!31941 modify Strided_slice for master

Merge pull request !31941 from lilei/modify_stridedslice_for_master

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

@@ -16,6 +16,7 @@
 
 #include "frontend/parallel/ops_info/strided_slice_info.h"
 
+#include <bitset>
 #include <algorithm>
 #include <memory>
 #include <utility>
@@ -51,6 +52,70 @@ Status StridedSliceInfo::GetMask(const std::string &mask_name, int64_t *mask_val
   return SUCCESS;
 }
 
+constexpr auto kStridedSliceMaxDims = 8;
+static std::vector<bool> Dec2Bin(int64_t mask) {
+  auto mask_str = std::bitset<kStridedSliceMaxDims>(mask).to_string();
+  int64_t dim_idx = 0;
+  std::vector<bool> result(kStridedSliceMaxDims, false);
+  for (int64_t i = mask_str.size() - 1; i >= 0; --i) {
+    if (mask_str[i] == '1') {
+      result[dim_idx] = true;
+    }
+    dim_idx++;
+  }
+  return result;
+}
+
+void StridedSliceInfo::ComputeBeginMask(int64_t begin_mask_) {
+  auto begin_mask = Dec2Bin(begin_mask_);
+  for (size_t i = 0; i < begin_mask.size(); ++i) {
+    if (i < kStridedSliceMaxDims && begin_mask[i]) {
+      begin_[i] = strides_[i] < 0 ? SizeToLong(inputs_shape_[0][i]) - 1 : 0;
+    }
+  }
+}
+
+void StridedSliceInfo::ComputeEndMask(int64_t end_mask_) {
+  auto end_mask = Dec2Bin(end_mask_);
+  for (size_t j = 0; j < end_mask.size(); ++j) {
+    if (j < kStridedSliceMaxDims && end_mask[j]) {
+      end_[j] = strides_[j] < 0 ? -1 : SizeToLong(inputs_shape_[0][j]);
+    }
+  }
+}
+
+void StridedSliceInfo::ComputeEllipsisMask(int64_t ellipsis_mask_) {
+  auto ellipsis_mask = Dec2Bin(ellipsis_mask_);
+  for (size_t k = 0; k < ellipsis_mask.size(); ++k) {
+    if (k < kStridedSliceMaxDims && ellipsis_mask[k]) {
+      begin_[k] = 0;
+      end_[k] = SizeToLong(inputs_shape_[0][k]);
+      strides_[k] = 1;
+    }
+  }
+}
+
+void StridedSliceInfo::ComputeNewAxisMask(int64_t new_axis_mask_) {
+  auto new_axis_mask = Dec2Bin(new_axis_mask_);
+  for (size_t l = 0; l < new_axis_mask.size(); ++l) {
+    if (l < kStridedSliceMaxDims && new_axis_mask[l]) {
+      begin_[l] = 0;
+      end_[l] = SizeToLong(inputs_shape_[0][l]);
+      strides_[l] = 1;
+    }
+  }
+}
+
+void StridedSliceInfo::ComputShrinkAxisMask(int64_t shrink_axis_mask_) {
+  auto shrink_axis_mask = Dec2Bin(shrink_axis_mask_);
+  for (size_t m = 0; m < shrink_axis_mask.size(); ++m) {
+    if (m < kStridedSliceMaxDims && shrink_axis_mask[m]) {
+      end_[m] = end_[m] > begin_[m] ? begin_[m] + 1 : begin_[m] - 1;
+      strides_[m] = end_[m] > begin_[m] ? 1 : -1;
+    }
+  }
+}
+
 Status StridedSliceInfo::GetAttrs() {
   if (attrs_.size() < STRIDED_SLICE_ATTRS_SIZE) {
     MS_LOG(ERROR) << name_ << ": The size of attrs small than " << STRIDED_SLICE_ATTRS_SIZE;
@@ -62,7 +127,6 @@ Status StridedSliceInfo::GetAttrs() {
       (GetMask(SHRINK_AXIS_MASK, &shrink_axis_mask_) != SUCCESS)) {
     return FAILED;
   }
-  has_mask_ = ((ellipsis_mask_ != 0) || (new_axis_mask_ != 0) || (shrink_axis_mask_ != 0));
 
   if (input_value_.size() != STRIDED_SLICE_INPUTS_SIZE) {
     MS_LOG(ERROR) << name_ << ": The size of input value must be " << STRIDED_SLICE_INPUTS_SIZE << ", but got "
@@ -75,7 +139,11 @@ Status StridedSliceInfo::GetAttrs() {
       (TransValueSequeueToVector(input_value_[STRIDED_SLICE_STRIDES_INDEX], &strides_) != SUCCESS)) {
     return FAILED;
   }
-
+  ComputeBeginMask(begin_mask_);
+  ComputeEndMask(end_mask_);
+  ComputeEllipsisMask(ellipsis_mask_);
+  ComputeNewAxisMask(new_axis_mask_);
+  ComputShrinkAxisMask(shrink_axis_mask_);
   return SUCCESS;
 }
 
