PyTorch Multi-GPU support

Currently AIMET supports models using Multi-GPU in data parallel mode with the following features

  1. Cross-Layer Equalization (CLE)

  2. Quantization Aware Training (QAT)

A user can create a Data Parallel model using torch APIs. For example:

# Instantiate a torch model and pass it to DataParallel API
model = torch.nn.DataParallel(model)

Multi-GPU with CLE

For using multi-GPU with CLE, you can pass the above created model directly to the CLE API Cross-Layer Equalization API

NOTE: CLE doesn’t actually make use of multi-GPU, it is only integrated as a part of work-flow so that user need not move the model back and forth from single gpu to multi-GPU and back.

Multi-GPU with Quantization Aware Training

For using multi-GPU with QAT,

  1. Create a QuantizationSim as shown in Quantization Simulation API using a torch model (Not in DataParallel mode)

  2. Perform compute encodings (NOTE: Do not use a forward function that moves the model to multi-gpu and back)

  3. Move sim model to DataParallel:

    sim.model = torch.nn.DataParallel(sim.model)
  4. Perform Eval/Training