add scatterupdate op

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

@@ -185,6 +185,7 @@ using ZerosLikeCost = CastCost;
 using OnesLikeCost = CastCost;
 using RangeCost = CastCost;
 using SplitCost = CastCost;
+using ScatterUpdateCost = CastCost;
 
 class SqrtCost : public CastCost {
  public:

mindspore/ccsrc/frontend/parallel/dynamic_creator.h

@@ -193,6 +193,7 @@ REGISTER(SplitInfo);
 REGISTER(UniqueInfo);
 REGISTER(GatherNdInfo);
 REGISTER(TopKInfo);
+REGISTER(ScatterUpdateInfo);
 }  // namespace parallel
 }  // namespace mindspore
 

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

@@ -52,5 +52,6 @@
 #include "frontend/parallel/ops_info/reluv2_info.h"
 #include "frontend/parallel/ops_info/gathernd_info.h"
 #include "frontend/parallel/ops_info/topk_info.h"
+#include "frontend/parallel/ops_info/scatter_update_info.h"
 
 #endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_HEAD_FILES_H_

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

@@ -326,6 +326,7 @@ constexpr char DROPOUT[] = "Dropout";
 constexpr char KStridedSlice[] = "StridedSlice";
 constexpr char UNIQUE[] = "Unique";
 constexpr char GATHERND[] = "GatherNd";
+constexpr char SCATTER_UPDATE[] = "ScatterUpdate";
 
 // Parallel don't care
 constexpr char STRING_EQUAL[] = "string_equal";