@@ -93,12 +161,6 @@ Status StridedSliceInfo::CheckStrategy(const StrategyPtr &strategy) {
   }
 
   Dimensions strategy_value = stra[0];
-  bool has_split = std::any_of(strategy_value.begin(), strategy_value.end(), [](int64_t v) { return v > 1; });
-  if (has_split && has_mask_) {
-    MS_LOG(ERROR) << name_ << ": When there is a mask, the input is not supported to be split";
-    return FAILED;
-  }
-
   if (strategy_value.size() < strides_.size()) {
     MS_LOG(ERROR) << name_ << ": The size of strategy must be larger or equal to the size of strides";
     return FAILED;
@@ -153,6 +215,8 @@ Status StridedSliceInfo::InferTensorMap() {
   }
 
   inputs_tensor_map_.push_back(tensor_map);
+  if (new_axis_mask_ != 0) tensor_map.insert(tensor_map.begin() + (new_axis_mask_ - 1), -1);
+  if (shrink_axis_mask_ != 0) tensor_map.erase(tensor_map.begin() + (shrink_axis_mask_ - 1));
   outputs_tensor_map_.push_back(tensor_map);
   return SUCCESS;
 }
@@ -196,17 +260,11 @@ Status StridedSliceInfo::SetCostUnderStrategy(const StrategyPtr &strategy) {
 
 std::vector<StrategyPtr> StridedSliceInfo::GenerateOpStrategies(int64_t stage_id) {
   Shape input_split(inputs_shape_[0].size(), 1);
-  if (has_mask_) {
-    for (size_t i = 0; i < inputs_shape_[0].size(); ++i) {
+  for (size_t i = 0; i < begin_.size(); ++i) {
+    bool no_fully_fetch = ((begin_[i] != 0) || (end_[i] < inputs_shape_[0][i]));
+    if (no_fully_fetch || (strides_[i] != 1)) {
       input_split[i] = 0;
     }
-  } else {
-    for (size_t i = 0; i < begin_.size(); ++i) {
-      bool no_fully_fetch = ((begin_[i] != 0) || (end_[i] < inputs_shape_[0][i]));
-      if (no_fully_fetch || (strides_[i] != 1)) {
-        input_split[i] = 0;
-      }
-    }
   }
   Shapes splittable_inputs = {input_split};
 

mindspore/ccsrc/frontend/parallel/ops_info/strided_slice_info.h

@@ -40,6 +40,11 @@ class StridedSliceInfo : public OperatorInfo {
   std::vector<StrategyPtr> GenerateOpStrategies(int64_t) override;
   Status SetCostUnderStrategy(const StrategyPtr &) override;
   std::shared_ptr<Strategys> GenerateBatchStrategies() override;
+  void ComputeBeginMask(int64_t begin_mask_);
+  void ComputeEndMask(int64_t end_mask_);
+  void ComputeEllipsisMask(int64_t ellipsis_mask_);
+  void ComputeNewAxisMask(int64_t new_axis_mask_);
+  void ComputShrinkAxisMask(int64_t shrink_axis_mask_);
 
  protected:
   Status GetAttrs() override;
@@ -59,7 +64,6 @@ class StridedSliceInfo : public OperatorInfo {
   int64_t ellipsis_mask_ = 0;
   int64_t new_axis_mask_ = 0;
   int64_t shrink_axis_mask_ = 0;
-  bool has_mask_ = false;
 };
 
 using StridedSliceInfoPtr = std::shared_ptr<StridedSliceInfo>;

mindspore/python/mindspore/ops/operations/array_ops.py

@@ -24,7 +24,6 @@ from collections import Counter
 import numpy as np
 
 from mindspore import log as logger
-from mindspore import context
 from mindspore.common.initializer import Zero
 from .. import signature as sig
 from .._utils import get_broadcast_shape, is_shape_unknown
@@ -3505,10 +3504,6 @@ class StridedSlice(PrimitiveWithInfer):
         """Initialize StridedSlice"""
         self.init_prim_io_names(inputs=['x', 'begin', 'end', 'strides'], outputs=['output'])
 
