!5841 EmbeddingLookupSplitMode from nn move to parallel._utils

Merge pull request !5841 from yao_yf/EmbeddingLookUpSplitMode_move_to_parallel

mindspore/nn/layer/embedding.py

@@ -25,7 +25,7 @@ from mindspore.parallel._utils import _get_parallel_mode
 from ..cell import Cell
 from ..._checkparam import Validator as validator, Rel
 
-__all__ = ['Embedding', 'EmbeddingLookup', 'EmbeddingLookUpSplitMode']
+__all__ = ['Embedding', 'EmbeddingLookup']
 
 class Embedding(Cell):
     r"""
@@ -131,7 +131,7 @@ class EmbeddingLookup(Cell):
         target (str): Specify the target where the op is executed. The value should in
             ['DEVICE', 'CPU']. Default: 'CPU'.
         slice_mode (str): The slicing way in semi_auto_parallel/auto_parallel. The value should get through
-            nn.EmbeddingLookUpSplitMode. Default: nn.EmbeddingLookUpSplitMode.BATCH_SLICE.
+            nn.EmbeddingLookup. Default: nn.EmbeddingLookup.BATCH_SLICE.
         manual_shapes (tuple): The accompaniment array in field slice mode.
 
     Inputs:
@@ -147,6 +147,11 @@ class EmbeddingLookup(Cell):
         >>> input_indices = Tensor(np.array([[1, 0], [3, 2]]), mindspore.int32)
         >>> out = nn.EmbeddingLookup(4,2)(input_indices)
     """
+    BATCH_SLICE = "batch_slice"
+    FIELD_SLICE = "field_slice"
+    TABLE_ROW_SLICE = "table_row_slice"
+    TABLE_COLUMN_SLICE = "table_column_slice"
+
     def __init__(self, vocab_size, embedding_size, param_init='normal',
                  target='CPU', slice_mode='batch_slice', manual_shapes=None):
         super(EmbeddingLookup, self).__init__()
@@ -160,7 +165,7 @@ class EmbeddingLookup(Cell):
                                          name='embedding_table')
         parallel_mode = _get_parallel_mode()
         is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
-        if slice_mode == EmbeddingLookUpSplitMode.FIELD_SLICE and is_auto_parallel:
+        if slice_mode == "field_slice" and is_auto_parallel:
             if not manual_shapes:
                 raise ValueError("in slice field mode, the manual_shapes should not be none")
             if not isinstance(manual_shapes, tuple):
@@ -171,18 +176,18 @@ class EmbeddingLookup(Cell):
             self.embeddinglookup.add_prim_attr("manual_split", manual_shapes)
             self.gatherv2.set_strategy(((get_group_size(), 1), (1, get_group_size())))
             self.embeddinglookup.set_strategy(((get_group_size(), 1), (1, get_group_size())))
-        elif slice_mode == EmbeddingLookUpSplitMode.TABLE_ROW_SLICE and is_auto_parallel:
+        elif slice_mode == "table_row_slice" and is_auto_parallel:
             self.gatherv2.set_strategy(((get_group_size(), 1), (1, 1)))
             self.embeddinglookup.set_strategy(((get_group_size(), 1), (1, 1)))
-        elif slice_mode == EmbeddingLookUpSplitMode.TABLE_COLUMN_SLICE and is_auto_parallel:
+        elif slice_mode == "table_column_slice" and is_auto_parallel:
             self.gatherv2.set_strategy(((1, get_group_size()), (1, 1)))
             self.embeddinglookup.set_strategy(((1, get_group_size()), (1, 1)))
-        elif slice_mode == EmbeddingLookUpSplitMode.BATCH_SLICE and is_auto_parallel:
+        elif slice_mode == "batch_slice" and is_auto_parallel:
             self.gatherv2.set_strategy(((1, 1), (get_group_size(), 1)))
             self.embeddinglookup.set_strategy(((1, 1), (get_group_size(), 1)))
         else:
             if is_auto_parallel:
-                raise ValueError("slice_mode should support mode in nn.EmbeddingLookUpSplitMode, but get "
+                raise ValueError("slice_mode should support mode in nn.EmbeddingLookup, but get "
                                  + str(slice_mode))
 
