Upgrade paddle API used in mixed precision training (#227)

ppdet/experimental/mixed_precision.py

@@ -129,30 +129,27 @@ class DynamicLossScale(LossScale):
     def increment(self):
         enough_steps = layers.less_than(self.increment_every,
                                         self.good_steps + 1)
-        with layers.Switch() as switch:
-            with switch.case(enough_steps):
-                new_scale = self.scale * self.factor
-                scale_valid = layers.isfinite(new_scale)
-                with layers.Switch() as switch2:
-                    with switch2.case(scale_valid):
-                        layers.assign(new_scale, self.scale)
-                        layers.assign(
-                            layers.zeros_like(self.good_steps), self.good_steps)
-                    with switch2.default():
-                        layers.increment(self.good_steps)
-            with switch.default():
-                layers.increment(self.good_steps)
+
+        def increment_step():
+            layers.increment(self.good_steps)
+
+        def maybe_update():
+            new_scale = self.scale * self.factor
+            scale_valid = layers.isfinite(new_scale)
+
+            def update_scale_and_step():
+                layers.assign(new_scale, self.scale)
+                layers.assign(
+                    layers.zeros_like(self.good_steps), self.good_steps)
+
+            layers.cond(scale_valid, update_scale_and_step)
+
+        layers.cond(enough_steps, maybe_update, increment_step)
 
     def decrement(self):
         new_scale = self.scale / self.factor
         one = layers.fill_constant(shape=[1], dtype='float32', value=1.0)
-        less_than_one = layers.less_than(new_scale, one)
-        with layers.Switch() as switch:
-            with switch.case(less_than_one):
-                layers.assign(one, self.scale)
-            with switch.default():
-                layers.assign(new_scale, self.scale)
-
+        layers.assign(layers.elementwise_max(new_scale, one), self.scale)
         layers.assign(layers.zeros_like(self.good_steps), self.good_steps)
 
 
@@ -275,12 +272,13 @@ def scale_gradient(block, context):
         fwd_var = block._var_recursive(context[name])
         if not isinstance(fwd_var, Parameter):
             continue  # TODO verify all use cases
-        clip_op_desc = block.desc.append_op()
-        clip_op_desc.set_type("elementwise_div")
-        clip_op_desc.set_input("X", [name])
-        clip_op_desc.set_input("Y", [scale.name])
-        clip_op_desc.set_output("Out", [name])
-        clip_op_desc._set_attr(op_role_attr_name, bwd_role)
+        scale_op_desc = block.desc.append_op()
+        scale_op_desc.set_type("elementwise_div")
+        scale_op_desc.set_input("X", [name])
+        scale_op_desc.set_input("Y", [scale.name])
+        scale_op_desc.set_output("Out", [name])
+        scale_op_desc._set_attr("axis", -1)
+        scale_op_desc._set_attr(op_role_attr_name, bwd_role)
 
 
 def update_loss_scale(grads):
@@ -289,12 +287,8 @@ def update_loss_scale(grads):
         return
     per_grad_check = layers.stack([layers.reduce_sum(g) for g in grads])
     grad_valid = layers.isfinite(per_grad_check)
-
-    with layers.Switch() as switch:
-        with switch.case(grad_valid):
-            state.increment()
-        with switch.default():
-            state.decrement()
+    layers.cond(grad_valid, lambda: state.increment(),
+                lambda: state.decrement())
     return grad_valid
 
 
@@ -309,15 +303,15 @@ def backward(self, loss, **kwargs):
     else:
         kwargs['callbacks'] = callbacks
     param_grads = self._backward(loss, **kwargs)
+
+    def zero_grad():
+        for _, g in param_grads:
+            layers.assign(layers.zeros_like(g), g)
+
     if state is not None:
         grad_valid = update_loss_scale(v for k, v in param_grads)
         if state.dynamic_scaling:
-            with layers.Switch() as switch:
-                with switch.case(grad_valid):
-                    pass
-                with switch.default():
-                    for _, g in param_grads:
-                        layers.assign(layers.zeros_like(g), g)
+            layers.cond(grad_valid, None, zero_grad)
 
     return param_grads