[GitHub] sxjscience commented on a change in pull request #9262: FTML optimizer implementation

[GitBox <gi...@apache.org>]

sxjscience commented on a change in pull request #9262: FTML optimizer implementation
URL: https://github.com/apache/incubator-mxnet/pull/9262#discussion_r159146034
 
 

 ##########
 File path: tests/python/unittest/test_optimizer.py
 ##########
 @@ -333,6 +333,75 @@ def test_sparse_sgd():
                             compare_optimizer(opt1(**kwarg), opt2(**kwarg), shape, dtype,
                                               w_stype='row_sparse', g_stype='row_sparse')
 
+
+# FTML
+
+class PyFTML(mx.optimizer.Optimizer):
+    """python reference implemenation of FTML"""
+    def __init__(self, beta1=0.6, beta2=0.999, epsilon=1e-8, **kwargs):
+        super(PyFTML, self).__init__(**kwargs)
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.epsilon = epsilon
+
+    def create_state(self, index, weight):
+        return (mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype), # d_0
+                mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype), # v_0
+                mx.nd.zeros(weight.shape, weight.context, dtype=weight.dtype)) # z_0
+
+    def update(self, index, weight, grad, state):
+        assert(isinstance(weight, mx.nd. NDArray))
+        assert(isinstance(grad, mx.nd.NDArray))
+        self._update_count(index)
+        lr = self._get_lr(index)
+        wd = self._get_wd(index)
+        t = self._index_update_count[index]
+
+        grad = grad * self.rescale_grad
+        if self.clip_gradient is not None:
+            grad = mx.nd.clip(grad, -self.clip_gradient, self.clip_gradient)
+        grad += wd * weight 
 
 Review comment:
   We should clip after adding the gradient of L2. This is consistent with other optimizers.

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