顯存使用量、記憶體使用量、實際算力(FLOP/s)與每秒鐘Token處理量。
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LLM硬體負載與吞吐量測試。
顯存使用量、記憶體使用量、實際算力(FLOP/s)與每秒鐘Token處理量。
以下的結果使用 Tesla T4*4
:Model
======================================
Model name: gpt2
Model size: 124M
Strategy: deepspeed_stage_2_offload
Batch size: 1
Seq length: 512
:Hardware
======================================
Number gpus: 4
--------------------------------------
:Memory
======================================
Peak CPU RAM: 12.7GB
Peak GPU RAM: 3.8GB, 3.8GB, 3.6GB, 3.6GB
--------------------------------------
:Token
======================================
Tokens/sec/GPU: 1587.348
Tokens/sec/total: 6349.392
--------------------------------------
:TFLOP
======================================
TFLOP/sec/GPU: 3.795
TFLOP/sec/total: 15.18
--------------------------------------
docker run --shm-size 8G --rm --gpus all p208p2002/llm-training-test --model_name gpt2 --batch_size 2 --seq_length 512
usage: main.py [-h]
[--strategy {deepspeed_stage_1,deepspeed_stage_2,deepspeed_stage_3,deepspeed_stage_1_offload,deepspeed_stage_2_offload,deepspeed_stage_3_offload}]
[--learning_rate LEARNING_RATE] [--batch_size BATCH_SIZE]
[--seq_length SEQ_LENGTH]
[--precision {16,32,64,bf-16,16-mixed,bf16-mixed,32-true,64-true}]
[--model_name {gpt2,gpt2-medium,gpt2-large,gpt2-xl,bigscience/bloom-560m,bigscience/bloom-1b1,bigscience/bloom-1b7,bigscience/bloom-3b,bigscience/bloom-7b1,bigscience/bloom,huggyllama/llama-7b,huggyllama/llama-13b,huggyllama/llama-30b,huggyllama/llama-65b}]
options:
-h, --help show this help message and exit
--strategy {deepspeed_stage_1,deepspeed_stage_2,deepspeed_stage_3,deepspeed_stage_1_offload,deepspeed_stage_2_offload,deepspeed_stage_3_offload}, -s {deepspeed_stage_1,deepspeed_stage_2,deepspeed_stage_3,deepspeed_stage_1_offload,deepspeed_stage_2_offload,deepspeed_stage_3_offload}
--learning_rate LEARNING_RATE, -lr LEARNING_RATE
--batch_size BATCH_SIZE, -B BATCH_SIZE
--seq_length SEQ_LENGTH, -L SEQ_LENGTH
--precision {16,32,64,bf-16,16-mixed,bf16-mixed,32-true,64-true}, -p {16,32,64,bf-16,16-mixed,bf16-mixed,32-true,64-true}
--model_name {gpt2,gpt2-medium,gpt2-large,gpt2-xl,bigscience/bloom-560m,bigscience/bloom-1b1,bigscience/bloom-1b7,bigscience/bloom-3b,bigscience/bloom-7b1,bigscience/bloom,huggyllama/llama-7b,huggyllama/llama-13b,huggyllama/llama-30b,huggyllama/llama-65b}, -M {gpt2,gpt2-medium,gpt2-large,gpt2-xl,bigscience/bloom-560m,bigscience/bloom-1b1,bigscience/bloom-1b7,bigscience/bloom-3b,bigscience/bloom-7b1,bigscience/bloom,huggyllama/llama-7b,huggyllama/llama-13b,huggyllama/llama-30b,huggyllama/llama-65b}
程式會在執行10個step後自動結束,並將結果紀錄到report.txt
$$D = Token總數$$
$$N = 模型參數量$$
$$C = 6DN$$
$$T_{days} = C/(FLOPS \times 86400 \times 10^{12})$$
參見: p208p2002/Compute-Optimal-Model-Estimator
若已知實際算力,則
utilization=1,或使用經驗值0.2~0.3。
Content type
Image
Digest
sha256:d95dbf539…
Size
6.6 GB
Last updated
over 3 years ago
docker pull p208p2002/llm-training-test