change internal API _get_strategy() to _get_shard_strategy()

mindspore/common/api.py

@@ -448,7 +448,7 @@ class _Executor:
         new_param = {x.name: replace[x] for x in replace if id(x) != id(replace[x])}
         return self._executor.updata_param_node_default_input(phase, new_param)
 
-    def _get_strategy(self, obj):
+    def _get_shard_strategy(self, obj):
         real_phase = self.phase_prefix + obj.phase + '.' + str(obj.create_time)
         return self._executor.get_strategy(real_phase)
 

tests/ut/python/parallel/test_alltoall.py

@@ -86,7 +86,7 @@ def all_to_all_common(strategy1):
     model = Model(net, loss, opt)
 
     model.train(epoch_size, dataset, dataset_sink_mode=False)
-    strategys = _executor._get_strategy(model._train_network)
+    strategys = _executor._get_shard_strategy(model._train_network)
     return strategys
 
 

tests/ut/python/parallel/test_auto_parallel_arithmetic.py

@@ -76,7 +76,7 @@ def test_auto_parallel_arithmetic():
     y = Tensor(np.ones([32, 128]), dtype=ms.float32)
     b = Tensor(np.ones([64, 128]), dtype=ms.float32)
     compile_net(net, x, y, b, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     expected_strategies = {'Default/network-Net/FloorDiv-op0': [[2, 4], [2, 4]],
                            'Default/network-Net/MatMul-op1': [[2, 1], [1, 4]]}
     assert strategies == expected_strategies
@@ -103,7 +103,7 @@ def test_auto_parallel_arithmetic_broadcast_both():
     y = Tensor(np.ones([32, 1]), dtype=ms.float32)
     b = Tensor(np.ones([1, 64]), dtype=ms.float32)
     compile_net(net, x, y, b, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     expected_strategies = {'Default/network-Net/FloorDiv-op0': [[8, 1], [1, 1]],
                            'Default/network-Net/MatMul-op1': [[8, 1], [1, 1]]}
     assert strategies == expected_strategies
@@ -130,7 +130,7 @@ def test_auto_parallel_arithmetic_broadcast_right():
     y = Tensor(np.ones([32, 32]), dtype=ms.float32)
     b = Tensor(np.ones([32]), dtype=ms.float32)
     compile_net(net, x, y, b, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [2]],
                            'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]}
     assert strategies == expected_strategies
@@ -157,7 +157,7 @@ def test_auto_parallel_arithmetic_broadcast_left():
     y = Tensor(np.ones([32, 32]), dtype=ms.float32)
     b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
     compile_net(net, x, y, b, phase="train")
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     expected_strategies = {'Default/network-Net/FloorDiv-op0': [[4, 2], [1, 4, 2]],
                            'Default/network-Net/MatMul-op1': [[4, 1], [1, 2]]}
     assert strategies == expected_strategies

tests/ut/python/parallel/test_auto_parallel_assign_sub_with_ref_key.py

@@ -62,7 +62,7 @@ def test_auto_parallel_assign_sub_with_ref_key():
     reset_op_id()
 
     _executor.compile(net, x, phase="train")
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     for (k, v) in strategies.items():
         if re.search('PReLU-op', k) is not None:
             assert v == [[1, 1, 1, 8], [1]]

tests/ut/python/parallel/test_auto_parallel_cast.py

@@ -82,7 +82,7 @@ def test_double_star_graph():
     reset_op_id()
 
     _executor.compile(net, x, y, z, w, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     expected_strategies = {'Default/network-Net/Cast-op0': [[8, 1]],
                            'Default/network-Net/Cast-op1': [[1, 8]],
                            'Default/network-Net/MatMul-op3': [[8, 1], [1, 1]],

tests/ut/python/parallel/test_auto_parallel_double_subgraphs.py

@@ -113,7 +113,7 @@ def test_double_subgraphs():
     x = Tensor(np.ones([8, 8, 8, 8]), dtype=ms.float32)
     reset_op_id()
     _executor.compile(net, x, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     expected_strategies = {'Default/network-NetWithLoss/ReduceMean-op0': [[8, 1, 1, 1]],
                            'Default/network-NetWithLoss/net-Net/ReLU-op1': [[8, 1, 1, 1]],
                            'Default/network-NetWithLoss/net-Net/Mul-op2': [[8, 1, 1, 1], [8, 1, 1, 1]],
@@ -159,7 +159,7 @@ def test_double_subgraphs_train():
     ds_train = DatasetLenet(Tensor(batch_ids), None)
     model = Model(net)
     model.train(1, ds_train, dataset_sink_mode=False)
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     expected_strategies = {'Default/network-NetWithLoss/ReduceMean-op3': [[1, 1, 1, 1]],
                            'Default/network-NetWithLoss/net-Net/ReLU-op4': [[1, 1, 1, 1]],
                            'Default/network-NetWithLoss/net-Net/Mul-op5': [[1, 1, 1, 1], [1, 1, 1, 1]],

tests/ut/python/parallel/test_auto_parallel_matmul_prelu.py

@@ -75,7 +75,7 @@ def test_matmul_prelu():
     reset_op_id()
 
