!8269 forward unique dynamic shape

From: @yao_yf
Reviewed-by: 
Signed-off-by:

mindspore/ccsrc/backend/kernel_compiler/host/dynamic_shape_kernel.cc

@@ -27,7 +27,7 @@ void DynamicShapeKernel::Execute() {
   }
 
   auto prev_output_shape = AnfAlgo::GetPrevNodeOutputInferShape(cnode_ptr_, 0);
-  auto output_shape = std::vector<int64_t>(SizeToLong(prev_output_shape.size()));
+  std::vector<int64_t> output_shape = {SizeToLong(prev_output_shape.size())};
 
   auto output_type = TypeId::kNumberTypeInt64;
 

mindspore/ccsrc/backend/optimizer/pass/convert_const_input_to_attr.cc

@@ -62,7 +62,7 @@ const AnfNodePtr ConvertConstInputToAttr::Process(const FuncGraphPtr &, const An
         continue;
       }
     }
-    if (AnfAlgo::IsNodeDynamicShape(cnode) &&
+    if (AnfAlgo::IsDynamicShape(cnode) &&
         DynamicShapeConstInputToAttr.find(AnfAlgo::GetCNodeName(cnode)) == DynamicShapeConstInputToAttr.end()) {
       MS_LOG(INFO) << "current node is dynamic shape " << cnode->fullname_with_scope();
       continue;

mindspore/ccsrc/backend/session/ascend_session.cc

@@ -569,7 +569,6 @@ void AscendSession::BuildDynamicKernel(const std::shared_ptr<KernelGraph> &kerne
 void AscendSession::MemoryAlloc(KernelGraph *kernel_graph) const {
   MS_LOG(INFO) << "Start!";
   MS_EXCEPTION_IF_NULL(kernel_graph);
-  opt::RemoveNopNode(kernel_graph);
   auto runtime_instance = device::KernelRuntimeManager::Instance().GetKernelRuntime(kAscendDevice, device_id_);
   MS_EXCEPTION_IF_NULL(runtime_instance);
   runtime_instance->AssignMemory(kernel_graph);

mindspore/ccsrc/runtime/device/kernel_runtime.cc

@@ -471,7 +471,21 @@ bool KernelRuntime::KernelMemNotReuse(const AnfNodePtr &node) { return false; }
 
 DeviceAddressPtr KernelRuntime::PreAssignCNodeMemory(const AnfNodePtr &anf_node, size_t index) {
   MS_EXCEPTION_IF_NULL(anf_node);
+  if (!anf_node->isa<CNode>()) {
+    MS_LOG(EXCEPTION) << "anf_node should be a cnode";
+  }
+  auto cnode = anf_node->cast<CNodePtr>();
+  if (opt::IsNopNode(cnode)) {
+    size_t kNopNodeInputSize = 2;
+    size_t kNopNodeRealInputIndex = 1;
+    if (cnode->size() != kNopNodeInputSize) {
+      MS_LOG(EXCEPTION) << cnode->fullname_with_scope() << " has invalid input size: " << cnode->size();
+    }
+    auto input_node_with_index = AnfAlgo::GetPrevNodeOutput(anf_node, index);
+    return PreAssignCNodeMemory(cnode->input(kNopNodeRealInputIndex), input_node_with_index.second);
+  }
   auto kernel_mod = AnfAlgo::GetKernelMod(anf_node);
+  MS_EXCEPTION_IF_NULL(kernel_mod);
   auto output_sizes = kernel_mod->GetOutputSizeList();
   if (output_sizes.size() <= index) {
     MS_LOG(EXCEPTION) << "Previous node output size < node index";

mindspore/common/parameter.py

@@ -126,6 +126,7 @@ class Parameter(MetaTensor_):
         self.is_param_ps = False
         self._cast_type = None
         self.init_in_server = False
+        self._unique = False
         self.is_in_parallel = _is_in_parallel_mode()
 
