Move learning rate and releated op to pserver #8209
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typhoonzero
reviewed
Feb 7, 2018
| persistable=var.persistable, | ||
| dtype=var.dtype, | ||
| shape=var.shape) | ||
| pserver_program.global_block().create_var( |
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Can add a FIXME here, should only create var once.
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| def _is_opt_op(self, op): | ||
| # optimize op: SGDOptimize, MomentumOptimizer, AdamOptimizer and etc... | ||
| if op.inputs and op.inputs.has_key("Param") \ |
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Add comment HACK, this is a hack.
| opt_op_on_pserver.append(op) | ||
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| for _, op in enumerate(self.optimize_ops): | ||
| if not True in [ |
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not True in [] is still confusing.
| pserver_program.global_block().create_var( | ||
| name="%s.trainer_%d" % (v.name, trainer_id), | ||
| persistable=True, | ||
| dtype=v.dtype, | ||
| shape=v.shape) | ||
| # step6 | ||
| optimize_sub_program = Program() | ||
| ufind = self._create_ufind(self.optimize_ops) |
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Add comments or document about why we need ufind.
Yancey1989
changed the title
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I'll separate return all optimize and related ops feature into another PR #8280 |
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Fixed #8032