ColossalAI

mirror of https://github.com/hpcaitech/ColossalAI.git synced 2026-02-21 06:22:09 +00:00

Files

Hongxin Liu d202cc28c0 [npu] change device to accelerator api (#5239 )

* update accelerator

* fix timer

* fix amp

* update

* fix

* update bug

* add error raise

* fix autocast

* fix set device

* remove doc accelerator

* update doc

* update doc

* update doc

* use nullcontext

* update cpu

* update null context

* change time limit for example

* udpate

* update

* update

* update

* [npu] polish accelerator code

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Co-authored-by: Xuanlei Zhao <xuanlei.zhao@gmail.com>
Co-authored-by: zxl <43881818+oahzxl@users.noreply.github.com>

2024-01-09 10:20:05 +08:00

benchmark_utils.py

[misc] update pre-commit and run all files (#4752 )

2023-09-19 14:20:26 +08:00

benchmark.py

[misc] update pre-commit and run all files (#4752 )

2023-09-19 14:20:26 +08:00

benchmark.sh

[booster] update bert example, using booster api (#3885 )

2023-06-07 15:51:00 +08:00

data.py

[misc] update pre-commit and run all files (#4752 )

2023-09-19 14:20:26 +08:00

finetune.py

[npu] change device to accelerator api (#5239 )

2024-01-09 10:20:05 +08:00

README.md

[shardformer] update shardformer readme (#4617 )

2023-09-05 13:14:41 +08:00

requirements.txt

[booster] update bert example, using booster api (#3885 )

2023-06-07 15:51:00 +08:00

test_ci.sh

[shardformer] update bert finetune example with HybridParallelPlugin (#4584 )

2023-09-04 21:46:29 +08:00

README.md

Overview

This directory includes two parts: Using the Booster API finetune Huggingface Bert and AlBert models and benchmarking Bert and AlBert models with different Booster Plugin.

Finetune

bash test_ci.sh

Bert-Finetune Results

Plugin	Accuracy	F1-score	GPU number
torch_ddp	84.4%	88.6%	2
torch_ddp_fp16	84.7%	88.8%	2
gemini	84.0%	88.4%	2
hybrid_parallel	84.5%	88.6%	4

Benchmark

bash benchmark.sh

Now include these metrics in benchmark: CUDA mem occupy, throughput and the number of model parameters. If you have custom metrics, you can add them to benchmark_util.

Results

Bert

	max cuda mem	throughput(sample/s)	params
ddp	21.44 GB	3.0	82M
ddp_fp16	16.26 GB	11.3	82M
gemini	11.0 GB	12.9	82M
low_level_zero	11.29 G	14.7	82M

AlBert

	max cuda mem	throughput(sample/s)	params
ddp	OOM
ddp_fp16	OOM
gemini	69.39 G	1.3	208M
low_level_zero	56.89 G	1.4	208M