raise RuntimeError when using full_batch neither under semi_auto_parallel nor auto_parallel

mindspore/common/api.py

@@ -23,7 +23,7 @@ from mindspore import log as logger
 from .._c_expression import generate_key, Executor_, Tensor, MetaTensor, PynativeExecutor_
 from .._c_expression import verify_inputs_signature, init_exec_dataset, _set_dataset_mode_config, init_backend
 from .tensor import Tensor as MsTensor
-from ..parallel._utils import _get_device_num, _get_global_rank, _need_to_full, _to_full_tensor
+from ..parallel._utils import _get_device_num, _get_global_rank, _need_to_full, _check_full_batch, _to_full_tensor
 from ..parallel._ps_context import _is_role_pserver
 # store ms_function class compiled pipeline cache
 ms_compile_cache = {}
@@ -384,6 +384,7 @@ class _Executor:
             Bool, if the graph has been compiled before, return False, else return True.
         """
         obj.check_names()
+        _check_full_batch()
         args_names, args_list = _generate_pip_args(obj, *args)
         dic = dict(zip(args_names, args_list))
         key = generate_key(phase, dic)

mindspore/parallel/_utils.py

@@ -32,6 +32,18 @@ def _get_full_batch():
     """Get whether to use full_batch."""
     return auto_parallel_context().get_full_batch()
 
+def _check_full_batch():
+    """
+    full_batch could only be used under semi_auto_parallel or auto_parallel, check it.
+
+    Raises:
+        RuntimeError: Using full_batch under neither semi_auto_parallel nor auto_parallel.
+    """
+    parallel_mode = _get_parallel_mode()
+    full_batch = _get_full_batch()
+    if ((parallel_mode not in ("semi_auto_parallel", "auto_parallel")) and full_batch):
+        raise RuntimeError("full_batch could only be used under semi_auto_parallel or auto_parallel.")
+
 
 def _need_to_full():
     """Check whether to convert input to full shape or tensor."""

tests/ut/python/parallel/test_full_batch.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import numpy as np
+import pytest
 
 import mindspore as ms
 import mindspore.nn as nn
@@ -88,3 +89,22 @@ def test_all_to_all():
     strategy1 = ((8, 1),)
     _reset_op_id()
     all_to_all_common(strategy1)
+
+def test_data_parallel_mode():
+    _reset_op_id()
+    learning_rate = 0.1
+    momentum = 0.9
+    epoch_size = 2
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
+    context.reset_auto_parallel_context()
+    context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, full_batch=True)
+    predict = Tensor(np.ones([256, 128]), dtype=ms.float32)
+    label = Tensor(np.ones([256]), dtype=ms.int32)
+    dataset = Dataset(predict, label, 2)
+    net = all_to_all_net(None)
+    loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    opt = Momentum(net.trainable_params(), learning_rate, momentum)
+    model = Model(net, loss, opt)
+
+    with pytest.raises(RuntimeError):
+        model.train(epoch_size, dataset, dataset_sink_mode=False)