torch-xla-SPMD

• Pytorch/XLA SPMD Test code in Google TPU.
• For more details, see SPMD user guide

Installation

pip install torch~=2.1.0 --index-url https://download.pytorch.org/whl/cpu
pip install torch_xla[tpu]~=2.1.0 -f https://storage.googleapis.com/libtpu-releases/index.html
pip install -r requirements.txt

# If you use conda, set your lib/ 
export LD_LIBRARY_PATH=/path/to/conda/envs/your_env/lib
# export LD_LIBRARY_PATH=$HOME/anaconda3/envs/easydel/lib/

# for transformers library in TPU
export USE_TORCH=True

SPMD usage

see train_with_spmd.py

# 1. define mesh (use fsdp in in this example)
num_devices = xr.global_runtime_device_count()
mesh_shape = (1, num_devices, 1)
device_ids = np.array(range(num_devices))
mesh = Mesh(device_ids, mesh_shape, ('dp', 'fsdp', 'mp'))

...

# 2. partition module with mesh
partition_module(model, mesh) 

...

# 3. partition input with mesh and forward
input_ids = torch.randint(0, 1000, (batch_size, seq_length + 1)).to(xm.xla_device())
xs.mark_sharding(input_ids, mesh, (0, 1))

output = model(input_ids=input_ids[:, :-1], labels=input_ids[:, 1:])

implemented sharding rules

You can see the implemented sharding rules in spmd_util.py

SPMD OOM Check (20230901 nightly)

• code: spmd_gpt.py
• Architecture: GPT NeoX
• batch_size: 1
• Optimizer: AdamW
• Mesh: (1, 8, 1). Mesh shape이 바뀌면 OOM이 생기기도 합니다.

TPU v2-8

() 괄호는 해당 크기 배치로 했을 때 OOM 발생하지 않았음을 표시. 표시 없을 경우 1이고 더 큰 배치에서 안되는 것은 아님. 실험을 안한 것.

TPU v3-8

Setup

Use docker

sudo docker run -it --name torch \
    -d --privileged \
    -p 7860:7860 \
    -v `pwd`:/workspace \
    us-central1-docker.pkg.dev/tpu-pytorch-releases/docker/xla:nightly_3.10_tpuvm_20230901 \
    /bin/bash