39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
from logging import getLogger
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import torch.nn as nn
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from ..quantization import make_quant, QuantLinear
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logger = getLogger(__name__)
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def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
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if type(module) in layers:
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return {name: module}
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res = {}
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for name1, child in module.named_children():
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res.update(find_layers(child, layers=layers, name=name + '.' + name1 if name != '' else name1))
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return res
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def get_module_by_name(model, module_name: str):
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for name, module in model.named_modules():
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if name.startswith(module_name):
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return module
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def pack_model(model, quantizers, bits, group_size):
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model.cpu()
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logger.info('Packing model...')
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layers = find_layers(model)
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layers = {n: layers[n] for n in quantizers}
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make_quant(model, quantizers, bits, group_size)
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qlayers = find_layers(model, [QuantLinear])
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for name in qlayers:
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logger.info(name)
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quantizers[name], scale, zero, g_idx = quantizers[name]
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qlayers[name].pack(layers[name], scale, zero, g_idx)
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logger.info('Model packed.')
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__all__ = ["find_layers", "get_module_by_name", "pack_model"]
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