     _executor.compile(net, x, y, b, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     for (k, v) in strategies.items():
         if re.search('PReLU-op', k) is not None:
             assert v == [[16, 1, 1, 1], [1]]

tests/ut/python/parallel/test_auto_parallel_parameter_cast.py

@@ -69,7 +69,7 @@ def test_common_parameter():
     reset_op_id()
 
     _executor.compile(net, x, y, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     for (k, v) in strategies.items():
         if re.search('MatMul-op', k) is not None:
             assert v == [[8, 1], [1, 1]]

tests/ut/python/parallel/test_auto_parallel_resnet.py

@@ -295,7 +295,7 @@ def test_train_32k_8p(batch_size=32, num_classes=32768):
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
     model = Model(net, loss_fn=loss, optimizer=opt)
     model.train(5, dataset, dataset_sink_mode=False)
-    strategies = _executor._get_strategy(model._train_network)
+    strategies = _executor._get_shard_strategy(model._train_network)
     for (k, v) in strategies.items():
         if re.search('Conv2D-op', k) is not None:
             assert v[0][0] == dev_num
@@ -671,7 +671,7 @@ def test_train_64k_8p(batch_size=32, num_classes=65536):  # 1048576 #131072 #327
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
     model = Model(net, loss_fn=loss, optimizer=opt)
     model.train(5, dataset, dataset_sink_mode=False)
-    strategies = _executor._get_strategy(model._train_network)
+    strategies = _executor._get_shard_strategy(model._train_network)
     for (k, v) in strategies.items():
         if re.search('Conv2D-op', k) is not None:
             assert v[0][0] == dev_num
@@ -698,7 +698,7 @@ def test_train_8k_8p_gpu(batch_size=32, num_classes=8192):
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
     model = Model(net, loss_fn=loss, optimizer=opt)
     model.train(5, dataset, dataset_sink_mode=False)
-    strategies = _executor._get_strategy(model._train_network)
+    strategies = _executor._get_shard_strategy(model._train_network)
     for (k, v) in strategies.items():
         if re.search('Conv2D-op', k) is not None:
             assert v[0][0] == dev_num
@@ -724,7 +724,7 @@ def test_train_4k_8p_gpu(batch_size=32, num_classes=4096):
     opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.01, 0.9)
     model = Model(net, loss_fn=loss, optimizer=opt)
     model.train(5, dataset, dataset_sink_mode=False)
-    strategies = _executor._get_strategy(model._train_network)
+    strategies = _executor._get_shard_strategy(model._train_network)
     for (k, v) in strategies.items():
         if re.search('Conv2D-op', k) is not None:
             assert v[0][0] == dev_num

tests/ut/python/parallel/test_auto_parallel_transpose.py

@@ -77,7 +77,7 @@ def test_two_matmul_transpose():
     reset_op_id()
 
     _executor.compile(net, x, y, b, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     expected_strategies = {'Default/network-Net/Transpose-op0': [[1, 16]],
                            'Default/network-Net/Transpose-op1': [[16, 1]],
                            'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]],

tests/ut/python/parallel/test_auto_parallel_two_bn.py

@@ -78,7 +78,7 @@ def test_two_bn():
     reset_op_id()
 
     _executor.compile(net, x, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     assert len(strategies) == 4
 
     for (k, v) in strategies.items():

tests/ut/python/parallel/test_auto_parallel_two_matmul.py

@@ -135,7 +135,7 @@ def test_two_matmul():
     reset_op_id()
 
     _executor.compile(net, x, y, b, phase='train')
-    strategies = _executor._get_strategy(net)
+    strategies = _executor._get_shard_strategy(net)
     expected_strategies = {'Default/network-Net/MatMul-op0': [[16, 1], [1, 1]],
                            'Default/network-Net/MatMul-op1': [[16, 1], [1, 1]]}
     assert strategies == expected_strategies

tests/ut/python/parallel/test_one_dev.py

@@ -87,7 +87,7 @@ def all_to_all_common():
     model = Model(net, loss, opt)
 
     model.train(epoch_size, dataset, dataset_sink_mode=False)
-    strategys = _executor._get_strategy(model._train_network)
+    strategys = _executor._get_shard_strategy(model._train_network)
     return strategys