!7437 auto parallel support dynamic

Merge pull request !7437 from yao_yf/auto_parallel_support_dynamic_shape

mindspore/ccsrc/frontend/parallel/auto_parallel/operator_costmodel.cc

@@ -808,6 +808,61 @@ double LayerNormCost::GetForwardComputationCost(const std::vector<TensorInfo> &i
   return result;
 }
 
+double UniqueCost::GetForwardCommCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
+                                      int32_t stage_id) const {
+  return 0.0;
+}
+double UniqueCost::GetBackwardCommCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
+                                       int32_t stage_id) const {
+  double result = 0.0;
+  if (is_parameter_[0]) {
+    TensorInfo input = inputs[0];
+    CheckGlobalDeviceManager();
+    MS_EXCEPTION_IF_NULL(g_device_manager);
+    auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
+    Shape input_shape = input.shape();
+    Shape input_slice_shape = input.slice_shape();
+    int32_t used_device_num = 1;
+    for (size_t i = 0; i < input_shape.size(); ++i) {
+      used_device_num *= input_shape[i] / input_slice_shape[i];
+    }
+    if (total_device_num != IntToSize(used_device_num)) {
+      result = ListProduct(input_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
+    }
+  }
+  return result;
+}
+double UniqueCost::GetForwardComputationCost(const std::vector<TensorInfo> &inputs,
+                                             const std::vector<TensorInfo> &outputs, int32_t stage_id) const {
+  // In forward phase, the computation cost = slice(A) + slice(B)
+  Shape input_slice_shape = inputs[0].slice_shape();
+  double result = ListProduct(input_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
+  return result;
+}
+double UniqueCost::GetBackwardComputationCost(const std::vector<TensorInfo> &inputs,
+                                              const std::vector<TensorInfo> &outputs, int32_t stage_id) const {
+  // In backward phase, the computation cost = (0 or 1) allreduce(slice(B))
+  double result = 0.0;
+  if (is_parameter_[0]) {
+    TensorInfo input = inputs[0];  // tensor B
+    CheckGlobalDeviceManager();
+    MS_EXCEPTION_IF_NULL(g_device_manager);
+    auto total_device_num = g_device_manager->GetDeviceListByStageId(stage_id).size();
+
+    Shape input_shape = input.shape();
+    Shape input_slice_shape = input.slice_shape();
+    int32_t used_device_num = 1;
+    for (size_t i = 0; i < input_shape.size(); ++i) {
+      used_device_num *= input_shape[i] / input_slice_shape[i];
+    }
+
+    if (total_device_num != IntToSize(used_device_num)) {
+      result += ListProduct(input_slice_shape) * static_cast<double>(inputs_type_lengths_[0]);
+    }
+  }
+  return result;
+}
+
 double GatherV2PCost::GetForwardCommCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
                                          int32_t stage_id) const {
   double result = 0.0;

mindspore/ccsrc/frontend/parallel/auto_parallel/operator_costmodel.h

@@ -606,6 +606,32 @@ class LayerNormCost : public OperatorCost {
 
 using DropOutCostPtr = std::shared_ptr<DropOutCost>;
 
+class UniqueCost : public OperatorCost {
+ public:
+  explicit UniqueCost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}
+  UniqueCost() : OperatorCost(true) {}
+  ~UniqueCost() override = default;
+
+  double GetCommCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
+                     int32_t stage_id) const override {
+    return GetForwardCommCost(inputs, outputs, stage_id) + GetBackwardCommCost(inputs, outputs, stage_id);
+  }
+  double GetForwardCommCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
+                            int32_t stage_id) const override;
+  double GetBackwardCommCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
+                             int32_t stage_id) const override;
+  double GetComputationCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
+                            int32_t stage_id) const override {
+    return GetForwardComputationCost(inputs, outputs, stage_id) + GetBackwardComputationCost(inputs, outputs, stage_id);
+  }
+  double GetForwardComputationCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
+                                   int32_t stage_id) const override;
+  double GetBackwardComputationCost(const std::vector<TensorInfo> &inputs, const std::vector<TensorInfo> &outputs,
+                                    int32_t) const override;
+};
+
+using UniqueCostPtr = std::shared_ptr<UniqueCost>;
+
 class GatherV2Cost : public OperatorCost {
  public:
   explicit GatherV2Cost(bool is_inputs_related) : OperatorCost(is_inputs_related) {}