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

@@ -0,0 +1,210 @@
+/**
+ * Copyright 2021 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/scatter_update_info.h"
+
+#include <algorithm>
+#include <functional>
+#include <memory>
+#include <utility>
+#include <vector>
+
+#include "frontend/parallel/device_matrix.h"
+#include "frontend/parallel/strategy.h"
+#include "frontend/parallel/tensor_layout/tensor_redistribution.h"
+#include "pipeline/jit/resource.h"
+
+namespace mindspore {
+namespace parallel {
+// The indices can not be split.
+// The strategy of input and the strategy of updates must be equal.
+// The first dimension of input or updates can not be split.
+Status ScatterUpdateInfo::CheckStrategy(const StrategyPtr &strategy) {
+  MS_EXCEPTION_IF_NULL(strategy);
+  if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Invalid strategy";
+    return FAILED;
+  }
+
+  std::vector<Dimensions> stra = strategy->GetInputDim();
+  if (stra.size() != 3) {
+    MS_LOG(ERROR) << name_ << ": The size of strategy must be 3";
+    return FAILED;
+  }
+
+  if (stra[0] != stra[2]) {
+    MS_LOG(ERROR) << name_ << ": The strategy[0] and strategy[2] must be equal";
+    return FAILED;
+  }
+
+  if (stra[0].empty()) {
+    MS_LOG(ERROR) << name_ << ": The strategy[0] is empty";
+    return FAILED;
+  }
+
+  if (stra[0][0] != 1) {
+    MS_LOG(ERROR) << name_ << ": The first dimension of input can not be split";
+    return FAILED;
+  }
+
+  if (!stra[1].empty() && std::accumulate(stra[1].begin(), stra[1].end(), 1, std::multiplies<int64_t>()) != 1) {
+    MS_LOG(ERROR) << name_ << ": The indices can not be split";
+    return FAILED;
+  }
+
+  return SUCCESS;
+}
+
+Status ScatterUpdateInfo::InferDevMatrixShape() {
+  MS_EXCEPTION_IF_NULL(strategy_);
+  std::vector<Dimensions> stra = strategy_->GetInputDim();
+  if (stra.empty()) {
+    MS_LOG(ERROR) << name_ << "The strategy is empty";
+    return FAILED;
+  }
+
+  dev_matrix_shape_ = stra[0];
+  return SUCCESS;
+}
+
+Status ScatterUpdateInfo::InferTensorMap() {
+  TensorMap input_tensor_map;
+  TensorMap indices_tensor_map(inputs_shape_[1].size(), MAP_NONE);
+  if (inputs_shape_.size() != 3) {
+    MS_LOG(ERROR) << name_ << "The size of inputs shape must be 3";
+    return FAILED;
+  }
+
+  // cannot use dev_matrix_shape_ replace inputs_shape_[0], because it may not be fully split in all devices.
+  int64_t size = SizeToLong(inputs_shape_[0].size());
+  for (int64_t i = 0; i < size; ++i) {
+    input_tensor_map.push_back(size - i - 1);
+  }
+
+  inputs_tensor_map_.push_back(input_tensor_map);    // input
+  inputs_tensor_map_.push_back(indices_tensor_map);  // indices
+  inputs_tensor_map_.push_back(input_tensor_map);    // updates
+
+  outputs_tensor_map_.push_back(input_tensor_map);
+  return SUCCESS;
+}
+
+Status ScatterUpdateInfo::InferTensorInfo() {
+  if (inputs_shape_.empty() || outputs_shape_.empty() || inputs_tensor_map_.empty() || outputs_tensor_map_.empty()) {
+    MS_LOG(ERROR) << name_ << ": Invalid args";
+    return FAILED;
+  }
+
+  TensorLayout input_layout, output_layout;
+  for (size_t i = 0; i < inputs_shape_.size(); ++i) {
+    // infer tensor layout
+    if (input_layout.InitFromVector(dev_matrix_shape_, inputs_tensor_map_[i], inputs_shape_[i]) != SUCCESS) {
+      MS_LOG(ERROR) << name_ << ": Infer input tensor layout failed.";
+      return FAILED;
+    }
+    TensorInfo input_tensor_info(input_layout);
+    inputs_tensor_info_.push_back(input_tensor_info);
+  }
+
+  if (output_layout.InitFromVector(dev_matrix_shape_, outputs_tensor_map_[0], outputs_shape_[0]) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer output tensor layout failed.";
+    return FAILED;
+  }
+  TensorInfo output_tensor_info(output_layout);
+  outputs_tensor_info_.push_back(output_tensor_info);
+  return SUCCESS;
+}
+
+void ScatterUpdateInfo::ReComputeBatchSplitFlagList() {
+  for (size_t i = 0; i < inputs_shape_.size(); i++) {
+    split_flag_list_[i] = false;  // the first dimension can not be split
+  }
+}
+
+Status ScatterUpdateInfo::SetCostUnderStrategy(const StrategyPtr &strategy) {
+  return SetCostUnderStrategyBase(strategy);
+}
+
+Status ScatterUpdateInfo::GenerateStrategies(int64_t stage_id) {
+  if (InferAttrs() != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Infer attrs failed";
+    return FAILED;
+  }
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << ": The inputs shape is empty";
+    return FAILED;
+  }
+
+  // to generate the first input's strategy
+  Shape input_split(inputs_shape_[0].size(), 1);
+  input_split[0] = 0;
+  Shapes splittable_input = {input_split};
+  Shapes tmp_inputs_shape = {inputs_shape_[0]};
+
+  std::vector<StrategyPtr> sp_vector;
+  if (GenerateStrategiesForIndependentInputs(stage_id, tmp_inputs_shape, splittable_input, &sp_vector) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Generate strategies failed";
+    return FAILED;
+  }
+
+  // the others strategies are equal to the first input's strategy
+  for (auto &sp : sp_vector) {
+    if ((sp == nullptr) || sp->GetInputDim().empty()) {
+      MS_LOG(ERROR) << name_ << ": The strategy is null or empty";
+      return FAILED;
+    }
+    Strategys tmp_strategy;
+    Dimensions first_input_strategy = sp->GetInputDim()[0];
+    Dimensions indices_strategy(inputs_shape_[1].size(), 1);
+    tmp_strategy.push_back(first_input_strategy);  // input
+    tmp_strategy.push_back(indices_strategy);      // indices
+    tmp_strategy.push_back(first_input_strategy);  // updates
+
+    sp->ResetInputs(tmp_strategy);
+  }
+
+  size_t success = 0;
+  for (auto &sp : sp_vector) {
+    PrintStrategy(sp);
+    if (SetCostUnderStrategy(sp) == SUCCESS) {
+      success++;
+      MS_LOG(INFO) << name_ << ": Successfully generated " << success << " strategy.";
+      PrintStrategy(sp);
+    }
+  }
+  return SUCCESS;
+}
+
+Status ScatterUpdateInfo::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 ScatterUpdateInfo::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;
+}
+}  // namespace parallel
+}  // namespace mindspore

