parallel operators for ResizeBilinear and ResizeNearestNeighbor

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

@@ -187,6 +187,7 @@ using RangeCost = CastCost;
 using SplitCost = CastCost;
 using ScatterUpdateCost = CastCost;
 using UniformRealCost = CastCost;
+using ResizeBilinearCost = CastCost;
 
 class SqrtCost : public CastCost {
  public:

mindspore/ccsrc/frontend/parallel/dynamic_creator.h

@@ -208,6 +208,8 @@ REGISTER(ReduceAnyInfo);
 REGISTER(MatmulDDSInfo);
 REGISTER(DSDMatmulInfo);
 REGISTER(UniformRealInfo);
+REGISTER(ResizeBilinearInfo);
+REGISTER(ResizeNearestNeighborInfo);
 }  // namespace parallel
 }  // namespace mindspore
 

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

@@ -62,5 +62,6 @@
 #include "frontend/parallel/ops_info/matmul_dds_info.h"
 #include "frontend/parallel/ops_info/dsd_matmul_info.h"
 #include "frontend/parallel/ops_info/uniform_real_info.h"
+#include "frontend/parallel/ops_info/resizebilinear_info.h"
 
 #endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_HEAD_FILES_H_

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

@@ -134,6 +134,7 @@ constexpr char NEW_AXIS_MASK[] = "new_axis_mask";
 constexpr char SHRINK_AXIS_MASK[] = "shrink_axis_mask";
 constexpr char BEGIN[] = "begin";
 constexpr char SIZE[] = "size";
+constexpr char ALIGN_CORNERS[] = "align_corners";
 constexpr char END[] = "end";
 constexpr char STRIDES[] = "strides";
 constexpr char GROUP[] = "group";
@@ -398,6 +399,8 @@ constexpr char GATHERND[] = "GatherNd";
 constexpr char SCATTER_UPDATE[] = "ScatterUpdate";
 constexpr char GATHERD[] = "GatherD";
 constexpr char DSD_MATMUL[] = "DSDMatmul";
+constexpr char RESIZE_BILINEAR[] = "ResizeBilinear";
+constexpr char RESIZE_NEAREST_NEIGHBOR[] = "ResizeNearestNeighbor";
 
 // pipeline
 constexpr char MICRO[] = "micro";

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

@@ -0,0 +1,139 @@
+/**
+ * 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/resizebilinear_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 ResizeBilinearInfo::GetAttrs() {
+  size_ = GetTupleIntAttr(SIZE);
+  if (size_.size() != 2) {
+    MS_LOG(ERROR) << name_ << ": The size of input size must be 2, but got " << size_.size();
+    return FAILED;
+  }
+
+  if (size_[0] != size_[1]) {
+    MS_LOG(ERROR) << name_ << ": The second two elements of size must be the same, but got (" << size_[0] << ", "
+                  << size_[1] << ")";
+    return FAILED;
+  }
+
+  align_corners_ = GetBoolAttr(ALIGN_CORNERS);
+
+  MS_LOG(INFO) << name_ << ": The input size is " << size_ << ", align_corners is " << align_corners_;
+
+  return SUCCESS;
+}
+
+Status ResizeBilinearInfo::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() != 1) {
+    MS_LOG(ERROR) << name_ << ": The size of strategy must be 1, but got " << stra.size();
+    return FAILED;
+  }
+
+  Dimensions input_strategy = stra[0];
+  if (input_strategy.size() != 4) {
+    MS_LOG(ERROR) << name_ << ": The size of input strategy must be 4, but got" << input_strategy.size();
+    return FAILED;
+  }
+
+  if (input_strategy[2] != 1 || input_strategy[3] != 1) {
+    MS_LOG(ERROR) << name_ << ": Do not support split from H or W";
+    return FAILED;
+  }
+
+  return SUCCESS;
+}
+
+Status ResizeBilinearInfo::InferDevMatrixShape() {
+  // the strategy is (n, c, h, w)
+  // the dev matrix is (n, c, h, w)
+  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 ResizeBilinearInfo::InferTensorMap() {
+  // input_strategy: (n, c, h, w)
+  // output_strategy: (n, c, h, w)
+  // dev_matrix: (n, c, h, w)
+  TensorMap input_tensor_map = {3, 2, 1, 0};
+  TensorMap output_tensor_map = {3, 2, 1, 0};
+
+  (void)inputs_tensor_map_.emplace_back(std::move(input_tensor_map));
+  (void)outputs_tensor_map_.emplace_back(std::move(output_tensor_map));
+  return SUCCESS;
+}
+
+Status ResizeBilinearInfo::SetCostUnderStrategy(const StrategyPtr &strategy) {
+  return SetCostUnderStrategyBase(strategy);
+}
+
+std::vector<StrategyPtr> ResizeBilinearInfo::GenerateOpStrategies(int64_t stage_id) {
+  Shape input0_split(inputs_shape_[0].size(), 0);
+  input0_split[0] = 1;
+  Shapes splittable_inputs = {input0_split};
+
+  std::vector<StrategyPtr> sp_vector;
+  if (GenerateStrategiesForIndependentInputs(stage_id, inputs_shape_, splittable_inputs, &sp_vector) != SUCCESS) {
+    MS_LOG(EXCEPTION) << name_ << " : Generate strategies for independent inputs() failed.";
+  }
+  return sp_vector;
+}
+
+Status ResizeBilinearInfo::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 ResizeBilinearInfo::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/resizebilinear_info.h

