add select op

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

@@ -234,6 +234,7 @@ using AsinCost = ReLU6Cost;
 using ErfCost = ReLU6Cost;
 using ErfcCost = ReLU6Cost;
 using ActivationInfoCost = ReLU6Cost;
+using SelectCost = ReLU6Cost;
 
 class TransposeCost : public CastCost {
  public:

mindspore/ccsrc/frontend/parallel/dynamic_creator.h

@@ -191,6 +191,7 @@ REGISTER(StackInfo);
 REGISTER(ConcatInfo);
 REGISTER(SplitInfo);
 REGISTER(UniqueInfo);
+REGISTER(SelectInfo);
 REGISTER(GatherNdInfo);
 REGISTER(TopKInfo);
 REGISTER(ScatterUpdateInfo);

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

@@ -1,5 +1,5 @@
 /**
- * Copyright 2019 Huawei Technologies Co., Ltd
+ * 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.
@@ -50,6 +50,7 @@
 #include "frontend/parallel/ops_info/unique_info.h"
 #include "frontend/parallel/ops_info/uniform_candidate_sampler_info.h"
 #include "frontend/parallel/ops_info/reluv2_info.h"
+#include "frontend/parallel/ops_info/select_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"

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

@@ -189,6 +189,7 @@ constexpr char REDUCE_SCATTER[] = "ReduceScatter";
 constexpr char HOST_REDUCE_SCATTER[] = "_HostReduceScatter";
 constexpr char EMBEDDING_LOOKUP[] = "EmbeddingLookup";
 constexpr char CONCAT[] = "Concat";
+constexpr char SELECT[] = "Select";
 constexpr char SOFTMAX_CROSS_ENTROPY_WITH_LOGITS[] = "SoftmaxCrossEntropyWithLogits";
 constexpr char SIGMOID_CROSS_ENTROPY_WITH_LOGITS[] = "SigmoidCrossEntropyWithLogits";
 constexpr char MATMUL[] = "MatMul";

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

@@ -0,0 +1,194 @@
+/**
+ * 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/select_info.h"
+
+#include <algorithm>
+#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 {
+Status SelectInfo::CheckStrategy(const StrategyPtr &strategy) {
+  MS_EXCEPTION_IF_NULL(strategy);
+  if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) {
+    MS_LOG(ERROR) << name_ << ": Invalid strategy";
+    return FAILED;
+  }
+
+  if (inputs_shape_.size() != 3) {
+    MS_LOG(ERROR) << name_ << ": The size of inputs shape must be 3";
+    return FAILED;
+  }
+
+  if ((inputs_shape_[0] != inputs_shape_[1]) || (inputs_shape_[1] != inputs_shape_[2])) {
+    MS_LOG(ERROR) << name_ << ": Now we only support the case that all three input shapes are equal";
+    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[1]) || (stra[1] != stra[2])) {
+    MS_LOG(ERROR) << name_ << ": Now we only support the case that all three strategies are equal";
+    return FAILED;
+  }
+
+  return SUCCESS;
+}
+
+Status SelectInfo::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 SelectInfo::InferTensorMap() {
+  TensorMap tensor_map;
+  if (inputs_shape_.empty()) {
+    MS_LOG(ERROR) << name_ << "The inputs shape is empty";
+    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) {
+    tensor_map.push_back(size - i - 1);
+  }
+
+  for (size_t i = 0; i < inputs_shape_.size(); ++i) {
+    inputs_tensor_map_.push_back(tensor_map);
+  }
+  outputs_tensor_map_.push_back(tensor_map);
+  return SUCCESS;
+}
+
+Status SelectInfo::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 SelectInfo::ReComputeBatchSplitFlagList() {
+  for (size_t i = 0; i < inputs_shape_.size(); i++) {
+    split_flag_list_[i] = true;
+  }
+}
+
+Status SelectInfo::SetCostUnderStrategy(const StrategyPtr &strategy) { return SetCostUnderStrategyBase(strategy); }
+
+Status SelectInfo::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);
+  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];
+    for (size_t i = 0; i < inputs_shape_.size(); ++i) {
+      tmp_strategy.push_back(first_input_strategy);
+    }
+    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 SelectInfo::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 SelectInfo::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/select_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_SELECT_INFO_H_
+#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_SELECT_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 SelectInfo : public OperatorInfo {
+ public:
+  SelectInfo(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<SelectCost>()) {}
+  ~SelectInfo() 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 SelectInfoPtr = std::shared_ptr<SelectInfo>;
+}  // namespace parallel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_CONCAT_INFO_H_

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -162,7 +162,7 @@ bool IsSplittableOperator(const std::string &op_name) {
      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, UNIQUE, UNSORTED_SEGMENT_SUM,
-     UNSORTED_SEGMENT_MIN, REPEAT_ELEMENTS, TENSOR_DOT, RANGE, UNIFORM_CANDIDATE_SAMPLER, SLICE,
+     UNSORTED_SEGMENT_MIN, REPEAT_ELEMENTS, TENSOR_DOT, RANGE, UNIFORM_CANDIDATE_SAMPLER, SLICE, SELECT,
      UNSORTED_SEGMENT_MAX, GATHER_ND, TOPK, SCATTER_UPDATE};
   // clang-format on
 

tests/ut/python/parallel/test_select.py

@@ -0,0 +1,111 @@
+# 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.
+# ============================================================================
+import numpy as np
+import pytest
+
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.nn import Cell, Momentum
+from mindspore.ops import operations as P
+from mindspore.train import Model
+from tests.dataset_mock import MindData
+
+
+class Dataset(MindData):
+    def __init__(self, predict, label, length=3):
+        super(Dataset, self).__init__(size=length)
+        self.predict = predict
+        self.label = label
+        self.index = 0
+        self.length = length
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.index >= self.length:
+            raise StopIteration
+        self.index += 1
+        return self.predict, self.label
+
+    def reset(self):
+        self.index = 0
+
+
+class Net(Cell):
+    def __init__(self, w1, w2, strategy1=None, strategy2=None):
+        super().__init__()
+        self.less = P.Less().shard(strategy1)
+        self.w1 = Parameter(w1, "w1")
+        self.w2 = Parameter(w2, "w2")
+        self.select = P.Select().shard(strategy2)
+
+    def construct(self, x, b):
+        out = self.less(x, b)
+        out = self.select(out, self.w1, self.w2)
+        return out
+
+
+_x = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
+_w1 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w2 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+
+
+def compile_net(net):
+    context.set_context(save_graphs=True)
+    learning_rate = 0.1
+    momentum = 0.9
+    epoch_size = 2
+    dataset = Dataset(_x, _b)
+    opt = Momentum(net.trainable_params(), learning_rate, momentum)
+    model = Model(net, optimizer=opt)
+    model.train(epoch_size, dataset, dataset_sink_mode=False)
+    context.reset_auto_parallel_context()
+
+
+def test_select_data_parallel():
+    context.set_auto_parallel_context(
+        parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((8, 1, 1), (8, 1, 1))
+    strategy2 = ((8, 1, 1), (8, 1, 1), (8, 1, 1))
+    net = Net(_w1, _w2, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_select_model_parallel():
+    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), (2, 2, 2), (2, 2, 2))
+    net = Net(_w1, _w2, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_select_auto_parallel():
+    context.set_auto_parallel_context(
+        parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    net = Net(_w1, _w2)
+    compile_net(net)
+
+
+def test_select_strategy_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 = ((8, 1, 1), (2, 2, 2), (2, 2, 2))
+    net = Net(_w1, _w2, strategy1, strategy2)
+    with pytest.raises(RuntimeError):
+        compile_net(net)