mindspore/ccsrc/frontend/parallel/dynamic_creator.h

@@ -182,6 +182,7 @@ REGISTER(DropoutInfo);
 REGISTER(PackInfo);
 REGISTER(ConcatInfo);
 REGISTER(SplitInfo);
+REGISTER(UniqueInfo);
 }  // namespace parallel
 }  // namespace mindspore
 

mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.cc

@@ -151,6 +151,10 @@ Status GatherV2PInfo::GetAttrs() {
     MS_LOG(ERROR) << name_ << ": The axis or offset must be 0 if manual split, bug got " << axis_;
     return FAILED;
   }
+
+  if (std::find(inputs_shape_[1].begin(), inputs_shape_[1].end(), -1) != inputs_shape_[1].end()) {
+    dynamic_shape_indices_ = true;
+  }
   return SUCCESS;
 }
 
@@ -240,7 +244,7 @@ Status GatherV2PInfo::CheckStrategy(const StrategyPtr &strategy) {
 
   // axis=0, index_shape(0)%param_strategy(0) must be 0
   Shape index_shape = inputs_shape_.at(1);
-  if ((axis_ == 0) && (index_shape.at(0) % param_strategy.at(0) != 0)) {
+  if ((axis_ == 0) && (index_shape.at(0) % param_strategy.at(0) != 0) && !dynamic_shape_indices_) {
     MS_LOG(DEBUG) << name_ << ": index_shape(0) can't be divided by param_strategy(0).";
     return FAILED;
   }
@@ -357,13 +361,7 @@ Status GatherV2PInfo::InferDevMatrixShape() {
   return SUCCESS;
 }
 
-Status GatherV2PInfo::InferTensorMap() {
-  if (manual_split_) {
-    inputs_tensor_map_.push_back({1, 0});
-    inputs_tensor_map_.push_back({-1, 1});
-    outputs_tensor_map_.push_back({-1, 1, 0});
-    return SUCCESS;
-  }
+void GatherV2PInfo::InferInputsTensorMap() {
   // infer input tensor map
   // param_strategy(axis) != 1
   size_t param_size = inputs_shape_.at(0).size();
@@ -373,7 +371,7 @@ Status GatherV2PInfo::InferTensorMap() {
   Shape tensor_map_params;
   auto param_strategy = strategy_->GetInputDim().at(0);
   if (param_strategy.at(IntToSize(axis_)) != 1) {
-    tensor_map_index.insert(tensor_map_index.begin(), index_size, -1);
+    tensor_map_index.insert(tensor_map_index.begin(), index_size, MAP_NONE);
     for (size_t i = 0; i < param_size; ++i) {
       tensor_map_params.push_back(SizeToInt(i));
     }
@@ -386,9 +384,17 @@ Status GatherV2PInfo::InferTensorMap() {
       tensor_map_index.push_back(SizeToInt(index_size - i - 1));
     }
   }
+  inputs_tensor_map_.emplace_back(std::move(tensor_map_params));
+  inputs_tensor_map_.emplace_back(std::move(tensor_map_index));
+}
 