@@ -0,0 +1,68 @@
+/**
+ * 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_RESIZEBILINEAR_INFO_H_
+#define MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_RESIZEBILINEAR_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 ResizeBilinearInfo : public OperatorInfo {
+ public:
+  ResizeBilinearInfo(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<ResizeBilinearCost>()) {}
+  ~ResizeBilinearInfo() override = default;
+
+  Status Init(const StrategyPtr &strategy) override;
+  Status InitForCostModel(const StrategyPtr &strategy) override;
+  std::vector<StrategyPtr> GenerateOpStrategies(int64_t) override;
+  Status SetCostUnderStrategy(const StrategyPtr &) override;
+
+ protected:
+  Status GetAttrs() override;
+  Status CheckStrategy(const StrategyPtr &strategy) override;
+  Status InferForwardCommunication() override { return SUCCESS; }
+  Status InferDevMatrixShape() override;
+  Status InferTensorMap() override;
+  Status CheckHWStrategy(int64_t h_strategy, int64_t w_strategy);
+
+ private:
+  std::vector<int64_t> size_;  // four integers, NCHW
+  bool align_corners_;
+};
+
+class ResizeNearestNeighborInfo : public ResizeBilinearInfo {
+ public:
+  ResizeNearestNeighborInfo(const std::string &name, const Shapes &inputs_shape, const Shapes &outputs_shape,
+                            const PrimitiveAttrs &attrs)
+      : ResizeBilinearInfo(name, inputs_shape, outputs_shape, attrs) {}
+  ~ResizeNearestNeighborInfo() override = default;
+};
+
+}  // namespace parallel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_FRONTEND_PARALLEL_OPS_INFO_RESIZEBILINEAR_INFO_H_

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -172,7 +172,7 @@ bool IsSplittableOperator(const std::string &op_name) {
      SOFTPLUS, SOFTSIGN, GREATEREQUAL, LESSEQUAL, LESS, APPROXIMATEEQUAL, MOD, UNIQUE, UNSORTED_SEGMENT_SUM,
      UNSORTED_SEGMENT_MIN, REPEAT_ELEMENTS, TENSOR_DOT, RANGE, UNIFORM_CANDIDATE_SAMPLER, SLICE, SELECT, GATHERD,
      UNSORTED_SEGMENT_MAX, GATHER_ND, TOPK, SCATTER_UPDATE, VIRTUAL_OUTPUT, CONV2D_BACK_PROP_INPUT, CONV2D_TRANSPOSE,
-     MATMUL_DDS, DSD_MATMUL, UNIFORMREAL};
+     MATMUL_DDS, DSD_MATMUL, UNIFORMREAL, RESIZE_BILINEAR, RESIZE_NEAREST_NEIGHBOR};
   // clang-format on
 
   auto iter = splittable_op.find(op_name);

tests/ut/python/parallel/test_resizebilinear.py

@@ -0,0 +1,150 @@
+# 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.
+'''ResizeBilinear and ResizeNearestNeigbor ut'''
+import numpy as np
+
+import mindspore as ms
+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
+
+
+class Net(Cell):
+    '''
+    create the test Net
+    '''
+    def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride,
+                 strategy1=None, strategy2=None):
+        super(Net, self).__init__()
+        self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
+                               pad_mode=pad_mode, stride=stride).shard(strategy1)
+        self.conv2d_weight = Parameter(conv2d_weight, "w1")
+        self.resize_bilinear = P.ResizeBilinear((16, 16)).shard(strategy2)
+
+    def construct(self, x):
+        out = self.conv2d(x, self.conv2d_weight)
+        out = self.resize_bilinear(out)
+        return out
+
+
+class Net2(Cell):
+    '''
+    create the test Net
+    '''
+    def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride,
+                 strategy1=None, strategy2=None):
+        super(Net2, self).__init__()
+        self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
+                               pad_mode=pad_mode, stride=stride).shard(strategy1)
+        self.conv2d_weight = Parameter(conv2d_weight, "w1")
+        self.resize_neighbor = P.ResizeNearestNeighbor((16, 16)).shard(strategy2)
+
+    def construct(self, x):
+        out = self.conv2d(x, self.conv2d_weight)
+        out = self.resize_neighbor(out)
+        return out
+
+class Net3(Cell):
+    '''
+    create the test Net
+    '''
+    def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride,
+                 strategy1=None):
+        super(Net3, self).__init__()
+        self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
+                               pad_mode=pad_mode, stride=stride).shard(strategy1)
+        self.conv2d_weight = Parameter(conv2d_weight, "w1")
+        self.resize_bilinear = P.ResizeBilinear((16, 16))
+
+    def construct(self, x):
+        out = self.conv2d(x, self.conv2d_weight)
+        out = self.resize_bilinear(out)
+        return out
+
+
+_x = Tensor(np.ones([32, 16, 8, 8]), dtype=ms.float32)
+_w1 = Tensor(np.ones([8, 16, 2, 2]), dtype=ms.float32)
+
+
+def compile_net(net, inputs=_x):
+    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, inputs)
+    context.reset_auto_parallel_context()
+
+
+def test_bililear_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
+    strategy2 = ((8, 1, 1, 1),)
+    net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
+              strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)
+
+
+def test_bilinear_model_parallel1():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
+    strategy2 = ((4, 2, 1, 1),)
+    net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
+              strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)
+
+
+def test_bilinear_model_parallel2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
+    strategy2 = ((2, 1, 1, 1),)
+    net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
+              strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)
+
+
+def test_bilinear_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1)
+    compile_net(net)
+
+
+def test_bilinear_no_strategy():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    net = Net3(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1)
+    compile_net(net)
+
+
+def test_neighbor_data_parallel():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
+    strategy2 = ((8, 1, 1, 1),)
+    net = Net2(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
+               strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)
+
+
+def test_neighbor_model_parallel1():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
+    strategy2 = ((4, 2, 1, 1),)
+    net = Net2(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
+               strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)
+
+
+def test_neighbor_auto_parallel():
+    context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
+    net = Net2(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1)
+    compile_net(net)