-        # auto parallel haven't support begin_mask and end_mask
-        if context.get_auto_parallel_context("parallel_mode") in ["semi_auto_parallel", "auto_parallel"]:
-            begin_mask = 0
-            end_mask = 0
         validator.check_non_negative_int(begin_mask, 'begin_mask', self.name)
         validator.check_non_negative_int(end_mask, 'end_mask', self.name)
         validator.check_non_negative_int(ellipsis_mask, 'ellipsis_mask', self.name)

tests/ut/python/parallel/test_stridedslice.py

@@ -20,13 +20,18 @@ from mindspore import context, Tensor, Parameter
 from mindspore.common.api import _cell_graph_executor
 from mindspore.nn import Cell, TrainOneStepCell, Momentum
 from mindspore.ops import operations as P
+from parallel.utils.utils import ParallelValidator
 
 
 class Net(Cell):
-    def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=True, mask=0):
+    def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=True,
+                 begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0):
         super().__init__()
         self.mul = P.Mul().shard(strategy1)
-        self.strided_slice = P.StridedSlice(begin_mask=mask).shard(strategy2)
+        self.strided_slice = P.StridedSlice(begin_mask=begin_mask,
+                                            end_mask=end_mask,
+                                            ellipsis_mask=ellipsis_mask, new_axis_mask=new_axis_mask,
+                                            shrink_axis_mask=shrink_axis_mask).shard(strategy2)
         if is_parameter:
             self.weight = Parameter(weight, "w1")
         else:
@@ -45,10 +50,14 @@ class Net(Cell):
 
 
 class Net2(Cell):
-    def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None):
+    def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None,
+                 begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0):
         super().__init__()
         self.mul = P.Mul().shard(strategy1)
-        self.strided_slice = P.StridedSlice().shard(strategy2)
+        self.strided_slice = P.StridedSlice(begin_mask=begin_mask,
+                                            end_mask=end_mask,
+                                            ellipsis_mask=ellipsis_mask, new_axis_mask=new_axis_mask,
+                                            shrink_axis_mask=shrink_axis_mask).shard(strategy2)
         self.weight2 = Parameter(weight2, "w2")
         self.begin = begin
         self.end = end
@@ -60,105 +69,423 @@ class Net2(Cell):
         return out
 
 
-_x = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
+_x1 = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
+_x2 = Tensor(np.ones([1, 64, 32, 32]), dtype=ms.float32)
+_x3 = Tensor(np.ones([64, 32]), dtype=ms.float32)
 _w1 = Tensor(np.ones([256, 64, 32]), dtype=ms.float32)
 _w2 = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
-_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w3 = Tensor(np.ones([1, 64, 32, 32]), dtype=ms.float32)
+_b1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b2 = Tensor(np.ones([1, 64, 32, 32]), dtype=ms.float32)
 
 
-def compile_net(net):
+def compile_net(net, _x1, _b1):
     optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
     train_net = TrainOneStepCell(net, optimizer)
     train_net.set_auto_parallel()
     train_net.set_train()
-    _cell_graph_executor.compile(train_net, _x, _b)
+    _cell_graph_executor.compile(train_net, _x1, _b1)
     context.reset_auto_parallel_context()
 
 
+def compile_net_utils(net: Cell, *inputs):
+    net.set_auto_parallel()
+    net.set_train()
+    phase, _ = _cell_graph_executor.compile(net, *inputs, auto_parallel_mode=True)
+    context.reset_auto_parallel_context()
+    return phase
+
+
 def test_stridedslice_no_fully_fetch_split_error():
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((2, 2, 2), (2, 2, 2))
     strategy2 = ((2, 2, 2),)
     net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
     with pytest.raises(RuntimeError):
-        compile_net(net)
+        compile_net(net, _x1, _b1)
 
 
 def test_stridedslice_strides_no_1_split_error():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with strides no 1 split in semi auto parallel.
+    Expectation: compile error.
+    """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((2, 2, 2), (2, 2, 2))
     strategy2 = ((1, 2, 2),)
     net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 2), strategy1, strategy2, is_parameter=True)
     with pytest.raises(RuntimeError):
-        compile_net(net)
-
-
-def test_stridedslice_mask_no_0_split_error():
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
-    strategy1 = ((2, 2, 2), (2, 2, 2))
-    strategy2 = ((1, 2, 2),)
-    net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True, mask=1)
-    with pytest.raises(RuntimeError):
-        compile_net(net)
+        compile_net(net, _x1, _b1)
 