     @staticmethod
@@ -238,6 +239,15 @@ class Parameter(MetaTensor_):
     def sliced(self, sliced_):
         self._sliced = sliced_
 
+    @property
+    def unique(self):
+        """whether the parameter is already unique or not."""
+        return self._unique
+
+    @unique.setter
+    def unique(self, unique_):
+        self._unique = unique_
+
     @property
     def is_init(self):
         """

mindspore/core/abstract/prim_arrays.cc

@@ -561,6 +561,17 @@ AbstractBasePtr InferImplTranspose(const AnalysisEnginePtr &, const PrimitivePtr
     result_shp.push_back(input_shp[idx]);
     indices.insert(idx);
   }
+  ShapeVector max_shp;
+  ShapeVector min_shp;
+  if (input->shape()->max_shape().size() == input_shp.size() &&
+      input->shape()->min_shape().size() == input_shp.size()) {
+    for (size_t i = 0; i < perm_vec.size(); i++) {
+      size_t idx = static_cast<size_t>(perm_vec[i]);
+      max_shp.push_back(input->shape()->max_shape()[idx]);
+      min_shp.push_back(input->shape()->min_shape()[idx]);
+    }
+    return std::make_shared<AbstractTensor>(input->element(), std::make_shared<Shape>(result_shp, min_shp, max_shp));
+  }
   return std::make_shared<AbstractTensor>(input->element(), std::make_shared<Shape>(result_shp));
 }
 

mindspore/core/abstract/prim_others.cc

@@ -405,10 +405,9 @@ AbstractBasePtr InferImplAllGather(const AnalysisEnginePtr &, const PrimitivePtr
   if (tmp_shape.empty()) {
     MS_LOG(EXCEPTION) << "shape size is 0";
   }
-  if (tmp_shape[0] % rank_size != 0) {
-    MS_LOG(EXCEPTION) << "first dimension of x should be divided by rank_size";
+  if (tmp_shape[0] > 0) {
+    tmp_shape[0] = tmp_shape[0] * rank_size;
   }
-  tmp_shape[0] = tmp_shape[0] / rank_size;
   return std::make_shared<AbstractTensor>(x->element(), std::make_shared<Shape>(tmp_shape));
 }
 

