
+  desc: null
+  value: false
+logging_steps:
+  desc: null
+  value: 500
+logging_strategy:
+  desc: null
+  value: IntervalStrategy.STEPS
+lr_scheduler_type:
+  desc: null
+  value: SchedulerType.LINEAR
+max_eval_samples:
+  desc: null
+  value: null
+max_grad_norm:
+  desc: null
+  value: 1.0
+max_steps:
+  desc: null
+  value: -1
+max_train_samples:
+  desc: null
+  value: null
+metric_for_best_model:
+  desc: null
+  value: null
+model_name_or_path:
+  desc: null
+  value: null
+model_type:
+  desc: null
+  value: gpt2
+mp_parameters:
+  desc: null
+  value: ''
+no_cuda:
+  desc: null
+  value: false
+num_train_epochs:
+  desc: null
+  value: 10.0
+output_dir:
+  desc: null
+  value: ../gpt-2-tamil
+overwrite_cache:
+  desc: null
+  value: false
+overwrite_output_dir:
+  desc: null
+  value: true
+past_index:
+  desc: null
+  value: -1
+per_device_eval_batch_size:
+  desc: null
+  value: 128
+per_device_train_batch_size:
+  desc: null
+  value: 128
+per_gpu_eval_batch_size:
+  desc: null
+  value: null
+per_gpu_train_batch_size:
+  desc: null
+  value: null
+prediction_loss_only:
+  desc: null
+  value: false
+preprocessing_num_workers:
+  desc: null
+  value: 90
+push_to_hub:
+  desc: null
+  value: false
+push_to_hub_model_id:
+  desc: null
+  value: gpt-2-tamil
+push_to_hub_organization:
+  desc: null
+  value: null
+push_to_hub_token:
+  desc: null
+  value: null
+remove_unused_columns:
+  desc: null
+  value: true
+report_to:
+  desc: null
+  value:
+  - wandb
+resume_from_checkpoint:
+  desc: null
+  value: null
+run_name:
+  desc: null
+  value: trial
+save_on_each_node:
+  desc: null
+  value: false
+save_steps:
+  desc: null
+  value: 2500
+save_strategy:
+  desc: null
+  value: IntervalStrategy.STEPS
+save_total_limit:
+  desc: null
+  value: null
+seed:
+  desc: null
+  value: 42
+sharded_ddp:
+  desc: null
+  value: []
+skip_memory_metrics:
+  desc: null
+  value: true
+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

scripts/wandb/run-20210715_080856-2mpx5n1j/files/events.out.tfevents.1626336540.t1v-n-ebe36c53-w-0.751183.3.v2

@@ -0,0 +1 @@
+/home/tweety_abi/GPT2-Tamil/gpt-2-tamil/events.out.tfevents.1626336540.t1v-n-ebe36c53-w-0.751183.3.v2

scripts/wandb/run-20210715_080856-2mpx5n1j/files/requirements.txt

@@ -0,0 +1,123 @@
+absl-py==0.13.0
+aiohttp==3.7.4.post0
+appdirs==1.4.4
+astunparse==1.6.3
+async-timeout==3.0.1
+attrs==21.2.0
+backcall==0.2.0
+black==21.6b0
+cachetools==4.2.2
+certifi==2021.5.30
+cfgv==3.3.0
+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
+dm-tree==0.1.6
+docker-pycreds==0.4.0
+filelock==3.0.12
+flake8==3.9.2
+flatbuffers==1.12
+flax==0.3.4
+fsspec==2021.6.1
+gast==0.4.0
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==1.32.1
+google-pasta==0.2.0
+grpcio==1.34.1
+h5py==3.1.0
+huggingface-hub==0.0.12
+identify==2.2.10
+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
+msgpack==1.0.2
+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
+optax==0.0.8
+packaging==20.9
+pandas==1.2.5
+parso==0.8.2
+pathspec==0.8.1
+pathtools==0.1.2
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow==8.3.0
+pip==20.0.2
+pkg-resources==0.0.0
+pre-commit==2.13.0
+promise==2.3
+prompt-toolkit==3.0.19
+protobuf==3.17.3
+psutil==5.8.0
+ptyprocess==0.7.0
+pyarrow==4.0.1
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+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

