add sparse feature test cases for auto parallel

mindspore/ccsrc/frontend/parallel/ops_info/reshape_info.cc

@@ -150,6 +150,8 @@ Status ReshapeInfo::ComputeReplaceOp() {
     ConstructOperator constructor;
     replace_op_ = constructor.SkipRedisReshapeOP(output_layout_.slice_shape().array());
     replace_op_info_.clear();
+    MS_LOG(INFO) << "skip reshape redistribution and reshape slice_shape is "
+                 << ShapeToString(output_layout_.slice_shape().array());
   } else {
     RedistributionOpListPtr redistribution_oplist_ptr = tensor_redistribution.InferTensorRedistributionOperatorList();
     if (redistribution_oplist_ptr == nullptr) {

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -316,12 +316,6 @@ void Redistribution(const std::pair<AnfNodePtr, int> &node_pair, const OperatorI
   TensorLayout tensorlayout_out = tensorinfo_out.tensor_layout();
   TensorLayout tensorlayout_in = GetTensorInLayout(middle_node, middle_prim, distribute_operator);
 
-  if (tensorlayout_in.skip_redistribution() || tensorlayout_out.skip_redistribution()) {
-    MS_LOG(INFO) << "skip the reshape redistribution, operator name is" << distribute_operator->name()
-                 << "next distribute operator, operator name is" << next_distribute_operator->name();
-    return;
-  }
-
   if (tensor_redistribution.Init(tensorlayout_in, tensorlayout_out, dev_list) == FAILED) {
     MS_LOG(ERROR) << "Redistribution: middle_prim " << middle_prim->name() << " next_prim : " << next_prim_name;
     MS_LOG(ERROR) << "Redistribution: middle_node " << middle_node->ToString() << " next_node "
@@ -1380,10 +1374,6 @@ void SetClonedTensorShapeForOptimizer(const FuncGraphPtr &root) {
                         << cloned_index << ", but not found the be cloned parameter";
     }
   }
-  std::string env = common::GetEnv("SLICE_ENV");
-  if (!env.empty()) {
-    MS_LOG(INFO) << "Slice tensors shape will be configured from env:" << env;
-  }
 }
 
 void SetVirtualDatasetStrategy(const CNodePtr &node) {

tests/ut/python/parallel/test_sparse_feature_bprop.py

@@ -18,16 +18,14 @@ import numpy as np
 import mindspore as ms
 import mindspore.nn as nn
 from mindspore import context
-from mindspore.common import dtype as mstype
-from mindspore.common.tensor import Tensor, IndexedSlices
+from mindspore.common.parameter import Parameter
+from mindspore.common.tensor import Tensor
 from mindspore.ops import composite as C, operations as P
 from mindspore.ops.operations.comm_ops import AllReduce, _MirrorOperator
-from mindspore.ops._grad.grad_base import bprop_getters
-from mindspore._checkparam import Validator as validator
-from mindspore._checkparam import Rel
-from mindspore.ops.primitive import prim_attr_register, PrimitiveWithInfer
 from mindspore.common.api import _executor
 from mindspore.communication.management import HCCL_WORLD_COMM_GROUP
+from mindspore.nn import TrainOneStepCell, Adam
+
 
 class GradWrap(nn.Cell):
     def __init__(self, network):
@@ -37,40 +35,9 @@ class GradWrap(nn.Cell):
     def construct(self, x):
         return C.grad_all(self.network)(x)
 
-class VirtualGatherV2(PrimitiveWithInfer):
-    @prim_attr_register
-    def __init__(self):
-        """init index_select"""
-        super(VirtualGatherV2, self).__init__('VirtualGatherV2')
-        self.init_prim_io_names(inputs=['params', 'indices', 'axis'], outputs=['output'])
-
-    def __infer__(self, params, indices, axis):
-        validator.check_subclass("params", params['dtype'], mstype.tensor, self.name)
-        validator.check_tensor_type_same({"indices": indices['dtype']}, mstype.int_type, self.name)
-        validator.check_subclass("axis", axis['dtype'], mstype.int_, self.name)
-        axis_v = axis['value']
-        params_shp = params['shape']
-        rank = len(params_shp)
-        validator.check_int_range("axis", axis_v, -rank, rank, Rel.INC_LEFT, self.name)
-        if axis_v < 0:
-            axis_v += rank
-        out_shape = params_shp[:axis_v] + indices['shape'] + params_shp[axis_v + 1:]
-        out = {'shape': out_shape,
-               'dtype': params['dtype'],
-               'value': None}
-        return out
-
-@bprop_getters.register(VirtualGatherV2)
-def get_bprop_gather_v2(self):
-    """Generate bprop for GatherV2"""
-
-    def bprop(x, indices, axis, out, dout):
-        return IndexedSlices(indices, dout, x), axis, out
-
-    return bprop
-
 def test_bprop_with_sparse_feature_allreduce():
     context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="hybrid_parallel")
+    context.set_context(enable_sparse=True)
 
     class Net(nn.Cell):
         def __init__(self, axis=0, shape=None):
@@ -78,7 +45,7 @@ def test_bprop_with_sparse_feature_allreduce():
             if shape is None:
                 shape = [8, 8]
             self.all_reduce = AllReduce()
-            self.gatherv2 = P.GatherV2()
+            self.gatherv2 = P.SparseGatherV2()
             self.index = Tensor(np.ones(shape), dtype=ms.int32)
             self.axis = axis
 
@@ -95,6 +62,7 @@ def test_bprop_with_sparse_feature_allreduce():
 
 def test_bprop_with_sparse_feature_mirror():
     context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="hybrid_parallel")
+    context.set_context(enable_sparse=True)
 
     class Net(nn.Cell):
         def __init__(self, axis=0, shape=None):
@@ -102,7 +70,7 @@ def test_bprop_with_sparse_feature_mirror():
             if shape is None:
                 shape = [8, 8]
             self.mirror = _MirrorOperator(group=HCCL_WORLD_COMM_GROUP)
-            self.gatherv2 = P.GatherV2()
+            self.gatherv2 = P.SparseGatherV2()
             self.index = Tensor(np.ones(shape), dtype=ms.int32)
             self.axis = axis
 
@@ -116,3 +84,35 @@ def test_bprop_with_sparse_feature_mirror():
     x = Tensor(np.ones([64, 64]), dtype=ms.float32)
 
     _executor.compile(net, x)
+
+
+def test_bprop_with_sparse_feature_dataparallel():
+    context.set_auto_parallel_context(device_num=8, global_rank=0, parallel_mode="data_parallel")
+    context.set_context(enable_sparse=True)
+
+    class Net(nn.Cell):
+        def __init__(self, axis=0, shape=None):
+            super(Net, self).__init__()
+            if shape is None:
+                shape = [8, 8]
+            weight = Tensor(np.ones([64, 64]), dtype=ms.float32)
+            self.weight = Parameter(weight, "w")
+            self.index = Tensor(np.ones(shape), dtype=ms.int32)
+            self.axis = axis
+            self.gatherv2 = P.SparseGatherV2()
+
+        def construct(self, x, b):
+            out = self.gatherv2(self.weight, self.index, self.axis)
+
+            return out
+
+    _x = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
+    _b = Tensor(np.ones([126, 64, 32]), dtype=ms.float32)
+
+    def compile_net(net):
+        optimizer = Adam(net.trainable_params(), learning_rate=0.1, loss_scale=1024.0, weight_decay=0.9)
+        train_net = TrainOneStepCell(net, optimizer)
+        _executor.compile(train_net, _x, _b)
+
+    net = Net()
+    compile_net(net)