!25853 remove cache for embeddinglookup layer

Merge pull request !25853 from fangzehua/remove_cache_embedding

mindspore/nn/layer/embedding.py

@@ -181,8 +181,8 @@ class EmbeddingLookup(Cell):
                                        or None. Default: None
         sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.
         vocab_cache_size (int): Cache size of the dictionary of embeddings. Default: 0. It is valid only in
-            'DEVICE' target. And the moment parameter of corresponding optimizer will also be set to the cache size.
-            In addition, it should be noted that it will cost the 'DEVICE'
+            parameter server trainning mode and 'DEVICE' target. And the moment parameter of corresponding
+            optimizer will also be set to the cache size. In addition, it should be noted that it will cost the 'DEVICE'
             memory, so suggests setting a reasonable value to avoid insufficient memory.
 
     Inputs:
@@ -303,35 +303,13 @@ class EmbeddingLookup(Cell):
                 raise ValueError("For '{}', the 'slice_mode' must be in {}, "
                                  "but got \"{}\".".format(self.cls_name, support_mode, slice_mode))
         if self.cache_enable and not enable_ps:
-            if parallel_mode != ParallelMode.STAND_ALONE:
-                raise ValueError(f"For '{self.cls_name}', parallel mode haven't supported cache enable yet.")
-            self._set_cache_enable()
+            raise ValueError(f"For '{self.cls_name}', haven't supported cache enable for not ps mode.")
         self.embedding_table.unique = self.forward_unique
         self.max_norm = max_norm
         if self.max_norm is not None:
             self.max_norm = validator.check_positive_float(self.max_norm, 'max_norm', self.cls_name)
             self.max_norm = Tensor(self.max_norm, dtype=mstype.float32)
 
-    def _set_cache_enable(self):
-        """EmbeddingLookup cache check for not ps env, which is only support 'ascend'."""
-        if self.target != 'DEVICE':
-            raise ValueError(f"For '{self.cls_name}', the configuration of 'vocab_cache_size' is valid only "
-                             f"when 'target' is 'DEVICE', but got 'target': {self.target}")
-        if not self.sparse:
-            raise ValueError(f"For '{self.cls_name}', the configuration of 'vocab_cache_size' is valid only "
-                             f"when 'sparse' is true, but got 'sparse': {self.sparse}.")
-        if context.get_context("device_target") != 'Ascend':
-            raise ValueError(f"For '{self.cls_name}', the configuration of 'vocab_cache_size' is valid only "
-                             f"when device target is 'Ascend', but got {context.get_context('device_target')}.")
-
-        logger.info("EmbeddingLookup cache enable takes effect.")
-        self.forward_unique = True
-        self.unique = P.Unique().add_prim_attr('primitive_target', 'CPU')
-        self.unique.add_prim_attr('cache_enable', True)
-        self.embedding_table.cache_enable = self.cache_enable
-        self.embedding_table.cache_shape = (self.vocab_cache_size, self.embedding_size)
-        self.reshape_first = P.Reshape().add_prim_attr('primitive_target', 'CPU')
-
     def _process_vocab_cache(self, slice_mode):
         """PS embeddingLookup cache check and process."""
         self.cache_enable = False