scripts/wandb/run-20210715_080856-2mpx5n1j/files/wandb-metadata.json

@@ -0,0 +1,49 @@
+{
+    "os": "Linux-5.4.0-1043-gcp-x86_64-with-glibc2.29",
+    "python": "3.8.10",
+    "heartbeatAt": "2021-07-15T08:09:00.134255",
+    "startedAt": "2021-07-15T08:08:56.269238",
+    "docker": null,
+    "cpu_count": 96,
+    "cuda": null,
+    "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=128",
+        "--per_device_eval_batch_size=128",
+        "--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=10",
+        "--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": "69c9b7bf75b708a8f62cf5833d1b89acf5d1760b"
+    },
+    "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"
+}

scripts/wandb/run-20210715_080856-2mpx5n1j/files/wandb-summary.json

@@ -0,0 +1 @@
+{}

scripts/wandb/run-20210715_080856-2mpx5n1j/run-2mpx5n1j.wandb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad2816f7f07dec6835ab15fdfb6fa81ca124f1b3f1dfbaccb9b2f3658286d158
+size 38211

scripts/wandb/run-20210715_085943-1ize2alk/files/config.yaml

@@ -0,0 +1,301 @@
+wandb_version: 1
+
+__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
+      - 3
+      - 11
+      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: 2500
+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
+local_rank:
+  desc: null
+  value: -1
+log_level:
+  desc: null
+  value: -1
+log_level_replica:
+  desc: null
+  value: -1
+log_on_each_node:
+  desc: null
+  value: true
+logging_dir:
+  desc: null
+  value: ../gpt-2-tamil/runs/Jul15_07-55-49_t1v-n-ebe36c53-w-0
+logging_first_step:
+  desc: null
+  value: false
+logging_steps:
+  desc: null
+  value: 500
+logging_strategy:
+  desc: null
+  value: IntervalStrategy.STEPS
+lr_scheduler_type:
+  desc: null
+  value: SchedulerType.LINEAR
+max_eval_samples:
+  desc: null
+  value: null
+max_grad_norm:
+  desc: null
+  value: 1.0
+max_steps:
+  desc: null
+  value: -1
+max_train_samples:
+  desc: null
+  value: null
+metric_for_best_model:
+  desc: null
+  value: null
+model_name_or_path:
+  desc: null
+  value: null
+model_type:
+  desc: null
+  value: gpt2
+mp_parameters:
+  desc: null
+  value: ''
+no_cuda:
+  desc: null
+  value: false
+num_train_epochs:
+  desc: null
+  value: 10.0
+output_dir:
+  desc: null
+  value: ../gpt-2-tamil
+overwrite_cache:
+  desc: null
+  value: false
+overwrite_output_dir:
+  desc: null
+  value: true
+past_index:
+  desc: null
+  value: -1
+per_device_eval_batch_size:
+  desc: null
+  value: 128
+per_device_train_batch_size:
+  desc: null
+  value: 128
+per_gpu_eval_batch_size:
+  desc: null
+  value: null
+per_gpu_train_batch_size:
+  desc: null
+  value: null
+prediction_loss_only:
+  desc: null
+  value: false
+preprocessing_num_workers:
+  desc: null
+  value: 90
+push_to_hub:
+  desc: null
+  value: false
+push_to_hub_model_id:
+  desc: null
+  value: gpt-2-tamil
+push_to_hub_organization:
+  desc: null
+  value: null
+push_to_hub_token:
+  desc: null
+  value: null
+remove_unused_columns:
+  desc: null
+  value: true
+report_to:
+  desc: null
+  value:
+  - wandb
+resume_from_checkpoint:
+  desc: null
+  value: null
+run_name:
+  desc: null
+  value: trial
+save_on_each_node:
+  desc: null
+  value: false
+save_steps:
+  desc: null
+  value: 2500
+save_strategy:
+  desc: null
+  value: IntervalStrategy.STEPS
+save_total_limit:
+  desc: null
+  value: null
+seed:
+  desc: null
+  value: 42
+sharded_ddp:
+  desc: null
+  value: []
+skip_memory_metrics:
+  desc: null
+  value: true
+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

scripts/wandb/run-20210715_085943-1ize2alk/files/events.out.tfevents.1626339585.t1v-n-ebe36c53-w-0.759145.3.v2