+void GatherV2PInfo::InferOutputsTensorMap() {
   // infer output tensor map
+  size_t param_size = inputs_shape_.at(0).size();
+  size_t index_size = inputs_shape_.at(1).size();
+  size_t total_size = param_size + index_size;
   Shape tensor_map_out;
+  auto param_strategy = strategy_->GetInputDim().at(0);
   if (param_strategy.at(IntToSize(axis_)) == 1) {
     // param_strategy(axis) == 1
     for (size_t i = 0; i < param_size; ++i) {
@@ -403,25 +409,40 @@ Status GatherV2PInfo::InferTensorMap() {
   } else {
     // param_strategy(axis) != 1
     if (axis_ == 0) {
-      tensor_map_out.insert(tensor_map_out.end(), 0);
-      tensor_map_out.insert(tensor_map_out.end(), index_size - 1, -1);
+      if (dynamic_shape_indices_) {
+        tensor_map_out.insert(tensor_map_out.end(), MAP_NONE);
+      } else {
+        tensor_map_out.insert(tensor_map_out.end(), 0);
+      }
+      tensor_map_out.insert(tensor_map_out.end(), index_size - 1, MAP_NONE);
       for (size_t i = 1; i < param_size; ++i) {
         tensor_map_out.push_back(i);
       }
     } else {
       for (size_t i = 0; i < param_size; ++i) {
         if (i == IntToSize(axis_)) {
-          tensor_map_out.insert(tensor_map_out.end(), index_size, -1);
+          tensor_map_out.insert(tensor_map_out.end(), index_size, MAP_NONE);
         } else {
+          if (i == 0 && dynamic_shape_indices_) {
+            tensor_map_out.push_back(MAP_NONE);
+          }
           tensor_map_out.push_back(SizeToInt(param_size - i - 1));
         }
       }
     }
   }
-
-  inputs_tensor_map_.emplace_back(std::move(tensor_map_params));
-  inputs_tensor_map_.emplace_back(std::move(tensor_map_index));
   outputs_tensor_map_.emplace_back(std::move(tensor_map_out));
+}
+
+Status GatherV2PInfo::InferTensorMap() {
+  if (manual_split_) {
+    inputs_tensor_map_.push_back({1, 0});
+    inputs_tensor_map_.push_back({-1, 1});
+    outputs_tensor_map_.push_back({-1, 1, 0});
+    return SUCCESS;
+  }
+  InferInputsTensorMap();
+  InferOutputsTensorMap();
   return SUCCESS;
 }
 

mindspore/ccsrc/frontend/parallel/ops_info/gather_v2_p_info.h

@@ -57,6 +57,8 @@ class GatherV2PInfo : public OperatorInfo {
   Status InferTensorInfo() override;
   Status InferDevMatrixShape() override;
   Status InferTensorMap() override;
+  void InferInputsTensorMap();
+  void InferOutputsTensorMap();
   Status GetAttrs() override;
 
   Status ComputeReplaceGraph(const CNodePtr &cnode);
@@ -77,6 +79,7 @@ class GatherV2PInfo : public OperatorInfo {
   Shape out_dev_matrix_shape_;
   Group group_;
   bool manual_split_ = false;
+  bool dynamic_shape_indices_ = false;
   std::vector<int64_t> param_split_shapes_;
   std::vector<int64_t> index_offsets_;
 };

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

@@ -43,5 +43,6 @@
 #include "frontend/parallel/ops_info/split_info.h"
 #include "frontend/parallel/ops_info/pack_info.h"
 #include "frontend/parallel/ops_info/broadcast_to_info.h"
+#include "frontend/parallel/ops_info/unique_info.h"
 
 #endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_HEAD_FILES_H_

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

@@ -48,6 +48,9 @@ constexpr size_t DROPOUT_DO_MASK_KEEP_PROB_INDEX = 3;
 constexpr size_t SoftmaxCrossEntropyWithLogitsAttrSize = 1;
 constexpr size_t SoftmaxCrossEntropyWithLogitsInputsSize = 2;
 constexpr size_t SoftmaxCrossEntropyWithLogitsOutputsSize = 2;
+constexpr size_t UNIQUE_INPUTS_SIZE = 1;
+constexpr size_t UNIQUE_INPUT_SIZE = 1;
+constexpr size_t UNIQUE_OUTPUTS_SIZE = 2;
 constexpr double EPS = 1e-6;
 constexpr double INF = 1e20;
 
