fix_stage2_minimize (#39285)

python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/sharding_optimizer_stage2.py

@@ -70,7 +70,7 @@ class ShardingOptimizerStage2(Optimizer):
                  device="gpu",
                  **kw):
 
-        # super().__init__(optim._learning_rate, params, kw)
+        super().__init__(optim._learning_rate, params, kw)
 
         # Segmentation information
         self._dtype_rank_params = OrderedDict(
@@ -363,6 +363,10 @@ class ShardingOptimizerStage2(Optimizer):
         # Synchronize all the updated shards in between the ranks
         self._broadcast_params()
 
+    def minimize(self):
+        raise RuntimeError(
+            "optimizer.minimize() not support now, please use optimizer.step()")
+
     def _clear_cache(self):
         self.__segment_params.clear()
         self._dtype_rank_params.clear()

python/paddle/distributed/fleet/meta_parallel/sharding/sharding_stage3.py

@@ -506,7 +506,13 @@ class ShardingStage3(nn.Layer):
             else:
                 opt_step()
 
+        def _opt_minimize(self):
+            raise RuntimeError(
+                "optimizer.minimize() not support now, please use optimizer.step()"
+            )
+
         self._optim.step = MethodType(_opt_step, self._optim)
+        self._optim.minimize = MethodType(_opt_minimize, self._optim)
 
     def _redefine_opt_clear(self):
         clear_func = self._clear_gradients

python/paddle/fluid/tests/unittests/dygraph_sharding_optimizer_stage2.py

@@ -127,6 +127,15 @@ def train_mlp():
             # oss_optimizer clear cache
             oss_optimizer._clear_cache()
 
+            # check optimizer.minimize() error
+            try:
+                oss_optimizer.minimize()
+            except:
+                print(
+                    "====== Find sharding_stage2_optimizer.minimize() error ======"
+                )
+            return
+
 
 if __name__ == '__main__':
     train_mlp()

python/paddle/fluid/tests/unittests/dygraph_sharding_stage3.py

@@ -83,7 +83,8 @@ def train_mlp(model,
               accumulate_grad=False,
               batch_size=100,
               opt_group=False,
-              recompute=False):
+              recompute=False,
+              test_minimize=False):
     group = paddle.distributed.new_group([0, 1])
     if opt_group:
         optimizer = optimizer_setting(
@@ -113,6 +114,15 @@ def train_mlp(model,
             accumulate_grads=batch_size == 20,
             sync_comm=recompute)
 
+    # check optimizer.minimize() error
+    if test_minimize:
+        try:
+            optimizer.minimize()
+        except:
+            print(
+                "====== Find sharding_stage3_optimizer.minimize() error ======")
+        return
+
     train_reader = paddle.batch(
         reader_decorator(), batch_size=batch_size, drop_last=True)
 
@@ -160,8 +170,8 @@ def train_mlp(model,
 
 
 def test_stage2_stage3():
-    mlp, mlp1, mlp2, mlp3, mlp4, mlp5, mlp6, mlp7, mlp8 = MLP(), MLP(), MLP(
-    ), MLP(), MLP(), MLP(), MLP(), MLP(), MLP()
+    mlp, mlp1, mlp2, mlp3, mlp4, mlp5, mlp6, mlp7, mlp8, mlp9 = MLP(), MLP(
+    ), MLP(), MLP(), MLP(), MLP(), MLP(), MLP(), MLP(), MLP()
     state_dict = mlp.state_dict()
     mlp1.set_state_dict(state_dict)
     mlp2.set_state_dict(state_dict)
@@ -171,6 +181,8 @@ def test_stage2_stage3():
     mlp6.set_state_dict(state_dict)
     mlp7.set_state_dict(state_dict)
     mlp8.set_state_dict(state_dict)
+    mlp9.set_state_dict(state_dict)
+
     # fp32 
     stage2_params = train_mlp(
         mlp1, sharding_stage=2, use_pure_fp16=False, opt_group=False)
@@ -229,7 +241,14 @@ def test_stage2_stage3():
     for i in range(len(stage3_params)):
         np.testing.assert_allclose(
             stage3_params[i].numpy(), stage3_params_re[i].numpy(), rtol=1e-6)
-    return
+
+    # check optimizer.minimize() error
+    train_mlp(
+        mlp9,
+        sharding_stage=3,
+        use_pure_fp16=False,
+        opt_group=False,
+        test_minimize=True)
 
 
 if __name__ == '__main__':