@@ -0,0 +1 @@
+/home/tweety_abi/GPT2-Tamil/gpt-2-tamil/events.out.tfevents.1626339585.t1v-n-ebe36c53-w-0.759145.3.v2

scripts/wandb/run-20210715_085943-1ize2alk/files/requirements.txt

@@ -0,0 +1,123 @@
+absl-py==0.13.0
+aiohttp==3.7.4.post0
+appdirs==1.4.4
+astunparse==1.6.3
+async-timeout==3.0.1
+attrs==21.2.0
+backcall==0.2.0
+black==21.6b0
+cachetools==4.2.2
+certifi==2021.5.30
+cfgv==3.3.0
+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
+dm-tree==0.1.6
+docker-pycreds==0.4.0
+filelock==3.0.12
+flake8==3.9.2
+flatbuffers==1.12
+flax==0.3.4
+fsspec==2021.6.1
+gast==0.4.0
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==1.32.1
+google-pasta==0.2.0
+grpcio==1.34.1
+h5py==3.1.0
+huggingface-hub==0.0.12
+identify==2.2.10
+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
+msgpack==1.0.2
+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
+optax==0.0.8
+packaging==20.9
+pandas==1.2.5
+parso==0.8.2
+pathspec==0.8.1
+pathtools==0.1.2
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow==8.3.0
+pip==20.0.2
+pkg-resources==0.0.0
+pre-commit==2.13.0
+promise==2.3
+prompt-toolkit==3.0.19
+protobuf==3.17.3
+psutil==5.8.0
+ptyprocess==0.7.0
+pyarrow==4.0.1
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+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

scripts/wandb/run-20210715_085943-1ize2alk/files/wandb-metadata.json

@@ -0,0 +1,49 @@
+{
+    "os": "Linux-5.4.0-1043-gcp-x86_64-with-glibc2.29",
+    "python": "3.8.10",
+    "heartbeatAt": "2021-07-15T08:59:45.122600",
+    "startedAt": "2021-07-15T08:59:43.232731",
+    "docker": null,
+    "cpu_count": 96,
+    "cuda": null,
+    "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=128",
+        "--per_device_eval_batch_size=128",
+        "--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=10",
+        "--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": "69c9b7bf75b708a8f62cf5833d1b89acf5d1760b"
+    },
+    "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"
+}

scripts/wandb/run-20210715_085943-1ize2alk/files/wandb-summary.json

@@ -0,0 +1 @@
+{}

scripts/wandb/run-20210715_085943-1ize2alk/run-1ize2alk.wandb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4ddd483c4184ad35f642b4c9ddd01c8f4915a2cd4d811fb5e6395adec23ec07e
+size 11149