@@ -285,6 +288,7 @@ constexpr char DEPTHWISE_CONV2D[] = "DepthwiseConv2D";
 constexpr char ADD[] = "Add";
 constexpr char DROPOUT[] = "Dropout";
 constexpr char KStridedSlice[] = "StridedSlice";
+constexpr char UNIQUE[] = "Unique";
 
 // Parallel don't care
 constexpr char TUPLE_GETITEM[] = "tuple_getitem";

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

@@ -0,0 +1,192 @@
+/**
+ * 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.
+ */
+
+#include "frontend/parallel/ops_info/unique_info.h"
+
+#include <algorithm>
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "ir/value.h"
+#include "frontend/parallel/device_matrix.h"
+#include "frontend/parallel/strategy.h"
+#include "frontend/parallel/context.h"
+#include "frontend/parallel/tensor_layout/tensor_redistribution.h"
+
+namespace mindspore {
+namespace parallel {
+/*
+ * unique has one input, two outputs. Currently, unique cannot be split.
+ */
+Status UniqueInfo::InferTensorMap() {
+  MS_EXCEPTION_IF_NULL(ParallelContext::GetInstance());
+  for (auto shp : inputs_shape_) {
+    TensorMap out_tensor_map;
+    TensorMap in_tensor_map;
+    for (size_t i = 0; i < shp.size(); ++i) {
+      in_tensor_map.push_back(MAP_NONE);
+      out_tensor_map.push_back(MAP_NONE);
+    }
+    inputs_tensor_map_.push_back(in_tensor_map);
+    outputs_tensor_map_.push_back(out_tensor_map);
+    outputs_tensor_map_.push_back(out_tensor_map);
+  }
+  return SUCCESS;
+}
+
+Status UniqueInfo::InferTensorLayout(TensorLayouts *inputs_layout, TensorLayouts *outputs_layout) {
+  if (inputs_layout == nullptr || outputs_layout == nullptr) {
+    MS_LOG(ERROR) << name_ << " : The layout is null.";
+    return FAILED;
+  }
+  TensorLayout input_layout;
+  TensorLayout output_layout;
+  TensorLayout index_layout;
+  if ((input_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[0], inputs_shape_[0]) != SUCCESS) ||
+      (output_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], outputs_shape_[0]) != SUCCESS) ||
+      (index_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[1], outputs_shape_[1]) != SUCCESS)) {
+    return FAILED;
+  }
+  inputs_layout->push_back(input_layout);
+  outputs_layout->push_back(output_layout);
+  outputs_layout->push_back(index_layout);
+  return SUCCESS;
+}
+
+Status UniqueInfo::InferTensorInfo() {
+  TensorLayouts inputs_layout;
+  TensorLayouts outputs_layout;
+  if (InferTensorLayout(&inputs_layout, &outputs_layout) != SUCCESS) {
+    return FAILED;
+  }
+  for (size_t i = 0; i < inputs_layout.size(); ++i) {
+    TensorInfo input_tensor_info(inputs_layout[i]);
+    inputs_tensor_info_.push_back(input_tensor_info);
+  }
+  for (size_t i = 0; i < outputs_layout.size(); ++i) {
+    TensorInfo output_tensor_info(outputs_layout[i]);
+    outputs_tensor_info_.push_back(output_tensor_info);
+  }
+  return SUCCESS;
+}
+
+Status UniqueInfo::InferDevMatrixShape() {
+  dev_matrix_shape_.push_back(dev_num_);
+  return SUCCESS;
+}
+
+Status UniqueInfo::Init(const StrategyPtr &strategy) {
+  if (InitWithAutoRepeatCalc(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << " : Init failed";
+    return FAILED;
+  }
+  MS_LOG(INFO) << name_ << " : Init success";
