Skip updating not trainable parameters in distribute transpiler #10049
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typhoonzero
requested review from
jacquesqiao and
Yancey1989
and removed request for
jacquesqiao
Yancey1989
reviewed
Apr 19, 2018
| @@ -222,8 +222,14 @@ def transpile(self, | |||
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| # step1: For large parameters and gradients, split them into smaller | |||
| # blocks. | |||
| param_list = [pg[0] for pg in params_grads] | |||
| grad_list = [pg[1] for pg in params_grads] | |||
| param_list = [] | |||
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Maybe we can use filter for the shorter code:
param_list = filter(lambda x[0] : type(x[0]) == parameter and x[0].trainable == True , params_grads)There was a problem hiding this comment.
Well, this way actually run 2 for loops and current implementation just run 1for loop
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Fix #10014