metadata

tags:
  - generated_from_trainer
datasets:
  - wikitext
model-index:
  - name: clm_output_medium
    results: []

clm_output_medium

This model is a fine-tuned version of gpt2-medium on the wikitext-103-raw-v1 dataset. It achieves the following results on the evaluation set:

Loss: 2.6973

Model description

GPT2 is a large transformer-based language model. It is built using transformer decoder blocks. BERT, on the other hand, uses transformer encoder blocks. It adds Layer normalisation to the input of each sub-block, similar to a pre-activation residual networks and an additional layer normalisation.

Paper link : Language Models are Unsupervised Multitask Learners

Intended uses & limitations

More information needed

Training and evaluation data

wikitext-103-raw-v1 dataset

Training procedure

Trained on 16 Graphcore Mk2 IPUs using optimum-graphcore.

Command line:

python examples/language-modeling/run_clm.py \
  --model_name_or_path gpt2-medium \
  --ipu_config_name Graphcore/gpt2-medium-ipu \
  --dataset_name wikitext \
  --dataset_config_name wikitext-103-raw-v1 \
  --do_train \
  --do_eval \
  --num_train_epochs 10 \
  --dataloader_num_workers 64 \
  --per_device_train_batch_size 1 \
  --per_device_eval_batch_size 1 \
  --gradient_accumulation_steps 256 \
  --output_dir /tmp/clm_output_medium \
  --logging_steps 5 \
  --learning_rate 1e-5 \
  --lr_scheduler_type linear \
  --loss_scaling 16384 \
  --weight_decay 0.01 \
  --warmup_ratio 0.1 \
  --ipu_config_overrides="embedding_serialization_factor=5,inference_device_iterations=9,replication_factor=2,inference_replication_factor=2,ipus_per_replica=8,layers_per_ipu=[0 3 3 3 3 4 4 4],matmul_proportion=0.25" \
  --dataloader_drop_last \
  --pod_type pod16

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: IPU
gradient_accumulation_steps: 256
total_train_batch_size: 1024
total_eval_batch_size: 18
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10.0
training precision: Mixed Precision

Training results

***** train metrics *****
    "epoch": 10.0,
    "train_loss": 2.8070910754504506,
    "train_runtime": 11217.8167,
    "train_samples": 114248,
    "train_samples_per_second": 101.845,
    "train_steps_per_second": 0.099
    
***** eval metrics *****
    "eval_loss": 2.697265625,
    "eval_samples": 240,
    "perplexity": 14.83910053420958

Framework versions

Transformers 4.18.0.dev0
Pytorch 1.10.0+cpu
Datasets 2.0.0
Tokenizers 0.11.6