+  return SUCCESS;
+}
+
+Status UniqueInfo::CheckStrategy(const StrategyPtr &strategy) {
+  Strategys stras = strategy->GetInputDim();
+  if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Invalid strategy.";
+    return FAILED;
+  }
+  for (Dimensions stra : stras) {
+    if (stra.size() != UNIQUE_INPUT_SIZE) {
+      MS_LOG(ERROR) << name_ << " : Invalid strategy.";
+      return FAILED;
+    }
+  }
+  int32_t stage = strategy->GetInputStage();
+  int32_t dev_num = SizeToInt(g_device_manager->GetDeviceListByStageId(stage).size());
+  dev_num_ = dev_num;
+  if (stras[0][0] != 1) {
+    MS_LOG(ERROR) << "Currently, unique only support repeat calculate in all devices";
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
+Status UniqueInfo::GetAttrs() {
+  if ((inputs_shape_.size() != UNIQUE_INPUTS_SIZE) || (outputs_shape_.size() != UNIQUE_OUTPUTS_SIZE)) {
+    MS_LOG(ERROR) << name_ << ": Inputs shape size " << inputs_shape_.size() << " or outputs shape size "
+                  << outputs_shape_.size() << " is wrong.";
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
+Status UniqueInfo::InferMirrorOps() {
+  mirror_ops_.clear();
+
+  Shape tensor_map = inputs_tensor_map_[0];
+  std::vector<Group> group;
+  if (CreateGroupByTensorMap(tensor_map, &group) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << " : Create group failed.";
+    return FAILED;
+  }
+  OperatorVector mirror_op;
+  if (group.empty()) {
+    MS_LOG(INFO) << name_ << " : The mirror ops is empty.";
+    return SUCCESS;
+  } else {
+    mirror_op = CreateMirrorOps(group[0].name(), group[0].GetDevNum());
+    mirror_ops_.push_back(mirror_op);
+    std::string group_name = group[0].name();
+    MS_LOG(INFO) << name_ << " : Create the mirror ops success, the group name is " << group_name;
+  }
+
+  return SUCCESS;
+}
+
+Status UniqueInfo::InitForCostModel(const StrategyPtr &strategy) {
+  if (InitForCostModelWithAutoRepeatCalc(strategy) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << " : Init for cost model failed.";
+    return FAILED;
+  }
+  MS_LOG(INFO) << name_ << " : Init for cost model success.";
+  return SUCCESS;
+}
+
+Status UniqueInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); }
+
+Status UniqueInfo::GenerateStrategies(int32_t stage_id) {
+  if (inputs_shape_.size() != UNIQUE_INPUTS_SIZE) {
+    return FAILED;
+  }
+  if (inputs_shape_[0].size() != UNIQUE_INPUT_SIZE) {
+    return FAILED;
+  }
+  Shape input0_split;
+  input0_split.emplace_back(0);
+  Shapes splittable_inputs = {input0_split};
+  std::vector<StrategyPtr> sp_vector;
+  if (GenerateStrategiesForIndependentInputs(stage_id, inputs_shape_, splittable_inputs, &sp_vector) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": GenerateStrategiesForIndependentInputs failed";
+    return FAILED;
+  }
+  size_t success = 0;
+  for (auto &sp : sp_vector) {
+    if (SetCostUnderStrategy(sp) == SUCCESS) {
+      success++;
+      MS_LOG(INFO) << name_ << ": Successfully generated " << success << " strategy.";
+      PrintStrategy(sp);
+    }
+  }
+  return SUCCESS;
+}
+}  // namespace parallel
+}  // namespace mindspore

