semantic-segmentation-pytorch
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CSAILVision
Adding mixed precision training support
This PR adds mixed precision training support using APEX. https://github.com/NVIDIA/apex
Automatic mixed precision training makes use of both FP32 and FP16 precisions where appropriate. FP16 operations can leverage the Tensor cores on NVIDIA GPUs (Volta, Turing or newer architectures) for improved throughput.
Mixed precision training can be enabled with passing the --apex flag to the training script, for example:
python3 train.py --gpus 0-3 --cfg config/ade20k-mobilenetv2dilated-c1_deepsup.yaml --apex
How mixed precision works
Mixed precision is the use of both float16 and float32 data types when training a model.
Performing arithmetic operations in float16 takes advantage of the performance gains of using specialized processing units such as the Tensor cores on NVIDIA GPUs. Due to the smaller representable range of float16, performing the entire training with float16 data type can result in underflow of the gradients, leading to convergence or model quality issues.
However, performing only select arithmetic operations in float16 results in performance gains when using compatible hardware accelerators, decreasing training time and reducing memory usage, typically without sacrificing model performance.
To learn more about mixed precision and how it works:
Overview of Automatic Mixed Precision for Deep Learning NVIDIA Mixed Precision Training Documentation NVIDIA Deep Learning Performance Guide
@vinhngx This seems interesting. How much gain in training time were you able to achieve by using mixed precision training? Also, do we have to make any changes to the test.py also while running inferences?
@vinhngx Have you also prepared a script with mixed precision support for running inferences?
@vinhngx This seems interesting. How much gain in training time were you able to achieve by using mixed precision training? Also, do we have to make any changes to the test.py also while running inferences?
@ghost though the training is done with FP16, the weight is stored as FP32 and there is no changed required when doing inference (in FP32)
@vinhngx Have you also prepared a script with mixed precision support for running inferences?
@seedlit I have not produced a script for mixed precision inference here, though for serious productization, converting the model to ONNX then accelerated with TensorRT is recommended.
@vinhngx I am using this script for mixed precision training (on multiple gpus). However, it dosen't work well with O2, and O3. See #227. For O1, my gpus memory usage increases compared to FP32 training, and training time also increases. It seems we have to wait a little till mixed precision training support is introduced in Pytorch 1.5
I am also getting 'gradient overflow' when training on single gpu on O1 (when setting batch_size_per_gpu = 1, for batch_size_per_gpu >1, it works fine), even after
with amp.scale_loss(loss1, optimizer1) as scaled_loss:
scaled_loss.backward()
The issue I am getting is
Gradient overflow. Skipping step, loss scaler 0 reducing loss scale to 32768.0
Do you know any workaround this?
I am working on acceleration using mixed-precision training. Actually, it works fine when using a single GPU with several lines modification (about 8x faster than using 4 cards with the same batch size, half memory used). But this does not work for multiple GPUs. I think it is majorly caused by the new DataParallel and BatchNormSync implementation. Or is it possible to modify the code to have a better performance on multiple GPUs?
After referencing hszhao's DDP implementation on this link. Now my program has half GPU memory usage with 3x faster speed during the training on multiple devices.