scripts/wandb/run-20210715_091856-2v0tf7h4/files/config.yaml

@@ -0,0 +1,305 @@
+wandb_version: 1
+
+__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
+      - 3
+      - 11
+      2:
+      - 1
+      - 3
+      - 11
+      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: 2500
+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
+local_rank:
+  desc: null
+  value: -1
+log_level:
+  desc: null
+  value: -1
+log_level_replica:
+  desc: null
+  value: -1
+log_on_each_node:
+  desc: null
+  value: true
+logging_dir:
+  desc: null
+  value: ../gpt-2-tamil/runs/Jul15_09-16-14_t1v-n-ebe36c53-w-0
+logging_first_step:
+  desc: null
+  value: false
+logging_steps:
+  desc: null
+  value: 500
+logging_strategy:
+  desc: null
+  value: IntervalStrategy.STEPS
+lr_scheduler_type:
+  desc: null
+  value: SchedulerType.LINEAR
+max_eval_samples:
+  desc: null
+  value: null
+max_grad_norm:
+  desc: null
+  value: 1.0
+max_steps:
+  desc: null
+  value: -1
+max_train_samples:
+  desc: null
+  value: null
+metric_for_best_model:
+  desc: null
+  value: null
+model_name_or_path:
+  desc: null
+  value: null
+model_type:
+  desc: null
+  value: gpt2
+mp_parameters:
+  desc: null
+  value: ''
+no_cuda:
+  desc: null
+  value: false
+num_train_epochs:
+  desc: null
+  value: 10.0
+output_dir:
+  desc: null
+  value: ../gpt-2-tamil
+overwrite_cache:
+  desc: null
+  value: false
+overwrite_output_dir:
+  desc: null
+  value: true
+past_index:
+  desc: null
+  value: -1
+per_device_eval_batch_size:
+  desc: null
+  value: 128
+per_device_train_batch_size:
+  desc: null
+  value: 128
+per_gpu_eval_batch_size:
+  desc: null
+  value: null
+per_gpu_train_batch_size:
+  desc: null
+  value: null
+prediction_loss_only:
+  desc: null
+  value: false
+preprocessing_num_workers:
+  desc: null
+  value: 90
+push_to_hub:
+  desc: null
+  value: false
+push_to_hub_model_id:
+  desc: null
+  value: gpt-2-tamil
+push_to_hub_organization:
+  desc: null
+  value: null
+push_to_hub_token:
+  desc: null
+  value: null
+remove_unused_columns:
+  desc: null
+  value: true
+report_to:
+  desc: null
+  value:
+  - wandb
+resume_from_checkpoint:
+  desc: null
+  value: null
+run_name:
+  desc: null
+  value: trial
+save_on_each_node:
+  desc: null
+  value: false
+save_steps:
+  desc: null
+  value: 2500
+save_strategy:
+  desc: null
+  value: IntervalStrategy.STEPS
+save_total_limit:
+  desc: null
+  value: null
+seed:
+  desc: null
+  value: 42
+sharded_ddp:
+  desc: null
+  value: []
+skip_memory_metrics:
+  desc: null
+  value: true
+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

scripts/wandb/run-20210715_091856-2v0tf7h4/files/events.out.tfevents.1626340740.t1v-n-ebe36c53-w-0.765413.3.v2

@@ -0,0 +1 @@
+/home/tweety_abi/GPT2-Tamil/gpt-2-tamil/events.out.tfevents.1626340740.t1v-n-ebe36c53-w-0.765413.3.v2

scripts/wandb/run-20210715_091856-2v0tf7h4/files/requirements.txt

@@ -0,0 +1,123 @@
+absl-py==0.13.0
+aiohttp==3.7.4.post0
+appdirs==1.4.4
+astunparse==1.6.3
+async-timeout==3.0.1
+attrs==21.2.0
+backcall==0.2.0
+black==21.6b0
+cachetools==4.2.2
+certifi==2021.5.30
+cfgv==3.3.0
+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
+dm-tree==0.1.6
+docker-pycreds==0.4.0
+filelock==3.0.12
+flake8==3.9.2
+flatbuffers==1.12
+flax==0.3.4
+fsspec==2021.6.1
+gast==0.4.0
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==1.32.1
+google-pasta==0.2.0
+grpcio==1.34.1
+h5py==3.1.0
+huggingface-hub==0.0.12
+identify==2.2.10
+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
+msgpack==1.0.2
+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
+optax==0.0.8
+packaging==20.9
+pandas==1.2.5
+parso==0.8.2
+pathspec==0.8.1
+pathtools==0.1.2
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow==8.3.0
+pip==20.0.2
+pkg-resources==0.0.0
+pre-commit==2.13.0
+promise==2.3
+prompt-toolkit==3.0.19
+protobuf==3.17.3
+psutil==5.8.0
+ptyprocess==0.7.0
+pyarrow==4.0.1
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+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

scripts/wandb/run-20210715_091856-2v0tf7h4/files/wandb-metadata.json

@@ -0,0 +1,49 @@
+{
+    "os": "Linux-5.4.0-1043-gcp-x86_64-with-glibc2.29",
+    "python": "3.8.10",
+    "heartbeatAt": "2021-07-15T09:19:00.102585",
+    "startedAt": "2021-07-15T09:18:56.277815",
+    "docker": null,
+    "cpu_count": 96,
+    "cuda": null,
+    "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=128",
+        "--per_device_eval_batch_size=128",
+        "--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=10",
+        "--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": "69c9b7bf75b708a8f62cf5833d1b89acf5d1760b"
+    },
+    "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"
+}