mindspore/nn/layer/embedding.py

@@ -150,6 +150,7 @@ class EmbeddingLookup(Cell):
         max_norm (Union[float, None]): A maximum clipping value. The data type must be float16, float32
                                        or None. Default: None
         sparse (bool): Using sparse mode. When 'target' is set to 'CPU', 'sparse' has to be true. Default: True.
+
     Inputs:
         - **input_indices** (Tensor) - The shape of tensor is :math:`(y_1, y_2, ..., y_S)`.
           Specifies the indices of elements of the original Tensor. Values can be out of range of embedding_table,
@@ -191,6 +192,12 @@ class EmbeddingLookup(Cell):
                                          name='embedding_table')
         parallel_mode = _get_parallel_mode()
         is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
+        self.forward_unique = False
+        self.gather_revert = P.GatherV2()
+        self.unique = P.Unique().shard(((1,),))
+        self.reshape = P.Reshape()
+        self.shape = P.Shape()
+        indices_shape_size = 2
         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")
@@ -203,18 +210,32 @@ class EmbeddingLookup(Cell):
             self.gatherv2.shard(((get_group_size(), 1), (1, get_group_size())))
             self.embeddinglookup.shard(((get_group_size(), 1), (1, get_group_size())))
         elif slice_mode == "table_row_slice" and is_auto_parallel:
-            self.gatherv2.shard(((get_group_size(), 1), (1, 1)))
-            self.embeddinglookup.shard(((get_group_size(), 1), (1, 1)))
+            if target == 'DEVICE':
+                indices_shape_size = 1
+                self.gather_revert.shard(((1, 1), (1,)))
+                self.forward_unique = True
+            indices_strategy = (1,)*indices_shape_size
+            self.gatherv2.shard(((get_group_size(), 1), indices_strategy))
+            self.embeddinglookup.shard(((get_group_size(), 1), indices_strategy))
         elif slice_mode == "table_column_slice" and is_auto_parallel:
-            self.gatherv2.shard(((1, get_group_size()), (1, 1)))
-            self.embeddinglookup.shard(((1, get_group_size()), (1, 1)))
+            if target == 'DEVICE':
+                indices_shape_size = 1
+                self.gather_revert.shard(((1, get_group_size()), (1,)))
+                self.forward_unique = True
+            indices_strategy = (1,)*indices_shape_size
+            self.gatherv2.shard(((1, get_group_size()), indices_strategy))
+            self.embeddinglookup.shard(((1, get_group_size()), indices_strategy))
         elif slice_mode == "batch_slice" and is_auto_parallel:
-            self.gatherv2.shard(((1, 1), (get_group_size(), 1)))
-            self.embeddinglookup.shard(((1, 1), (get_group_size(), 1)))
+            indices_strategy = [get_group_size()]
+            indices_strategy.extend([1]*(indices_shape_size - 1))
+            indices_strategy = tuple(indices_strategy)
+            self.gatherv2.shard(((1, 1), indices_strategy))
+            self.embeddinglookup.shard(((1, 1), indices_strategy))
         else:
             if is_auto_parallel:
                 raise ValueError("slice_mode should support mode in nn.EmbeddingLookup, but get "
                                  + str(slice_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)
@@ -224,7 +245,15 @@ class EmbeddingLookup(Cell):
         if self.target == "CPU":
             out = self.embeddinglookup(self.embedding_table, indices, 0)
         else:
-            out = self.gatherv2(self.embedding_table, indices, 0)
+            if self.forward_unique:
+                shp = self.shape(indices) + (self.embedding_size,)
+                indices_flatten = self.reshape(indices, (-1,))
+                unique_id, unique_idx = self.unique(indices_flatten)
+                weight_unique = self.gatherv2(unique_id)
+                weight_flatten = self.gather_revert(weight_unique, unique_idx, 0)
+                out = self.reshape(weight_flatten, shp)
+            else:
+                out = self.gatherv2(self.embedding_table, indices, 0)
         if self.max_norm is not None:
             axis = _make_axis_range(F.rank(indices), F.rank(out))
             clip_by_norm = ClipByNorm(axis)

mindspore/nn/optim/optimizer.py

@@ -144,6 +144,11 @@ class Optimizer(Cell):
             decay_filter = lambda x: 'beta' not in x.name and 'gamma' not in x.name
             self.decay_flags = tuple(decay_filter(x) for x in self.parameters)
             self.exec_weight_decay = self.weight_decay > 0
+        # when a parameter has been unique, there is no need do another unique in optimizer.
+        for param in self.parameters:
+            if param.unique:
+                self._unique = False
+                break
         ps_filter = lambda x: x.is_param_ps
         self.ps_parameters = tuple(ps_filter(x) for x in self.parameters)
         self.reciprocal_scale = 1.0 / loss_scale

mindspore/ops/_op_impl/tbe/__init__.py

@@ -67,6 +67,7 @@ from .mul_ds import _mul_ds_tbe
 from .real_div import _real_div_tbe
 from .real_div_ds import _real_div_ds_tbe
 from .relu import _relu_tbe
+from .relu_ds import _relu_ds_tbe
 from .relu_grad import _relu_grad_tbe
 from .relu6 import _relu6_tbe
 from .relu6_grad import _relu6_grad_tbe

