64 lines
2.4 KiB
Python
64 lines
2.4 KiB
Python
import shlex
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import subprocess
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from typing import Tuple
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def execute_and_return(command_string: str) -> Tuple[str, str]:
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def _decode(subprocess_err_out_tuple):
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return tuple(
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to_decode.decode("UTF-8").strip()
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for to_decode in subprocess_err_out_tuple
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)
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def execute_and_return_decoded_std_streams(command_string):
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return _decode(
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subprocess.Popen(
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shlex.split(command_string),
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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).communicate()
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)
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std_out, std_err = execute_and_return_decoded_std_streams(command_string)
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return std_out, std_err
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def replace_linear(model, linear_replacement, skip_modules=["lm_head"], copy_weights=False, post_processing_function=None):
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"""
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Replace linear modules with a new Linear module.
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Parameters:
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model (`torch.nn.Module`):
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Input model or `torch.nn.Module` as the function is run recursively.
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linear_replacement (`torch.nn.Module`):
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The linear module that replaces the old one. Only expects standard arguments.
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If other arguments need to be passed, use a lambda.
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skip_modules (`List[str]`, *optional*, defaults to `lm_head`):
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List of modules names not to convert. Defaults to `lm_head`.
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copy_weights (`bool`):
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Copy the weights from the old linear module to the new one
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post_processing_fun_name (`str`):
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A function name of the replacement linear class that is called
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after processing.
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"""
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for name, module in model.named_children():
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if len(list(module.children())) > 0:
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replace_linear(module, linear_replacement, skip_modules, copy_weights, post_processing_function)
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if isinstance(module, torch.nn.Linear) and name not in skip_modules:
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old_module = model._modules[name]
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model._modules[name] = linear_replacement(
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module.in_features,
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module.out_features,
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module.bias is not None,
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)
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if copy_weights:
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model._modules[name].weight = old_module.weight
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model._modules[name].bias = old_module.bias
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if post_processing_function is not None:
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func = getattr(module, post_processing_function, None)
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if func is not None: func(module)
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return model
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