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do not reverse option for HyperMap when optimizers is adam or ftrl

This commit is contained in:
Margaret_wangrui 2021-07-01 19:31:57 +08:00
parent 4703c3085f
commit 426db22c35
2 changed files with 11 additions and 11 deletions
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16
mindspore/nn/optim/adam.py
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@ -350,15 +350,15 @@ class Adam(Optimizer):
beta2_power = self.beta2_power * self.beta2
self.beta2_power = beta2_power
if self.is_group_lr:
success = self.map_reverse(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
self.use_locking, self.use_nesterov, self._is_device,
beta1_power, beta2_power, self.beta1, self.beta2, self.eps),
lr, gradients, params, moment1, moment2, self.ps_parameters, self.cache_enable)
success = self.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
self.use_locking, self.use_nesterov, self._is_device,
beta1_power, beta2_power, self.beta1, self.beta2, self.eps),
lr, gradients, params, moment1, moment2, self.ps_parameters, self.cache_enable)
else:
success = self.map_reverse(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
self.use_locking, self.use_nesterov, self._is_device,
beta1_power, beta2_power, self.beta1, self.beta2, self.eps, lr),
gradients, params, moment1, moment2, self.ps_parameters, self.cache_enable)
success = self.map_(F.partial(_adam_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
self.use_locking, self.use_nesterov, self._is_device,
beta1_power, beta2_power, self.beta1, self.beta2, self.eps, lr),
gradients, params, moment1, moment2, self.ps_parameters, self.cache_enable)
return success
@Optimizer.target.setter

6
mindspore/nn/optim/ftrl.py
View File

@ -226,9 +226,9 @@ class FTRL(Optimizer):
grads = self._grad_sparse_indices_deduplicate(grads)
lr = self.get_lr()
success = self.map_reverse(F.partial(_ftrl_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
self.l1, self.l2, self.lr_power, lr),
linear, grads, params, moments, self.ps_parameters, self.cache_enable)
success = self.map_(F.partial(_ftrl_opt, self.opt, self.sparse_opt, self._ps_push, self._ps_pull,
self.l1, self.l2, self.lr_power, lr),
linear, grads, params, moments, self.ps_parameters, self.cache_enable)
return success
@Optimizer.target.setter