27 lines
1.4 KiB
Markdown
27 lines
1.4 KiB
Markdown
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# How to override config hyperparameters for particular weights/parameters
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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](howto_config_override.md) for more details
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```python
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import torch
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import bitsandbytes as bnb
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mng = bnb.optim.GlobalOptimManager.get_instance()
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model = MyModel()
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mng.register_parameters(model.parameters()) # 1. register parameters while still on CPU
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model = model.cuda()
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# use 8-bit optimizer states for all parameters
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adam = bnb.optim.Adam(model.parameters(), lr=0.001, optim_bits=8)
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# 2a. override: the parameter model.fc1.weight now uses 32-bit Adam
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mng.override_config(model.fc1.weight, 'optim_bits', 32)
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# 2b. override: the two special layers use
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# sparse optimization + different learning rate + different Adam betas
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mng.override_config([model.special.weight, model.also_special.weight],
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key_value_dict ={'is_sparse': True, 'lr': 1e-5, 'betas'=(0.9, 0.98)})
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```
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Possible options for the config override are: `betas, eps, weight_decay, lr, optim_bits, min_8bit_size, percentile_clipping, block_wise, max_unorm`
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