1 GPU Memory Usage
1.1 How to Compute
How to compute GPU Memory Usage?
Model size:
Model Weights: 4Bytes * num_param
Optimizer: 4Bytes * 2 * num_param (for AdamW)
Gradient: 4Bytes * num_param
feed forward:
sum:
1.2 How to Reduce
Strategy 1:
| Optimization Strategy | Optimization Object | Description | Training Time |
|---|---|---|---|
| Baseline | - | ||
| + Gradient Accumulation | Forward propagation value | ||
+ Gradient CheckpointsTrainer(gradient_checkingpoint = True) |
Forward propagation value | not save the immediate weights and values | take more time -> get less memory |
| + Adafactor Optimizer | Optimizer | ||
| + Freeze Model | Forward propagation value / Gradient | ||
| + Data Length | Forward propagation value |
Strategy 2: Reduce the number of parameters
PEFT(Prompt Tuning, LoRA...)
Strategy 3: Reduce the number of bytes each parameter occupies
The default precision is single precision, which is represented as fp32, using 32 bits to represent one digit.
| Name | |||
|---|---|---|---|
| Single-precision floating-point format | fp32 | 4 Bytes | 32 bits |
| Half-precision floating-point format | fp16 | 2 Bytes | 16 bits |
| Brain floating-point format(BFloat16) | bp16 | 2 Bytes | 16 bits |
| int8 | 1 Bytes | 8 bits | |
| fp4 | 0.5 Bytes | 4 bits | |
| 4-bit NormalFloat | nf4 | 0.5 Bytes | 4 bits |
2 Precision
02 - Half precision & LLaMA 2
03 - Half precision & ChatGLM 3
04 - 8 Bit
05 - 4 Bit & QLoRA
Reference
标签：bf16,propagation,Gradient,Bytes,precision,Quantization,fp4,Forward,bits From： https://www.cnblogs.com/forhheart/p/18171303