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

@@ -0,0 +1,60 @@
+/**
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_UNIQUE_INFO_H_
+#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_UNIQUE_INFO_H_
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "frontend/parallel/auto_parallel/operator_costmodel.h"
+#include "frontend/parallel/ops_info/operator_info.h"
+#include "frontend/parallel/strategy.h"
+
+namespace mindspore {
+namespace parallel {
+class UniqueInfo : public OperatorInfo {
+ public:
+  UniqueInfo(const std::string &operator_name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+             const PrimitiveAttrs &attrs)
+      : OperatorInfo(operator_name, inputs_shape, outputs_shape, attrs, std::make_shared<GetNextCost>(false)) {}
+  ~UniqueInfo() override = default;
+
+  Status Init(const StrategyPtr &strategy) override;
+  Status SetCostUnderStrategy(const StrategyPtr &strategy) override;
+  Status InitForCostModel(const StrategyPtr &strategy) override;
+  Status GenerateStrategies(int32_t stage_id) override;
+
+ protected:
+  Status CheckStrategy(const StrategyPtr &strategy) override;
+  Status GetAttrs() override;
+  Status InferTensorMap() override;
+  Status InferTensorLayout(TensorLayouts *inputs_layout, TensorLayouts *outputs_layout);
+  Status InferTensorInfo() override;
+  Status InferDevMatrixShape() override;
+  Status InferMirrorOps() override;
+  Status InferForwardCommunication() override { return SUCCESS; }
+  Status InferAsLossDivisor() override { return SUCCESS; }
+
+ private:
+  int32_t dev_num_ = 1;
+};
+}  // namespace parallel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_UNIQUE_INFO_H_

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -312,7 +312,7 @@ bool IsSplittableOperator(const std::string &op_name) {
      EMBEDDING_LOOKUP, FUSE_BATCH_NORM_EX, SPLIT, BROADCAST_TO, ABS, ACOSH, ASIN, ASINH, ATAN, ATANH, CEIL, COSH,
      EXPM1, LOG1P, SIN, SINH, TAN, RSQRT, INV, RECIPROCAL, ROUND, FLOOR, SIGN, ERF, ERFC, ZEROSLIKE, ONESLIKE,
      BESSELI0E, BESSELI1E, FLOORMOD, ASSIGN, ASSIGN_ADD, ATAN2, DIVNONAN, LOGICALAND, LOGICALOR, ELU, RELU6, RELUV2,
-     SOFTPLUS, SOFTSIGN, GREATEREQUAL, LESSEQUAL, LESS, APPROXIMATEEQUAL, MOD};
+     SOFTPLUS, SOFTSIGN, GREATEREQUAL, LESSEQUAL, LESS, APPROXIMATEEQUAL, MOD, UNIQUE};
   // clang-format on
 
   auto iter = splittable_op.find(op_name);

mindspore/ccsrc/frontend/parallel/tensor_layout/arrangement.cc

@@ -39,7 +39,7 @@ Status Arrangement::Init(const Shape &array) {
 }
 
 bool Arrangement::IsValidArrangement() {
-  return !std::any_of(array_.begin(), array_.end(), [](int64_t value) { return value <= 0; });
+  return !std::any_of(array_.begin(), array_.end(), [](int64_t value) { return value <= 0 && value != -1; });
 }
 
 void Arrangement::ComputeSize() {

mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_layout_transfer.cc

@@ -21,7 +21,19 @@
 
 namespace mindspore {
 namespace parallel {
-Status RedistributionLayoutTransfer::CheckValidTransfer() { return Status::SUCCESS; }
+Status RedistributionLayoutTransfer::CheckValidTransfer() {
+  Shape from_shape = from_in_.tensor_shape().array();
+  if (std::find(from_shape.begin(), from_shape.end(), -1) != from_shape.end()) {
+    is_dynamic_shape_ = true;
+    if (from_in_ != to_in_) {
+      MS_LOG(ERROR) << "In dynamic shape scene, the from_tensor_shape should be equal to to_tensor_shape";
+      MS_LOG(ERROR) << "from_in layout" << from_in_.ToString();
+      MS_LOG(ERROR) << "to_in layout" << to_in_.ToString();
+      return Status::FAILED;
+    }
+  }
+  return Status::SUCCESS;
+}
 
