Benchmark for Tuning Accuracy and Efficiency
Overview
The benchmark includes our efforts in using Colossal-AI to train different tasks to achieve SOTA results. We are interested in both validataion accuracy and training speed, and prefer larger batch size to take advantage of more GPU devices. For example, we trained vision transformer with batch size 512 on CIFAR10 and 4096 on ImageNet1k, which are basically not used in existing works. Some of the results in the benchmark trained with 8x A100 are shown below.
| Task | Model | Training Time | Top-1 Accuracy |
|---|---|---|---|
| CIFAR10 | ViT-Lite-7/4 | ~ 16 min | ~ 90.5% |
| ImageNet1k | ViT-S/16 | ~ 16.5 h | ~ 74.5% |
The train.py script in each task runs training with the specific configuration script in configs/ for different parallelisms. Supported parallelisms include data parallel only (ends with vanilla), 1D (ends with 1d), 2D (ends with 2d), 2.5D (ends with 2p5d), 3D (ends with 3d).
Each configuration scripts basically includes the following elements, taking ImageNet1k task as example:
TOTAL_BATCH_SIZE = 4096
LEARNING_RATE = 3e-3
WEIGHT_DECAY = 0.3
NUM_EPOCHS = 300
WARMUP_EPOCHS = 32
# data parallel only
TENSOR_PARALLEL_SIZE = 1
TENSOR_PARALLEL_MODE = None
# parallelism setting
parallel = dict(
pipeline=1,
tensor=dict(mode=TENSOR_PARALLEL_MODE, size=TENSOR_PARALLEL_SIZE),
)
fp16 = dict(mode=AMP_TYPE.TORCH, ) # amp setting
gradient_accumulation = 2 # accumulate 2 steps for gradient update
BATCH_SIZE = TOTAL_BATCH_SIZE // gradient_accumulation # actual batch size for dataloader
clip_grad_norm = 1.0 # clip gradient with norm 1.0
Upper case elements are basically what train.py needs, and lower case elements are what Colossal-AI needs to initialize the training.
Usage
To start training, use the following command to run each worker:
$ DATA=/path/to/dataset python train.py --world_size=WORLD_SIZE \
--rank=RANK \
--local_rank=LOCAL_RANK \
--host=MASTER_IP_ADDRESS \
--port=MASTER_PORT \
--config=CONFIG_FILE
It is also recommended to start training with torchrun as:
$ DATA=/path/to/dataset torchrun --nproc_per_node=NUM_GPUS_PER_NODE \
--nnodes=NUM_NODES \
--node_rank=NODE_RANK \
--master_addr=MASTER_IP_ADDRESS \
--master_port=MASTER_PORT \
train.py --config=CONFIG_FILE
2 Sep 29, 2022
279 Dec 26, 2022
1 Nov 26, 2021
91 Nov 21, 2022
98 Dec 30, 2022
40 Dec 29, 2022
11 Oct 17, 2022
64 Dec 19, 2022
44 Oct 08, 2022
124 Dec 27, 2022
2.3k Jan 02, 2023
46 Oct 24, 2022
8 Sep 29, 2022
21 Sep 27, 2022
42 Nov 17, 2022
122 Dec 07, 2022
425 Dec 25, 2022
99 Dec 11, 2022
975 Dec 30, 2022
124 Dec 27, 2022