Fix dist train with rmsprop (#12649)

* fix dist train with rmsprop

* add rmsprop transpiler test

* update by comment

python/paddle/fluid/tests/unittests/test_dist_transpiler.py

@@ -536,5 +536,35 @@ class TestAsyncDistLookupTable(TestDistLookupTableBase):
         self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
 
 
+class TestRMSPropOptimizer(TranspilerTest):
+    def net_conf(self):
+        x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
+        y_predict = fluid.layers.fc(input=x,
+                                    size=1000,
+                                    act=None,
+                                    param_attr=fluid.ParamAttr(name='fc_w'),
+                                    bias_attr=fluid.ParamAttr(name='fc_b'))
+        y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+        cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+        avg_cost = fluid.layers.mean(cost)
+        optimizer = fluid.optimizer.RMSProp(learning_rate=0.1)
+        optimizer.minimize(avg_cost)
+        return
+
+    def transpiler_test_impl(self):
+        pserver, startup = self.get_pserver(self.pserver1_ep)
+        pserver2, startup2 = self.get_pserver(self.pserver2_ep)
+
+        self.assertEqual(len(pserver.blocks), 3)
+        # block1~2: optimize pass
+        self.assertEqual([op.type for op in pserver.blocks[1].ops],
+                         ["sum", "scale", "rmsprop"])
+        # the variable #fc_w will be split into two blocks
+        fc_w_var = startup.global_block().var("fc_w.block1")
+        self.assertEqual(fc_w_var.shape, (500, 1000))
+        moment_var = startup.global_block().var("momentum_1")
+        self.assertEqual(moment_var.shape, (500, 1000))
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -1182,18 +1182,39 @@ class DistributeTranspiler(object):
         program = optimize_block.program
         pserver_block = program.global_block()
         new_inputs = dict()
+
         # update param/grad shape first, then other inputs like
         # moment can use the updated shape
-        for key in opt_op.input_names:
-            if key == "Grad":
-                new_inputs[key] = merged_var
-            elif key == "Param":
+        def _get_param_block(opt_op):
             # param is already created on global program
             param_block = None
             for p in self.param_grad_ep_mapping[endpoint]["params"]:
-                    if same_or_split_var(p.name, opt_op.input(key)[0]):
+                if same_or_split_var(p.name, opt_op.input("Param")[0]):
                     param_block = p
                     break
+            return param_block
+
+        for key in opt_op.input_names:
+            if key == "Grad":
+                new_inputs[key] = merged_var
+            # For RMSProp optimizer
+            elif key == "Moment" or key == "MeanSquare":
+                param_block = _get_param_block(opt_op)
+                if not param_block:
+                    return
+                moment_var = origin_program.global_block().vars[opt_op.input(
+                    key)[0]]
+                tmpvar = pserver_block.create_var(
+                    name=moment_var.name,
+                    persistable=moment_var.persistable,
+                    dtype=moment_var.dtype,
+                    # change to use same shape as param
+                    # TODO(typhoonzero): didn't append .block in the var name,
+                    # may affect checkpoint saving? Need to verify.
+                    shape=param_block.shape)
+                new_inputs[key] = tmpvar
+            elif key == "Param":
+                param_block = _get_param_block(opt_op)
                 if not param_block:
                     return
                 tmpvar = pserver_block.create_var(
@@ -1219,7 +1240,7 @@ class DistributeTranspiler(object):
 
         for key in opt_op.input_names:
             new_shape = None
-            if key in ["Param", "Grad", "LearningRate"]:
+            if key in ["Param", "Grad", "LearningRate", "Moment", "MeanSquare"]:
                 continue
             var = self.origin_program.global_block().vars[opt_op.input(key)[0]]
             # update accumulator variable shape