 /*
  * unify device arrangement between in_layout and out_layout

mindspore/ccsrc/frontend/parallel/tensor_layout/redistribution_layout_transfer.h

@@ -29,10 +29,12 @@ class RedistributionLayoutTransfer : public LayoutTransfer {
   RedistributionLayoutTransfer() = default;
   ~RedistributionLayoutTransfer() override = default;
   std::shared_ptr<ReshapeLayoutTransfer> UnifyDeviceArrangementAndTensorShape() const;
+  bool IsDynamicShape() const { return is_dynamic_shape_; }
 
  private:
   Status CheckValidTransfer() override;
   std::shared_ptr<ReshapeLayoutTransfer> UnifyDeviceArrangement() const;
+  bool is_dynamic_shape_ = false;
 };
 }  // namespace parallel
 }  // namespace mindspore

mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.cc

@@ -357,6 +357,10 @@ bool TensorLayout::operator==(const TensorLayout &t1) const {
   return (IsSameDeviceArrangement(t1) && IsSameTensorMap(t1) && IsSameTensorShape(t1));
 }
 
+bool TensorLayout::operator!=(const TensorLayout &t1) const {
+  return !(IsSameDeviceArrangement(t1) && IsSameTensorMap(t1) && IsSameTensorShape(t1));
+}
+
 /*
  * remove elements equal to 1 in tensor_shape, if all elements are 1, squeeze the tensor_shape to [ 1 ]
  * example 1:

mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_layout.h

@@ -82,6 +82,8 @@ class TensorLayout {
 
   bool operator==(const TensorLayout &t1) const;
 
+  bool operator!=(const TensorLayout &t1) const;
+
   bool TensorShapeCanBeExpanded(const Arrangement &expanded_shape) const;
 
   std::shared_ptr<Arrangement> ComputeExpandedTensorShape(const Arrangement &expand_shape) const;

mindspore/ccsrc/frontend/parallel/tensor_layout/tensor_redistribution.cc

@@ -82,17 +82,24 @@ RedistributionOpListPtr TensorRedistribution::InferTensorRedistributionOperatorL
   if (status != Status::SUCCESS) {
     return nullptr;
   }
-  std::shared_ptr<ReshapeLayoutTransfer> ptr = layout_transfer.UnifyDeviceArrangementAndTensorShape();
-  if (ptr == nullptr) {
-    MS_LOG(ERROR) << "Infer tensor layout return nullptr!";
-    return nullptr;
-  }
-  if (!ptr->ExpandAble()) {
-    expand_able_ = false;
-    return InferTensorRedistributionOperatorListUnExpand(is_cost_model);
+  TensorLayout from_layout;
+  TensorLayout to_layout;
+  if (layout_transfer.IsDynamicShape()) {
+    from_layout = layout_transfer.from_in();
+    to_layout = layout_transfer.to_in();
+  } else {
+    std::shared_ptr<ReshapeLayoutTransfer> ptr = layout_transfer.UnifyDeviceArrangementAndTensorShape();
+    if (ptr == nullptr) {
+      MS_LOG(ERROR) << "Infer tensor layout return nullptr!";
+      return nullptr;
+    }
+    if (!ptr->ExpandAble()) {
+      expand_able_ = false;
+      return InferTensorRedistributionOperatorListUnExpand(is_cost_model);
+    }
+    from_layout = ptr->from_in();
+    to_layout = ptr->to_in();
   }
-  TensorLayout from_layout = ptr->from_in();
-  TensorLayout to_layout = ptr->to_in();
   MS_LOG(DEBUG) << "reshape from_layout " << from_layout.ToString();
   MS_LOG(DEBUG) << "reshape to_layout " << to_layout.ToString();
   MS_LOG(DEBUG) << "reshape from_origin_ " << from_origin_.ToString();