scripts/wandb/run-20210715_091856-2v0tf7h4/files/wandb-summary.json

@@ -0,0 +1 @@
+{}

scripts/wandb/run-20210715_091856-2v0tf7h4/run-2v0tf7h4.wandb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:74c24264810cc8a5625c9a6fd0093d95ea89e0980f556fce2e873e00ba0254c5
+size 38212

scripts/wandb/run-20210715_092837-watdq7ib/files/config.yaml

@@ -0,0 +1,301 @@
+wandb_version: 1
+
+__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
+      - 3
+      - 11
+      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: 2500
+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
+local_rank:
+  desc: null
+  value: -1
+log_level:
+  desc: null
+  value: -1
+log_level_replica:
+  desc: null
+  value: -1
+log_on_each_node:
+  desc: null
+  value: true
+logging_dir:
+  desc: null
+  value: ../gpt-2-tamil/runs/Jul15_09-27-21_t1v-n-ebe36c53-w-0
+logging_first_step:
+  desc: null
+  value: false
+logging_steps:
+  desc: null
+  value: 500
+logging_strategy:
+  desc: null
+  value: IntervalStrategy.STEPS
+lr_scheduler_type:
+  desc: null
+  value: SchedulerType.LINEAR
+max_eval_samples:
+  desc: null
+  value: null
+max_grad_norm:
+  desc: null
+  value: 1.0
+max_steps:
+  desc: null
+  value: -1
+max_train_samples:
+  desc: null
+  value: null
+metric_for_best_model:
+  desc: null
+  value: null
+model_name_or_path:
+  desc: null
+  value: null
+model_type:
+  desc: null
+  value: gpt2
+mp_parameters:
+  desc: null
+  value: ''
+no_cuda:
+  desc: null
+  value: false
+num_train_epochs:
+  desc: null
+  value: 10.0
+output_dir:
+  desc: null
+  value: ../gpt-2-tamil
+overwrite_cache:
+  desc: null
+  value: false
+overwrite_output_dir:
+  desc: null
+  value: true
+past_index:
+  desc: null
+  value: -1
+per_device_eval_batch_size:
+  desc: null
+  value: 64
+per_device_train_batch_size:
+  desc: null
+  value: 64
+per_gpu_eval_batch_size:
+  desc: null
+  value: null
+per_gpu_train_batch_size:
+  desc: null
+  value: null
+prediction_loss_only:
+  desc: null
+  value: false
+preprocessing_num_workers:
+  desc: null
+  value: 90
+push_to_hub:
+  desc: null
+  value: false
+push_to_hub_model_id:
+  desc: null
+  value: gpt-2-tamil
+push_to_hub_organization:
+  desc: null
+  value: null
+push_to_hub_token:
+  desc: null
+  value: null
+remove_unused_columns:
+  desc: null
+  value: true
+report_to:
+  desc: null
+  value:
+  - wandb
+resume_from_checkpoint:
+  desc: null
+  value: null
+run_name:
+  desc: null
+  value: trial
+save_on_each_node:
+  desc: null
+  value: false
+save_steps:
+  desc: null
+  value: 2500
+save_strategy:
+  desc: null
+  value: IntervalStrategy.STEPS
+save_total_limit:
+  desc: null
+  value: null
+seed:
+  desc: null
+  value: 42
+sharded_ddp:
+  desc: null
+  value: []
+skip_memory_metrics:
+  desc: null
+  value: true
+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

scripts/wandb/run-20210715_092837-watdq7ib/files/events.out.tfevents.1626341319.t1v-n-ebe36c53-w-0.768105.3.v2

