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Upgrade ViT README with torch.compile #1494

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Upgrade ViT README with torch.compile #1494

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30 changes: 17 additions & 13 deletions examples/image-classification/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ pip install -r requirements.txt
Here we show how to fine-tune a Vision Transformer (`ViT`) on Cifar10:

```bash
python run_image_classification.py \
PT_HPU_LAZY_MODE=0 python run_image_classification.py \
--model_name_or_path google/vit-base-patch16-224-in21k \
--dataset_name cifar10 \
--output_dir /tmp/outputs/ \
Expand All @@ -51,10 +51,11 @@ python run_image_classification.py \
--save_total_limit 3 \
--seed 1337 \
--use_habana \
--use_lazy_mode \
--use_hpu_graphs_for_inference \
--use_lazy_mode False \
--torch_compile_backend hpu_backend \
--torch_compile \
--gaudi_config_name Habana/vit \
--throughput_warmup_steps 3 \
--throughput_warmup_steps 6 \
--dataloader_num_workers 1 \
--bf16
```
Expand Down Expand Up @@ -92,16 +93,17 @@ root/cat/[...]/asd932_.png
In other words, you need to organize your images in subfolders, based on their class. You can then run the script like this:

```bash
python run_image_classification.py \
PT_HPU_LAZY_MODE=0 python run_image_classification.py \
--model_name_or_path google/vit-base-patch16-224-in21k \
--train_dir <path-to-train-root> \
--output_dir /tmp/outputs/ \
--remove_unused_columns False \
--do_train \
--do_eval \
--use_habana \
--use_lazy_mode \
--use_hpu_graphs_for_inference \
--use_lazy_mode False \
--torch_compile_backend hpu_backend \
--torch_compile \
--gaudi_config_name Habana/vit \
--throughput_warmup_steps 3 \
--dataloader_num_workers 1 \
Expand Down Expand Up @@ -184,7 +186,7 @@ python run_image_classification.py \
Here is how you would fine-tune ViT on Cifar10 using 8 HPUs:

```bash
python ../gaudi_spawn.py \
PT_HPU_LAZY_MODE=0 python ../gaudi_spawn.py \
--world_size 8 --use_mpi run_image_classification.py \
--model_name_or_path google/vit-base-patch16-224-in21k \
--dataset_name cifar10 \
Expand All @@ -203,8 +205,9 @@ python ../gaudi_spawn.py \
--save_total_limit 3 \
--seed 1337 \
--use_habana \
--use_lazy_mode \
--use_hpu_graphs_for_inference \
--use_lazy_mode False \
--torch_compile_backend hpu_backend \
--torch_compile \
--gaudi_config_name Habana/vit \
--throughput_warmup_steps 8 \
--dataloader_num_workers 1 \
Expand All @@ -224,7 +227,7 @@ For Swin, you need to change/add the following arguments:
Similarly to multi-HPU training, here is how you would fine-tune ViT on Cifar10 using 8 HPUs with DeepSpeed:

```bash
python ../gaudi_spawn.py \
PT_HPU_LAZY_MODE=0 python ../gaudi_spawn.py \
--world_size 8 --use_deepspeed run_image_classification.py \
--model_name_or_path google/vit-base-patch16-224-in21k \
--dataset_name cifar10 \
Expand All @@ -243,8 +246,9 @@ python ../gaudi_spawn.py \
--save_total_limit 3 \
--seed 1337 \
--use_habana \
--use_lazy_mode \
--use_hpu_graphs_for_inference \
--use_lazy_mode False \
--torch_compile_backend hpu_backend \
--torch_compile \
--gaudi_config_name Habana/vit \
--throughput_warmup_steps 3 \
--dataloader_num_workers 1 \
Expand Down