mindspore/ops/_op_impl/tbe/relu_ds.py

@@ -0,0 +1,40 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""ReLU op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+relu_op_info = TBERegOp("ReLU") \
+    .fusion_type("ELEMWISE") \
+    .async_flag(False) \
+    .binfile_name("relu.so") \
+    .compute_cost(10) \
+    .kernel_name("relu") \
+    .partial_flag(True) \
+    .dynamic_shape(True) \
+    .input(0, "x", False, "required", "all") \
+    .output(0, "y", False, "required", "all") \
+    .op_pattern("formatAgnostic") \
+    .dtype_format(DataType.I8_None, DataType.I8_None) \
+    .dtype_format(DataType.I32_None, DataType.I32_None) \
+    .dtype_format(DataType.F16_None, DataType.F16_None) \
+    .dtype_format(DataType.F32_None, DataType.F32_None) \
+    .get_op_info()
+
+
+@op_info_register(relu_op_info)
+def _relu_ds_tbe():
+    """Relu TBE register"""
+    return

mindspore/ops/operations/comm_ops.py

@@ -274,7 +274,8 @@ class AllGather(PrimitiveWithInfer):
 
     def infer_shape(self, x_shape):
         validator.check_positive_int(len(x_shape), "x shape", self.name)
-        x_shape[0] = x_shape[0] * self.rank_size
+        if x_shape[0] > 0:
+            x_shape[0] = x_shape[0] * self.rank_size
         return x_shape
 
     def infer_dtype(self, x_dtype):
@@ -324,7 +325,8 @@ class _HostAllGather(PrimitiveWithInfer):
 
     def infer_shape(self, x_shape):
         validator.check_positive_int(len(x_shape), "x shape", self.name)
-        x_shape[0] = x_shape[0] * self.group_size
+        if x_shape[0] > 0:
+            x_shape[0] = x_shape[0] * self.group_size
         return x_shape
 
     def infer_dtype(self, x_dtype):

mindspore/train/serialization.py

@@ -721,7 +721,7 @@ def _merge_param_with_strategy(sliced_data, parameter_name, strategy, is_even):
 
         if field_size > 0:
             from mindspore.parallel._tensor import _reshape_param_data_with_weight
-            merged_tensor = _reshape_param_data_with_weight(all_gather_tensor, dev_mat, [field_size])
+            merged_tensor = _reshape_param_data_with_weight(all_gather_tensor, dev_mat, field_size)
 
         else:
             from mindspore.parallel._tensor import _reshape_param_data

model_zoo/official/recommend/wide_and_deep/script/run_auto_parallel_train_cluster.sh

@@ -43,7 +43,7 @@ do
     python -s ${self_path}/../train_and_eval_auto_parallel.py --data_path=$DATASET --epochs=$EPOCH_SIZE --vocab_size=$VOCAB_SIZE --emb_dim=$EMB_DIM --dropout_flag=1 --host_device_mix=1 >train_deep$i.log 2>&1 &
   elif [ $MODE == "field_slice_host_device_mix" ]; then
     python -s ${self_path}/../train_and_eval_auto_parallel.py --data_path=$DATASET --epochs=$EPOCH_SIZE --vocab_size=$VOCAB_SIZE --emb_dim=$EMB_DIM --dropout_flag=1 --host_device_mix=1 --full_batch=1 --field_slice=1 >train_deep$i.log 2>&1 &
-  elif [ $MODE == "backward_unique" ]; then
+  elif [ $MODE == "forward_unique" ]; then
     python -s ${self_path}/../train_and_eval_auto_parallel.py --data_path=$DATASET --epochs=$EPOCH_SIZE --vocab_size=$VOCAB_SIZE --emb_dim=$EMB_DIM --dropout_flag=1 --sparse=1 >train_deep$i.log 2>&1 &
   else
     python -s ${self_path}/../train_and_eval_auto_parallel.py --data_path=$DATASET --epochs=$EPOCH_SIZE --vocab_size=$VOCAB_SIZE --emb_dim=$EMB_DIM --dropout_flag=1 --host_device_mix=0 >train_deep$i.log 2>&1 &