 
 def test_stridedslice_begin_size_smaller():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with begin size is smaller in semi auto parallel.
+    Expectation: compile done without error.
+    """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((1, 4, 1), (1, 4, 2))
     strategy2 = ((1, 4, 2),)
     net = Net(_w1, _w2, (0, 0), (128, 64), (1, 1), strategy1, strategy2, is_parameter=True)
-    compile_net(net)
+    compile_net(net, _x1, _b1)
 
 
 def test_stridedslice_parameter():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice of parameter in semi auto parallel.
+    Expectation: compile done without error.
+    """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((1, 4, 1), (1, 4, 2))
     strategy2 = ((1, 4, 2),)
     net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
-    compile_net(net)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_begin_mask_no_0_split_parameter():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with begin mask no 0 split in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 1), (1, 4, 2))
+    strategy2 = ((1, 4, 2),)
+    net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True, begin_mask=1)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_end_mask_no_0_parameter():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with end mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 1), (1, 4, 2))
+    strategy2 = ((1, 4, 2),)
+    net = Net(_w1, _w2, (127, 0, 0), (128, 63, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
+              begin_mask=1, end_mask=2)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_ellipsis_mask_no_0_parameter():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with ellipsis mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 1), (1, 4, 2))
+    strategy2 = ((1, 4, 2),)
+    net = Net(_w1, _w2, (127, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
+              begin_mask=1, end_mask=2, ellipsis_mask=4)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_new_axis_mask_no_0_parameter():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with new axis mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 2, 1), (1, 4, 2, 1))
+    strategy2 = ((1, 1, 4),)
+    net = Net(_w1, _w3, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
+              new_axis_mask=1)
+    compile_net(net, _x2, _b2)
+
+
+def test_stridedslice_shrink_axis_mask_no_0_parameter():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with shrink axis mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 2), (1, 2))
+    strategy2 = ((1, 4, 1),)
+    net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
+              shrink_axis_mask=1)
+    compile_net(net, _x3, _b1)
 
 
 def test_stridedslice_tensor():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice of tensor in semi auto parallel.
+    Expectation: compile done without error.
+    """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((1, 4, 1), (1, 4, 2))
     strategy2 = ((1, 4, 2),)
     net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False)
-    compile_net(net)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_begin_mask_no_0_tensor():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with begin mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 1), (1, 4, 2))
+    strategy2 = ((1, 4, 2),)
+    net = Net(_w1, _w2, (127, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False, begin_mask=1)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_end_mask_no_0_tensor():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with end mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 1), (1, 4, 2))
+    strategy2 = ((1, 4, 2),)
+    net = Net(_w1, _w2, (0, 0, 0), (128, 63, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False, end_mask=2)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_ellipsis_mask_no_0_tensor():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with ellipsis mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 1), (1, 4, 2))
+    strategy2 = ((1, 4, 2),)
+    net = Net(_w1, _w2, (127, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False,
+              begin_mask=1, end_mask=2, ellipsis_mask=4)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_new_axis_mask_no_0_tensor():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with new axis mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 2, 1), (1, 4, 2, 1))
+    strategy2 = ((1, 1, 4),)
+    net = Net(_w1, _w3, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False,
+              new_axis_mask=1)
+    compile_net(net, _x2, _b2)
+
+
+def test_stridedslice_shrink_axis_mask_no_0_tensor():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with shrink axis mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 2), (1, 2))
+    strategy2 = ((1, 4, 1),)
+    net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False,
+              shrink_axis_mask=1)
+    compile_net(net, _x3, _b1)
 
 
 def test_stridedslice_parameter_no_full_split():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with no full split in semi auto parallel.
+    Expectation: compile done without error.
+    """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((1, 4, 1), (1, 4, 2))
     strategy2 = ((1, 2, 2),)
     net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
-    compile_net(net)
+    compile_net(net, _x1, _b1)
 
 
 def test_stridedslice_output():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice of output in semi auto parallel.
+    Expectation: compile done without error.
+    """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((1, 8, 1), (1, 8, 1))
     strategy2 = ((1, 8, 1),)
     net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
-    compile_net(net)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_begin_mask_no_0_output():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with begin mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 8, 1), (1, 8, 1))
+    strategy2 = ((1, 8, 1),)
+    net = Net2(_w2, (61, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2, begin_mask=1)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_end_mask_no_0_output():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with end mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 8, 1), (1, 8, 1))
+    strategy2 = ((1, 8, 1),)
+    net = Net2(_w2, (0, 0, 0), (64, 63, 1), (1, 1, 1), strategy1, strategy2, end_mask=2)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_ellipsis_mask_no_0_output():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with ellipsis mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 8, 1), (1, 8, 1))
+    strategy2 = ((1, 8, 1),)
+    net = Net2(_w2, (63, 0, 0), (64, 63, 1), (1, 1, 1), strategy1, strategy2,
+               begin_mask=1, end_mask=2, ellipsis_mask=4)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_new_axis_mask_no_0_output():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with new axis mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 8, 1), (1, 8, 1))
+    strategy2 = ((8, 1, 1),)
+    net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2, new_axis_mask=1)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_shrink_axis_mask_no_0_output():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with shrink axis mask no 0 in semi auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 8, 1), (1, 8, 1))
+    strategy2 = ((1, 8, 1),)
+    net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2, shrink_axis_mask=1)
+    compile_net(net, _x1, _b1)
 