@@ -0,0 +1 @@
+/home/tweety_abi/GPT2-Tamil/gpt-2-tamil/events.out.tfevents.1626341319.t1v-n-ebe36c53-w-0.768105.3.v2

scripts/wandb/run-20210715_092837-watdq7ib/files/requirements.txt

@@ -0,0 +1,123 @@
+absl-py==0.13.0
+aiohttp==3.7.4.post0
+appdirs==1.4.4
+astunparse==1.6.3
+async-timeout==3.0.1
+attrs==21.2.0
+backcall==0.2.0
+black==21.6b0
+cachetools==4.2.2
+certifi==2021.5.30
+cfgv==3.3.0
+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
+dm-tree==0.1.6
+docker-pycreds==0.4.0
+filelock==3.0.12
+flake8==3.9.2
+flatbuffers==1.12
+flax==0.3.4
+fsspec==2021.6.1
+gast==0.4.0
+gitdb==4.0.7
+gitpython==3.1.18
+google-auth-oauthlib==0.4.4
+google-auth==1.32.1
+google-pasta==0.2.0
+grpcio==1.34.1
+h5py==3.1.0
+huggingface-hub==0.0.12
+identify==2.2.10
+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
+msgpack==1.0.2
+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
+optax==0.0.8
+packaging==20.9
+pandas==1.2.5
+parso==0.8.2
+pathspec==0.8.1
+pathtools==0.1.2
+pexpect==4.8.0
+pickleshare==0.7.5
+pillow==8.3.0
+pip==20.0.2
+pkg-resources==0.0.0
+pre-commit==2.13.0
+promise==2.3
+prompt-toolkit==3.0.19
+protobuf==3.17.3
+psutil==5.8.0
+ptyprocess==0.7.0
+pyarrow==4.0.1
+pyasn1-modules==0.2.8
+pyasn1==0.4.8
+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

scripts/wandb/run-20210715_092837-watdq7ib/files/wandb-metadata.json

@@ -0,0 +1,49 @@
+{
+    "os": "Linux-5.4.0-1043-gcp-x86_64-with-glibc2.29",
+    "python": "3.8.10",
+    "heartbeatAt": "2021-07-15T09:28:39.248463",
+    "startedAt": "2021-07-15T09:28:37.215410",
+    "docker": null,
+    "cpu_count": 96,
+    "cuda": null,
+    "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=10",
+        "--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": "69c9b7bf75b708a8f62cf5833d1b89acf5d1760b"
+    },
+    "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"
+}

scripts/wandb/run-20210715_092837-watdq7ib/files/wandb-summary.json

@@ -0,0 +1 @@
+{"global_step": 162500, "_timestamp": 1626515598.82067, "train_time": 3039175.75, "train_learning_rate": 2.0749264422192937e-06, "_step": 324025, "train_loss": 1.1235102415084839, "eval_loss": 1.1323037147521973, "eval_perplexity": 3.1027963161468506}

scripts/wandb/run-20210715_092837-watdq7ib/run-watdq7ib.wandb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17ccbfb69a2e91865a50d34837db9291fa2687143f65c6f6c712e23f40a46343
+size 71362583

src/convert_flax_to_pytorch.py

@@ -0,0 +1,4 @@
+from transformers import GPT2LMHeadModel
+
+model = GPT2LMHeadModel.from_pretrained("../gpt-2-tamil", from_flax=True)
+model.save_pretrained("../")

src/create_config.py

@@ -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)

src/run_clm_flax.py

@@ -0,0 +1,661 @@
+#!/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, concatenate_datasets
+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.
+
+    #GPT-2 tamil
+    logger.info(f"Loading dataset....")
+    print("Loading indic corp tamil dataset")
+    indic_tamil = load_dataset("csv",data_files="/tmp/indic_corp/ta.csv")
+
+    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,
+            )
+            ## GPT2-tamil - adding indic_corp dataset manually
+            print("Concatenating datasets")
+            #pdb.set_trace()
+            dataset['train'] = concatenate_datasets([indic_tamil['train'],dataset['train']])
+    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='wandb',
+            project='hf-flax-gpt2-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()

src/train_tokenizer.py

@@ -0,0 +1,40 @@
+
+from datasets import load_dataset,concatenate_datasets
+from tokenizers import trainers, Tokenizer, normalizers, ByteLevelBPETokenizer
+
+
+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")
+indic_tamil = load_dataset("csv",data_files="/tmp/indic_corp/ta.csv")
+dataset = concatenate_datasets([dataset,indic_tamil['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")