model_zoo/official/recommend/wide_and_deep/script/start_cluster.sh

@@ -38,7 +38,7 @@ do
   user=$(get_node_user ${cluster_config_path} ${node})
   passwd=$(get_node_passwd ${cluster_config_path} ${node})
   echo "------------------${user}@${node}---------------------"
-  if [ $MODE == "host_device_mix" ] || [ $MODE == "field_slice_host_device_mix" ] || [ $MODE == "backward_unique" ]; then
+  if [ $MODE == "host_device_mix" ] || [ $MODE == "field_slice_host_device_mix" ] || [ $MODE == "forward_unique" ]; then
     ssh_pass ${node} ${user} ${passwd} "mkdir -p ${execute_path}; cd ${execute_path}; bash ${SCRIPTPATH}/run_auto_parallel_train_cluster.sh ${RANK_SIZE} ${RANK_START} ${EPOCH_SIZE} ${VOCAB_SIZE} ${EMB_DIM} ${DATASET} ${ENV_SH} ${MODE} ${RANK_TABLE_FILE}"
   else
     echo "[ERROR] mode is wrong"

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

@@ -88,7 +88,7 @@ class EvalCallBack(Callback):
     Args:
         print_per_step (int): Print loss every times. Default: 1.
     """
-    def __init__(self, model, eval_dataset, auc_metric, config, print_per_step=1, host_device_mix=False):
+    def __init__(self, model, eval_dataset, auc_metric, config, print_per_step=1):
         super(EvalCallBack, self).__init__()
         if not isinstance(print_per_step, int) or print_per_step < 0:
             raise ValueError("print_per_step must be int and >= 0.")
@@ -99,7 +99,7 @@ class EvalCallBack(Callback):
         self.aucMetric.clear()
         self.eval_file_name = config.eval_file_name
         self.eval_values = []
-        self.host_device_mix = host_device_mix
+        self.sparse = config.sparse
         self.config = config
 
     def epoch_end(self, run_context):
@@ -116,7 +116,7 @@ class EvalCallBack(Callback):
                              ParallelMode.DATA_PARALLEL):
             rank_id = get_rank()
         start_time = time.time()
-        out = self.model.eval(self.eval_dataset, dataset_sink_mode=(not self.host_device_mix))
+        out = self.model.eval(self.eval_dataset, dataset_sink_mode=(not self.sparse))
         end_time = time.time()
         eval_time = int(end_time - start_time)
 

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

@@ -48,6 +48,7 @@ def argparse_init():
     parser.add_argument("--parameter_server", type=int, default=0, help="Open parameter server of not")
     parser.add_argument("--field_slice", type=int, default=0, help="Enable split field mode or not")
     parser.add_argument("--sparse", type=int, default=0, help="Enable sparse or not")
+    parser.add_argument("--deep_table_slice_mode", type=str, default="column_slice", help="column_slice/row_slice")
     return parser
 
 
@@ -86,6 +87,7 @@ class WideDeepConfig():
         self.field_slice = False
         self.manual_shape = None
         self.sparse = False
+        self.deep_table_slice_mode = "column_slice"
 
     def argparse_init(self):
         """
@@ -121,5 +123,6 @@ class WideDeepConfig():
         self.parameter_server = args.parameter_server
         self.field_slice = bool(args.field_slice)
         self.sparse = bool(args.sparse)
+        self.deep_table_slice_mode = args.deep_table_slice_mode
         if self.host_device_mix == 1:
             self.sparse = True

