bitsandbytes-rocm/howto_config_override.md

How to override config hyperparameters for particular weights/parameters

If you want to optimize some unstable parameters with 32-bit Adam and others with 8-bit Adam, you can use the GlobalOptimManager. With this, we can also configure specific hyperparameters for particular layers, such as embedding layers. To do that, we need two things: (1) register the parameter while they are still on the CPU, (2) override the config with the new desired hyperparameters (anytime, anywhere). See our guide for more details

For global overrides in many different places in your code you can do:

import torch
import bitsandbytes as bnb

mng = bnb.optim.GlobalOptimManager.get_instance()

model = MyModel()
mng.register_parameters(model.parameters()) # 1. register parameters while still on CPU

model = model.cuda()
# use 8-bit optimizer states for all parameters
adam = bnb.optim.Adam(model.parameters(), lr=0.001, optim_bits=8) 

# 2a. override: the parameter model.fc1.weight now uses 32-bit Adam
mng.override_config(model.fc1.weight, 'optim_bits', 32) 

# 2b. override: the two special layers use
# sparse optimization + different learning rate + different Adam betas
mng.override_config([model.special.weight, model.also_special.weight],
                    key_value_dict ={'is_sparse': True, 'lr': 1e-5, 'betas'=(0.9, 0.98)}) 

Possible options for the config override are: betas, eps, weight_decay, lr, optim_bits, min_8bit_size, percentile_clipping, block_wise, max_unorm

For overrides for particular layers we recommend overriding locally in each module. You can do this by passing the module, the parameter, and its attribute name to the GlobalOptimManager:

class MyModule(torch.nn.Module):
  def __init__(din, dout):
    super(MyModule, self).__init__()
    self.linear = torch.nn.Linear(din, dout)
    # optimization will happen in 32-bit and
    # learning rate will be set to 0.0001 independent of the main learning rate
    config = {'optim_bits': 32, 'lr' : 0.0001}
    GlobalOptimManager.get_instance().register_module_override(self, 'weight', config)