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1cdcdd78af
Summary:
/cc goldsborough
Working on #14582
The corresponding python implementations are at: [pytorch/torch/nn/init.py](6302e4001a/torch/nn/init.py (L261-L327))
Here is my initial implementation of Kaiming Initialization. I have not been able to figure out how to successfully run tests locally so I haven't added any yet.
A couple questions:
- Are the enums defined in the right place? I copied their names from Python, but do you prefer different naming conventions for C++?
- To run tests locally do I use `python setup.py test`? Can I run just a subset of the tests somehow?
- Should I add my tests at [test/cpp/api/misc.cpp](https://github.com/pytorch/pytorch/blob/master/test/cpp/api/misc.cpp#L47-L54)?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14718
Differential Revision: D14049159
Pulled By: goldsborough
fbshipit-source-id: 966ac5126875936e69b185b5041f16476ed4cf70
73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
"""Script to generate baseline values from PyTorch initialization algorithms"""
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import sys
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import torch
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HEADER = """
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#include <torch/types.h>
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#include <vector>
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namespace expected_parameters {
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"""
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FOOTER = "} // namespace expected_parameters"
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PARAMETERS = "inline std::vector<std::vector<torch::Tensor>> {}() {{"
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INITIALIZERS = {
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"Xavier_Uniform": lambda w: torch.nn.init.xavier_uniform(w),
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"Xavier_Normal": lambda w: torch.nn.init.xavier_normal(w),
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"Kaiming_Normal": lambda w: torch.nn.init.kaiming_normal(w),
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"Kaiming_Uniform": lambda w: torch.nn.init.kaiming_uniform(w)
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}
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def emit(initializer_parameter_map):
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# Don't write generated with an @ in front, else this file is recognized as generated.
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print("// @{} from {}".format('generated', __file__))
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print(HEADER)
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for initializer_name, weights in initializer_parameter_map.items():
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print(PARAMETERS.format(initializer_name))
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print(" return {")
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for sample in weights:
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print(" {")
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for parameter in sample:
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parameter_values = "{{{}}}".format(", ".join(map(str, parameter)))
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print(" torch::tensor({}),".format(parameter_values))
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print(" },")
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print(" };")
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print("}\n")
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print(FOOTER)
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def run(initializer):
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torch.manual_seed(0)
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layer1 = torch.nn.Linear(7, 15)
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INITIALIZERS[initializer](layer1.weight)
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layer2 = torch.nn.Linear(15, 15)
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INITIALIZERS[initializer](layer2.weight)
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layer3 = torch.nn.Linear(15, 2)
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INITIALIZERS[initializer](layer3.weight)
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weight1 = layer1.weight.data.numpy()
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weight2 = layer2.weight.data.numpy()
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weight3 = layer3.weight.data.numpy()
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return [weight1, weight2, weight3]
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def main():
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initializer_parameter_map = {}
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for initializer in INITIALIZERS.keys():
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sys.stderr.write('Evaluating {} ...\n'.format(initializer))
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initializer_parameter_map[initializer] = run(initializer)
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emit(initializer_parameter_map)
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if __name__ == "__main__":
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main()
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