[Eager] Support sharding_parallel under eager (#42910)

python/paddle/distributed/fleet/utils/hybrid_parallel_util.py

@@ -162,29 +162,36 @@ def sharding_reduce_gradients(parameter_list, hcg):
         sharding_nrank = hcg.get_sharding_parallel_group().nranks
         for param in parameter_list:
             if param.trainable and (param._grad_ivar() is not None):
-
-                g_var = param._grad_ivar()
-
-                # need use trace_op to allreduce 
-                # paddle.distributed.all_reduce(
-                #     g_var, group=hcg.get_sharding_parallel_group(), use_calc_stream=True)
-                paddle.fluid.framework._dygraph_tracer().trace_op(
-                    type="c_allreduce_sum",
-                    inputs={'X': g_var},
-                    outputs={'Out': g_var},
-                    attrs={
-                        'ring_id': hcg.get_sharding_parallel_group().id,
-                        'use_calc_stream': True
-                    })
-
-                # grad / sharding_rank
-                div_factor = paddle.to_tensor(sharding_nrank, dtype=g_var.dtype)
-                paddle.fluid.framework._dygraph_tracer().trace_op(
-                    type="elementwise_div",
-                    inputs={'X': g_var,
-                            'Y': div_factor},
-                    outputs={'Out': g_var},
-                    attrs={'axis': -1})
+                if in_dygraph_mode():
+                    param.grad.scale_(1.0 / sharding_nrank)
+                    paddle.distributed.all_reduce(
+                        param.grad,
+                        group=hcg.get_sharding_parallel_group(),
+                        use_calc_stream=True)
+
+                elif _in_legacy_dygraph():
+                    g_var = param._grad_ivar()
+                    # need use trace_op to allreduce 
+                    # paddle.distributed.all_reduce(
+                    #     g_var, group=hcg.get_sharding_parallel_group(), use_calc_stream=True)
+                    paddle.fluid.framework._dygraph_tracer().trace_op(
+                        type="c_allreduce_sum",
+                        inputs={'X': g_var},
+                        outputs={'Out': g_var},
+                        attrs={
+                            'ring_id': hcg.get_sharding_parallel_group().id,
+                            'use_calc_stream': True
+                        })
+
+                    # grad / sharding_rank
+                    div_factor = paddle.to_tensor(
+                        sharding_nrank, dtype=g_var.dtype)
+                    paddle.fluid.framework._dygraph_tracer().trace_op(
+                        type="elementwise_div",
+                        inputs={'X': g_var,
+                                'Y': div_factor},
+                        outputs={'Out': g_var},
+                        attrs={'axis': -1})
 
 
 def broadcast_sharding_parameters(model, hcg):

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

@@ -25,8 +25,7 @@ class TestHybridParallel(TestMultipleGpus):
 
     # check sharding logic as well as the accuracy with single mode
     def test_hybrid_parallel_sharding_logic(self):
-        # self.run_mnist_2gpu(
-        #     'hybrid_parallel_sharding_model.py')
+        self.run_mnist_2gpu('hybrid_parallel_sharding_model.py')
         self.run_mnist_2gpu(
             'hybrid_parallel_sharding_model.py', eager_mode=False)