 
 def test_stridedslice_output_no_full_split():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with no full split in semi auto parallel.
+    Expectation: compile done without error.
+    """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((1, 8, 1), (1, 8, 1))
     strategy2 = ((1, 4, 1),)
     net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
-    compile_net(net)
+    compile_net(net, _x1, _b1)
 
 
 def test_stridedslice_no_strategy():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with no strategy in semi auto parallel.
+    Expectation: compile done without error.
+    """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((1, 8, 1), (1, 8, 1))
     strategy2 = None
     net = Net2(_w2, (0, 0, 0), (128, 64, 1), (1, 1, 1), strategy1, strategy2)
-    compile_net(net)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_begin_mask_no_0_no_strategy():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with begin mask no 0 in auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 8, 1), (1, 8, 1))
+    strategy2 = None
+    net = Net2(_w2, (127, 0, 0), (128, 64, 1), (1, 1, 1), strategy1, strategy2, begin_mask=1)
+    compile_net(net, _x1, _b1)
 
 
 def test_stridedslice_auto_parallel():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice in auto parallel.
+    Expectation: compile done without error.
+    """
     context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
     net = Net2(_w2, (0, 0, 0), (32, 64, 1), (1, 1, 1))
-    compile_net(net)
+    compile_net(net, _x1, _b1)
+
+
+
+def test_stridedslice_begin_mask_no_0_auto_parallel():
+    """
+    Feature: distribute operator stridedslice in auto parallel mode.
+    Description: test stridedslice with begin mask no 0 in auto parallel.
+    Expectation: compile done without error.
+    """
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    net = Net2(_w2, (29, 0, 0), (32, 64, 1), (1, 1, 1), begin_mask=1)
+    compile_net(net, _x1, _b1)
+
+
+def test_stridedslice_layout():
+    """
+    Features: StridedSlice
+    Description: validate layout and structure
+    Expectation: No raise RuntimeError
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 4, 1), (1, 4, 2))
+    strategy2 = ((1, 4, 2),)
+    net = Net(_w1, _w2, (127, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True,
+              begin_mask=1, end_mask=2, ellipsis_mask=4)
+    phase = compile_net_utils(net, _x1, _b1)
+    validator = ParallelValidator(net, phase)
+
+    # check layout
+    features_expect_layout = ([4, 2], [-1, 1, 0], [256, 16, 16], 0, True, '')
+    assert validator.check_parameter_layout('w1', features_expect_layout)
+
+    # check attrs
+    roi_expect_attrs = {'begin_mask': 1, 'end_mask': 2, 'ellipsis_mask': 4}
+    assert validator.check_node_attrs('StridedSlice-1', roi_expect_attrs)
+
+    # check inputs
+    roi_expect_inputs = ['Load-0', 'out((127, 0, 0))', 'out((128, 64, 32))', 'out((1, 1, 1))']
+    assert validator.check_node_inputs('StridedSlice-1', roi_expect_inputs)
+
+    # check sub_graph
+    sub_graph = {
+        'StridedSlice-1': ['Load-0', 'out((127, 0, 0))', 'out((128, 64, 32))', 'out((1, 1, 1))'],
+        'Mul-0': ['Reshape-1', 'StridedSlice-1'],
+        'AllGather-2': ['Reshape-2'],
+        'Split-1': ['AllGather-2'],
+        'TupleGetItem-3': ['Split-1', 0],
+        'TupleGetItem-4': ['Split-1', 1],
+        'TupleGetItem-5': ['Split-1', 2],
+        'TupleGetItem-6': ['Split-1', 3],
+        'MakeTuple-2': ['TupleGetItem-3', 'TupleGetItem-4', 'TupleGetItem-5', 'TupleGetItem-6'],
+        'Concat-1': ['MakeTuple-2']
+    }
+    assert validator.check_graph_structure(sub_graph)