     def construct(self, indices):
@@ -191,25 +196,3 @@ class EmbeddingLookup(Cell):
         else:
             out = self.gatherv2(self.embedding_table, indices, 0)
         return out
-
-
-class EmbeddingLookUpSplitMode:
-    """
-    EmbeddingLookUp slice options in auto parallel and semi auto parallel mode.
-
-    There are five kinds of slice options, "BATCH_SLICE", "FIELD_SLICE",
-    "TABLE_ROW_SLICE" and "TABLE_COLUMN_SLICE". Default: "BATCH_SLICE".
-
-        - BATCH_SLICE: Slicing batch dimensions of indices.
-        - FIELD_SLICE: Slicing field dimensions of indices.
-        - TABLE_ROW_SLICE: Slicing row of table.
-        - TABLE_COLUMN_SLICE: Slicing column of table.
-
-    MODE_LIST: The list for all supported parallel modes.
-    """
-
-    BATCH_SLICE = "batch_slice"
-    FIELD_SLICE = "field_slice"
-    TABLE_ROW_SLICE = "table_row_slice"
-    TABLE_COLUMN_SLICE = "table_column_slice"
-    MODE_LIST = [BATCH_SLICE, FIELD_SLICE, TABLE_ROW_SLICE, TABLE_COLUMN_SLICE]

model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py

@@ -202,9 +202,9 @@ class WideDeepModel(nn.Cell):
             self.dense_layer_1.dropout.dropout.set_strategy(((1, get_group_size()),))
             self.dense_layer_1.matmul.set_strategy(((1, get_group_size()), (get_group_size(), 1)))
             self.deep_embeddinglookup = nn.EmbeddingLookup(self.vocab_size, self.emb_dim,
-                                                           slice_mode=nn.EmbeddingLookUpSplitMode.TABLE_COLUMN_SLICE)
+                                                           slice_mode=nn.EmbeddingLookup.TABLE_COLUMN_SLICE)
             self.wide_embeddinglookup = nn.EmbeddingLookup(self.vocab_size, 1,
-                                                           slice_mode=nn.EmbeddingLookUpSplitMode.TABLE_ROW_SLICE)
+                                                           slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE)
             self.deep_mul.set_strategy(((1, 1, get_group_size()), (1, 1, 1)))
             self.deep_reshape.add_prim_attr("skip_redistribution", True)
             self.reduce_sum.add_prim_attr("cross_batch", True)
@@ -212,10 +212,10 @@ class WideDeepModel(nn.Cell):
         elif is_auto_parallel and host_device_mix and is_field_slice and config.full_batch and config.manual_shape:
             manual_shapes = tuple((s[0] for s in config.manual_shape))
             self.deep_embeddinglookup = nn.EmbeddingLookup(self.vocab_size, self.emb_dim,
-                                                           slice_mode=nn.EmbeddingLookUpSplitMode.FIELD_SLICE,
+                                                           slice_mode=nn.EmbeddingLookup.FIELD_SLICE,
                                                            manual_shapes=manual_shapes)
             self.wide_embeddinglookup = nn.EmbeddingLookup(self.vocab_size, 1,
-                                                           slice_mode=nn.EmbeddingLookUpSplitMode.FIELD_SLICE,
+                                                           slice_mode=nn.EmbeddingLookup.FIELD_SLICE,
                                                            manual_shapes=manual_shapes)
             self.deep_mul.set_strategy(((1, get_group_size(), 1), (1, get_group_size(), 1)))
             self.wide_mul.set_strategy(((1, get_group_size(), 1), (1, get_group_size(), 1)))