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

@@ -198,19 +198,29 @@ class WideDeepModel(nn.Cell):
         self.tile = P.Tile()
         self.concat = P.Concat(axis=1)
         self.cast = P.Cast()
+        self.unique = P.Unique().shard(((1,),))
+        self.wide_gatherv2 = P.GatherV2()
+        self.deep_gatherv2 = P.GatherV2()
         if is_auto_parallel and sparse and not is_field_slice:
-            self.dense_layer_1.dropout.dropout_do_mask.shard(((1, get_group_size()),))
-            self.dense_layer_1.dropout.dropout.shard(((1, get_group_size()),))
-            self.dense_layer_1.matmul.shard(((1, get_group_size()), (get_group_size(), 1)))
             target = 'DEVICE'
             if host_device_mix:
                 target = 'CPU'
-            self.deep_embeddinglookup = nn.EmbeddingLookup(self.vocab_size, self.emb_dim, target=target,
-                                                           slice_mode=nn.EmbeddingLookup.TABLE_COLUMN_SLICE)
             self.wide_embeddinglookup = nn.EmbeddingLookup(self.vocab_size, 1, target=target,
                                                            slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE)
-            self.deep_mul.shard(((1, 1, get_group_size()), (1, 1, 1)))
-            self.deep_reshape.add_prim_attr("skip_redistribution", True)
+            if target == 'DEVICE':
+                self.wide_mul.shard(((1, 1, 1), (1, 1, 1)))
+            if config.deep_table_slice_mode == "column_slice":
+                self.deep_embeddinglookup = nn.EmbeddingLookup(self.vocab_size, self.emb_dim, target=target,
+                                                               slice_mode=nn.EmbeddingLookup.TABLE_COLUMN_SLICE)
+                self.dense_layer_1.dropout.dropout_do_mask.shard(((1, get_group_size()),))
+                self.dense_layer_1.dropout.dropout.shard(((1, get_group_size()),))
+                self.dense_layer_1.matmul.shard(((1, get_group_size()), (get_group_size(), 1)))
+                self.dense_layer_1.matmul.add_prim_attr("field_size", self.field_size)
+                self.deep_mul.shard(((1, 1, get_group_size()), (1, 1, 1)))
+                self.deep_reshape.add_prim_attr("skip_redistribution", True)
+            else:
+                self.deep_embeddinglookup = nn.EmbeddingLookup(self.vocab_size, self.emb_dim, target=target,
+                                                               slice_mode=nn.EmbeddingLookup.TABLE_ROW_SLICE)
             self.reduce_sum.add_prim_attr("cross_batch", True)
             self.embedding_table = self.deep_embeddinglookup.embedding_table
         elif is_auto_parallel and host_device_mix and is_field_slice and config.full_batch and config.manual_shape:
@@ -247,13 +257,15 @@ class WideDeepModel(nn.Cell):
             id_hldr: batch ids;
             wt_hldr: batch weights;
         """
-        mask = self.reshape(wt_hldr, (self.batch_size, self.field_size, 1))
         # Wide layer
         wide_id_weight = self.wide_embeddinglookup(id_hldr)
+        # Deep layer
+        deep_id_embs = self.deep_embeddinglookup(id_hldr)
+        mask = self.reshape(wt_hldr, (self.batch_size, self.field_size, 1))
+        # Wide layer
         wx = self.wide_mul(wide_id_weight, mask)
         wide_out = self.reshape(self.reduce_sum(wx, 1) + self.wide_b, (-1, 1))
         # Deep layer
-        deep_id_embs = self.deep_embeddinglookup(id_hldr)
         vx = self.deep_mul(deep_id_embs, mask)
         deep_in = self.deep_reshape(vx, (-1, self.field_size * self.emb_dim))
         deep_in = self.dense_layer_1(deep_in)
@@ -333,7 +345,8 @@ class TrainStepWrap(nn.Cell):
         parameter_server (Bool): Whether run in parameter server mode. Default: False
     """
 