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

@@ -0,0 +1,59 @@
+/**
+ * Copyright 2021 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_SCATTER_UPDATE_INFO_H_
+#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_SCATTER_UPDATE_INFO_H_
+
+#include <string>
+#include <memory>
+#include <unordered_map>
+#include <vector>
+
+#include "ir/value.h"
+#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 ScatterUpdateInfo : public OperatorInfo {
+ public:
+  ScatterUpdateInfo(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<ScatterUpdateCost>()) {}
+  ~ScatterUpdateInfo() override = default;
+
+  Status Init(const StrategyPtr &strategy) override;
+  Status InitForCostModel(const StrategyPtr &strategy) override;
+  Status GenerateStrategies(int64_t) override;
+  Status SetCostUnderStrategy(const StrategyPtr &) override;
+  void ReComputeBatchSplitFlagList() override;
+
+ protected:
+  Status GetAttrs() override { return SUCCESS; }
+  Status CheckStrategy(const StrategyPtr &strategy) override;
+  Status InferMirrorOps() override { return SUCCESS; }
+  Status InferForwardCommunication() override { return SUCCESS; }
+  Status InferTensorInfo() override;
+  Status InferDevMatrixShape() override;
+  Status InferTensorMap() override;
+};
+
+using ScatterUpdateInfoPtr = std::shared_ptr<ScatterUpdateInfo>;
+}  // namespace parallel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_SCATTER_UPDATE_INFO_H_

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -163,7 +163,7 @@ bool IsSplittableOperator(const std::string &op_name) {
      BESSELI0E, BESSELI1E, FLOORMOD, ASSIGN, ASSIGN_ADD, ATAN2, DIVNONAN, LOGICALAND, LOGICALOR, ELU, RELU6, RELUV2,
      SOFTPLUS, SOFTSIGN, GREATEREQUAL, LESSEQUAL, LESS, APPROXIMATEEQUAL, MOD, UNIQUE, UNSORTED_SEGMENT_SUM,
      UNSORTED_SEGMENT_MIN, REPEAT_ELEMENTS, TENSOR_DOT, RANGE, UNIFORM_CANDIDATE_SAMPLER, SLICE,
-     UNSORTED_SEGMENT_MAX, GATHER_ND, TOPK};
+     UNSORTED_SEGMENT_MAX, GATHER_ND, TOPK, SCATTER_UPDATE};
   // clang-format on
 
   auto iter = splittable_op.find(op_name);

tests/ut/python/parallel/test_scatter_update.py

@@ -0,0 +1,50 @@
+# Copyright 2021 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.
+# ============================================================================
+""" test scatter update """
+import numpy as np
+import mindspore.nn as nn
+from mindspore import Tensor, Model, Parameter
+from mindspore.ops import operations as P
+from mindspore import context
+
+
+class Net(nn.Cell):
+    """Net definition"""
+    def __init__(self):
+        super(Net, self).__init__()
+        self.inputs = Parameter(Tensor(np.ones([32, 128]).astype(np.float32)), "input")
+        self.indices = Tensor(np.ones([4]).astype(np.int32))
+        self.updates = Tensor(np.ones([4, 128]).astype(np.float32))
+        self.scatter_update = P.ScatterUpdate().shard(((1, 8), (1,), (1, 8)))
+        self.add = P.TensorAdd().shard(((8, 1), (8, 1)))
+        self.relu = P.ReLU()
+
+    def construct(self, x):
+        out = self.scatter_update(self.inputs, self.indices, self.updates)
+        out = self.add(x, out)
+        out = self.relu(out)
+        return out
+
+
+def test_distribute_predict():
+    context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, full_batch=True)
+    inputs = Tensor(np.ones([32, 128]).astype(np.float32))
+    net = Net()
+    model = Model(net)
+    predict_map = model.infer_predict_layout(inputs)
+    output = model.predict(inputs)
+    context.reset_auto_parallel_context()
+    return predict_map, output