MPC Quantized Machine Learning- Jacobi SVD #952
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MPC Quantized Machine Learning- Jacobi SVD
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| S = (A + A.T) / 2 | ||
| return S | ||
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| n = 10 |
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算法实现的文件中不要出现这种全局的测试，你可以在tests/jacobi_test.py中测试明文实现 或者 密态精度
| A, _, final_iterations = lax.fori_loop(0, max_iter, body_fun, (A, max_off_diag, iterations)) | ||
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| singular_values = jnp.abs(jnp.diag(A)) | ||
| idx = jnp.argsort(-singular_values) |
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我理解你就只是要把特征值从大到小排序，直接sort就行啊？
singular_values[idx]还会做一次secret index，没有必要
| idx = jnp.argsort(-singular_values) | ||
| singular_values = singular_values[idx] | ||
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| return singular_values |
| return A | ||
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| @jit | ||
| def jacobi_svd(A, tol=1e-10, max_iter=5): |
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麻烦增加一些必要的注释信息，起码需要包括：
- 明文下原始算法名称（最好包括ref）
- (optional)若有与明文算法不同处，可以简单概括并说明一下
- 涉及的参数介绍 以及 返回值介绍
| print("Start Jacobi SVD emulation.") | ||
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| def proc_transform(A): | ||
| singular_values = jacobi_svd(A) |
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tips：默认配置下，FM64的fxp_bits = 18, 故最小的非0正数为2**-18，如果有稳定性问题，可以试试把tol改大一些？
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请尽量把算法实现定义在jacobi_svd.py中，在emul文件中仅需要import即可
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另外，请先sign CLA： 复制一下上图红框里的话comment一下即可 |
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PLUS: SPU重构了构建系统，python代码的构建推荐使用wrapper函数 |
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| n = 10 | ||
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| A_jax = generate_symmetric_matrix(n) |
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这个算法只适用于对称矩阵么？如果是的话也可以直接在docstring里注明一下。。
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MPC Quantized Machine Learning- Jacobi SVD
Pull Request
What problem does this PR solve?
Issue Number: Fixed #
Possible side effects?
Performance:
Backward compatibility: