[GitHub] szha commented on a change in pull request #7903: Refactor AdaGrad optimizer to support sparse tensors

[gi...@git.apache.org]

szha commented on a change in pull request #7903: Refactor AdaGrad optimizer to support sparse tensors
URL: https://github.com/apache/incubator-mxnet/pull/7903#discussion_r139514542
 
 

 ##########
 File path: python/mxnet/optimizer.py
 ##########
 @@ -665,26 +667,46 @@ class AdaGrad(Optimizer):
     eps: float, optional
         Small value to avoid division by 0.
     """
-    def __init__(self, eps=1e-7, **kwargs):
+    def __init__(self, eps=1e-7, stype='default', **kwargs):
         super(AdaGrad, self).__init__(**kwargs)
         self.float_stable_eps = eps
+        self.stype = stype
 
     def create_state(self, index, weight):
-        return zeros(weight.shape, weight.context)  # history
+        return zeros(weight.shape, weight.context, stype=self.stype)  # history
 
     def update(self, index, weight, grad, state):
+        #print("ENTER ADAGRAD UPDATE")
         assert(isinstance(weight, NDArray))
         assert(isinstance(grad, NDArray))
         self._update_count(index)
         lr = self._get_lr(index)
         wd = self._get_wd(index)
-
+        save_grad_stype = grad.stype
         grad = grad * self.rescale_grad
         if self.clip_gradient is not None:
             grad = clip(grad, -self.clip_gradient, self.clip_gradient)
         history = state
-        history[:] += (grad * grad)
-        weight[:] += -lr * (grad / sqrt(history + self.float_stable_eps) + wd * weight)
+        save_history_stype = history.stype
+
+        is_sparse = True if weight.stype != 'default' or grad.stype != 'default' else False
 
 Review comment:
   `x = True if cond else False` <=> `x = cond`
 
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