mindspore/ops/_grad/grad_array_ops.py

@@ -33,6 +33,7 @@ reduce_sum = P.ReduceSum()
 unsorted_segment_sum = P.UnsortedSegmentSum()
 transpose = P.Transpose()
 shape_op = P.Shape()
+dyn_shape_op = P.DynamicShape()
 reshape = P.Reshape()
 size_op = P.Size()
 invert_permutation = P.InvertPermutation()
@@ -365,7 +366,10 @@ def get_bprop_gather_v2(self):
         # Example: out_shape:(3,2,3) axis 1 -> (1,0,2)
         perm_1 = _generate_shape_index(out_shp, ind_shp, axis)
         values_transpose = transpose(dout, perm_1)
-        params_grad = unsorted_segment_sum(values_transpose, indices, shape_op(x)[axis])
+        if -1 in shape_op(x):
+            params_grad = unsorted_segment_sum(values_transpose, indices, dyn_shape_op(x)[axis])
+        else:
+            params_grad = unsorted_segment_sum(values_transpose, indices, shape_op(x)[axis])
         # Example: out_shape:(3,2,3) axis 2 -> (1,2,0)
         perm_2 = _generate_inverse_index(x_shp, axis)
         params_grad = transpose(params_grad, perm_2)

tests/ut/python/parallel/test_dynamic_shape.py

@@ -0,0 +1,118 @@
+# 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.
+
+import numpy as np
+
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore import context
+from mindspore.common.api import _executor
+from mindspore.common.parameter import Parameter
+from mindspore.ops import composite as C
+from mindspore.ops import operations as P
+from mindspore.common.initializer import initializer
+from mindspore.nn import TrainOneStepCell, Momentum
+from tests.ut.python.ops.test_math_ops import VirtualLoss
+
+
+grad_all = C.GradOperation(get_all=True)
+
+
+class NetWithLoss(nn.Cell):
+    def __init__(self, network):
+        super(NetWithLoss, self).__init__()
+        self.loss = VirtualLoss()
+        self.network = network
+
+    def construct(self, x):
+        predict = self.network(x)
+        return self.loss(predict)
+
+
+class GradWrap(nn.Cell):
+    def __init__(self, network):
+        super(GradWrap, self).__init__()
+        self.network = network
+
+    def construct(self, x):
+        return grad_all(self.network)(x)
+
+def test_unique_column_split():
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.unique = P.Unique().shard(((1,),))
+            self.relu = P.ReLU()
+            self.mul = P.Mul()
+            self.embedding_lookp = P.GatherV2().shard(((1, 8), (1,)))
+            self.embedding_table = Parameter(initializer('normal', [2000, 128]),
+                                             name='embedding_table')
+            self.gatherv2 = P.GatherV2().shard(((1, 8), (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")
+
+        def construct(self, indices):
+            indices_flatten = self.reshape(indices, (-1,))
+            unique_id, unique_idx = self.unique(indices_flatten)
+            unique_id_weight = self.embedding_lookp(self.embedding_table, unique_id, 0)
+            weight_flatten = self.gatherv2(unique_id_weight, unique_idx, 0)
+            weight = self.reshape(weight_flatten, (32, 64, 128))
+            vx = self.mul(weight, self.mul_weight)
+            return vx
+
+    size = 8
+    context.set_auto_parallel_context(device_num=size, global_rank=0, parallel_mode="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)
+    train_net = TrainOneStepCell(net, optimizer)
+    train_net.set_auto_parallel()
+    train_net.set_train()
+    _executor.compile(train_net, x)
+
+def test_unique_row_split():
+    class Net(nn.Cell):
+        def __init__(self):
+            super().__init__()
+            self.unique = P.Unique().shard(((1,),))
+            self.relu = P.ReLU()
+            self.mul = P.Mul()
+            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.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")
+
+        def construct(self, indices):
+            indices_flatten = self.reshape(indices, (-1,))
+            unique_id, unique_idx = self.unique(indices_flatten)
+            unique_id_weight = self.embedding_lookp(self.embedding_table, unique_id, 0)
+            weight_flatten = self.gatherv2(unique_id_weight, unique_idx, 0)
+            weight = self.reshape(weight_flatten, (32, 64, 128))
+            vx = self.mul(weight, self.mul_weight)
+            return vx
+
+    size = 8
+    context.set_auto_parallel_context(device_num=size, global_rank=0, parallel_mode="stand_alone")
+    x = Tensor(np.ones([32, 64]), dtype=ms.int32)
+    net = Net()
+    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()
+    _executor.compile(train_net, x)