# Mixed precision training

In ColossalAI, we have incorporated different implementations of mixed precision training:
1. torch.cuda.amp
2. apex.amp
3. tensor-parallel amp

The first two rely on the original implementation of [PyTorch](https://pytorch.org/docs/stable/amp.html)
(version 1.6 and above) and [Nvidia Apex](https://github.com/NVIDIA/apex). However, these two methods are not compatible 
with tensor parallelism. This is because that tensors are split across devices in tensor parallelism, thus, it is required 
to communicate among different processes to check if `inf` or `nan` occurs in the whole model weights. For the mixed
precision training with tensor parallelism, we adapted this feature from [Megatron-LM](https://github.com/NVIDIA/Megatron-LM). 

To use mixed precision training, you can easily specify the `fp16` field in the config file to be True. Currently, PyTorch and 
Apex amp cannot be guaranteed to work with tensor and pipeline parallelism, thus, only the last one is recommended if you 
are using hybrid parallelism.

## PyTorch AMP

PyTorch provides mixed precision training in version 1.6 and above. It provides an easy way to cast data to `fp16` format 
while keeping some operations such as reductions in `fp32`. You can configure the gradient scaler in the config file.

```python
from colossalai.engine import AMP_TYPE

fp16=dict(
    mode=AMP_TYPE.TORCH,
    # below are default values for grad scaler
    init_scale=2.**16,
    growth_factor=2.0,
    backoff_factor=0.5,
    growth_interval=2000,
    enabled=True
)
```

## Apex AMP

For this mode, we rely on the [Apex](https://nvidia.github.io/apex/) implementation for mixed precision training. We support 
this plugin because it allows for finer control on the granularity of mixed precision. For example, `O2` level (optimization level 2) 
will keep batch normalization in `fp32`.

The following code block shows a config file for Apex AMP.

```python
from colossalai.engine import AMP_TYPE

fp16 = dict(
    mode=AMP_TYPE.APEX,
    # below are the default values
    enabled=True, 
    opt_level='O1', 
    cast_model_type=None, 
    patch_torch_functions=None, 
    keep_batchnorm_fp32=None, 
    master_weights=None, 
    loss_scale=None, 
    cast_model_outputs=None,
    num_losses=1, 
    verbosity=1, 
    min_loss_scale=None, 
    max_loss_scale=16777216.0
)
```

## Tensor Parallel AMP

We leveraged the Megatron-LM implementation to achieve mixed precision training while maintaining compatibility with complex tensor 
and pipeline parallelism.

The following conde block show a config file for this mode.

```python
from colossalai.engine import AMP_TYPE

fp16 = dict(
    mode=AMP_TYPE.PARALLEL,
    # below are the default values
    clip_grad=0,
    log_num_zeros_in_grad=False,
    initial_scale=2 ** 32,
    min_scale=1,
    growth_factor=2,
    backoff_factor=0.5,
    growth_interval=1000,
    hysteresis=2
)
```