-    def __init__(self, network, sens=1024.0, host_device_mix=False, parameter_server=False, sparse=False):
+    def __init__(self, network, sens=1024.0, host_device_mix=False, parameter_server=False,
+                 sparse=False):
         super(TrainStepWrap, self).__init__()
         parallel_mode = context.get_auto_parallel_context("parallel_mode")
         is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)

model_zoo/official/recommend/wide_and_deep/train_and_eval_auto_parallel.py

@@ -40,8 +40,8 @@ def get_WideDeep_net(config):
     WideDeep_net = WideDeepModel(config)
     loss_net = NetWithLossClass(WideDeep_net, config)
     loss_net = VirtualDatasetCellTriple(loss_net)
-    train_net = TrainStepWrap(
-        loss_net, host_device_mix=bool(config.host_device_mix), sparse=config.sparse)
+    train_net = TrainStepWrap(loss_net, host_device_mix=bool(config.host_device_mix),
+                              sparse=config.sparse)
     eval_net = PredictWithSigmoid(WideDeep_net)
     eval_net = VirtualDatasetCellTriple(eval_net)
     return train_net, eval_net
@@ -122,7 +122,7 @@ def train_and_eval(config):
                   metrics={"auc": auc_metric})
 
     eval_callback = EvalCallBack(
-        model, ds_eval, auc_metric, config, host_device_mix=host_device_mix)
+        model, ds_eval, auc_metric, config)
 
     callback = LossCallBack(config=config, per_print_times=20)
     ckptconfig = CheckpointConfig(save_checkpoint_steps=ds_train.get_dataset_size()*epochs,
@@ -146,7 +146,7 @@ if __name__ == "__main__":
     context.set_context(variable_memory_max_size="24GB")
     context.set_context(enable_sparse=True)
     init()
-    if wide_deep_config.host_device_mix == 1:
+    if wide_deep_config.sparse:
         context.set_auto_parallel_context(
             parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=True)
     else:

tests/st/model_zoo_tests/wide_and_deep/python_file_for_ci/config.py

@@ -37,6 +37,8 @@ def argparse_init():
     parser.add_argument("--ckpt_path", type=str, default="./checkpoints/")
     parser.add_argument("--eval_file_name", type=str, default="eval.log")
     parser.add_argument("--loss_file_name", type=str, default="loss.log")
+    parser.add_argument("--sparse", type=int, default=0, help="Enable sparse or not")
+    parser.add_argument("--deep_table_slice_mode", type=str, default="column_slice", help="column_slice/row_slice")
     return parser
 
 
@@ -66,6 +68,8 @@ class WideDeepConfig():
         self.loss_file_name = "loss.log"
         self.ckpt_path = "./checkpoints/"
         self.stra_ckpt = "./strategy_train.ckpt"
+        self.sparse = False
+        self.deep_table_slice_mode = "column_slice"
 
     def argparse_init(self):
         """
@@ -94,3 +98,7 @@ class WideDeepConfig():
         self.loss_file_name = args.loss_file_name
         self.ckpt_path = args.ckpt_path
         self.stra_ckpt = args.stra_ckpt
+        self.sparse = bool(args.sparse)
+        self.deep_table_slice_mode = args.deep_table_slice_mode
+        if self.host_device_mix == 1:
+            self.sparse = True

tests/ut/python/parallel/test_dynamic_shape.py

@@ -93,7 +93,7 @@ def test_unique_row_split():
             self.embedding_lookp = P.GatherV2().shard(((8, 1), (1,)))
             self.embedding_table = Parameter(initializer('normal', [2000, 128]),
                                              name='embedding_table')
-            self.gatherv2 = P.GatherV2().shard(((1, 1), (8,)))
+            self.gatherv2 = P.GatherV2().shard(((1, 1), (1,)))
             self.reshape = P.Reshape()
             self.matmul = P.MatMul()
             self.mul_weight = Parameter(Tensor(np.full([32, 64, 1], 0.5, dtype=np.float32)), name="mul_weight")
@@ -108,7 +108,7 @@ def test_unique_row_split():
             return vx
 
     size = 8
-    context.set_auto_parallel_context(device_num=size, global_rank=0, parallel_mode="stand_alone")
+    context.set_auto_parallel_context(device_num=size, global_rank=0, parallel_mode="semi_auto_parallel")
     x = Tensor(np.ones([32, 64]), dtype=ms.int32)
     net = Net()
     optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)