add check for resize op

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

@@ -56,6 +56,14 @@ std::string StrategyToString(const Strategys &strategy) {
   return strategy_str;
 }
 
+Status OperatorInfo::CheckOutputStrategy(const StrategyPtr &out_strategy) {
+  if (out_strategy) {
+    MS_LOG(ERROR) << name_ << ": It does not support to set output strategy now, please modify the shard set";
+    return FAILED;
+  }
+  return SUCCESS;
+}
+
 Status OperatorInfo::CheckStrategyValue(const StrategyPtr &strategy, const Shapes &inputs_shape) {
   if (strategy == nullptr) {
     MS_LOG(ERROR) << name_ << ": The strategy is null.";

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

@@ -209,7 +209,7 @@ class OperatorInfo {
   virtual Status InferMirrorOps();
   virtual Status InferTensorInfo();
   virtual void InferReplaceOps() {}
-  virtual Status CheckOutputStrategy(const StrategyPtr &out_strategy) { return SUCCESS; }
+  virtual Status CheckOutputStrategy(const StrategyPtr &out_strategy);
   Status CheckStrategyValue(const StrategyPtr &strategy, const Shapes &inputs_shape);
   void SetRepeatedCalcDevMatrix();
   void ResetTensorMapIfRepeatedCalc();

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

@@ -126,6 +126,25 @@ void ResizeBilinearInfo::ReplaceNodeInputOrAttrs() {
 Status ResizeNearestNeighborInfo::CheckStrategy(const StrategyPtr &strategy) {
   MS_EXCEPTION_IF_NULL(strategy);
 
+  if (align_corners_) {
+    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_ << ": The align_corners is True, do not support split from H or W";
+      return FAILED;
+    }
+  }
+
   // check input strategy
   if (CheckStrategyValue(strategy, inputs_shape_) != SUCCESS) {
     MS_LOG(ERROR) << name_ << ": Check input strategy failed";
@@ -143,6 +162,10 @@ Status ResizeNearestNeighborInfo::CheckStrategy(const StrategyPtr &strategy) {
 
 std::vector<StrategyPtr> ResizeNearestNeighborInfo::GenerateOpStrategies(int64_t stage_id) {
   Shape multiples_split(inputs_shape_[0].size(), 1);
+  if (align_corners_) {
+    multiples_split[2] = 0;
+    multiples_split[3] = 0;
+  }
   Shapes splittable_inputs = {multiples_split};
 
   std::vector<StrategyPtr> sp_vector;

tests/ut/python/parallel/test_resizebilinear.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 '''ResizeBilinear and ResizeNearestNeigbor ut'''
 import numpy as np
+import pytest
 
 import mindspore as ms
 from mindspore import context, Tensor, Parameter
@@ -43,13 +44,13 @@ class Net2(Cell):
     '''
     create the test Net
     '''
-    def __init__(self, conv2d_weight, mul_weight, out_channel, kernel_size, pad_mode, stride,
+    def __init__(self, conv2d_weight, mul_weight, out_channel, kernel_size, pad_mode, stride, align_corners=False,
-                 strategy1=None, strategy2=None):
+                 strategy1=None, strategy2=None, out_strategy=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)
+        self.resize_neighbor = P.ResizeNearestNeighbor((16, 16), align_corners).shard(strategy2, out_strategy)
         self.mul = P.Mul()
         self.mul_weight = Parameter(mul_weight, "w2")
 
@@ -59,6 +60,7 @@ class Net2(Cell):
         out = self.mul(out, self.mul_weight)
         return out
 
+
 class Net3(Cell):
     '''
     create the test Net
@@ -92,6 +94,11 @@ def compile_net(net, inputs=_x):
 
 
 def test_bililear_data_parallel():
+    """
+    Feature: test ResizeBilinear data parallel strategy
+    Description: only shard batch dimension
+    Expectation: compile success
+    """
     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),)
@@ -101,6 +108,11 @@ def test_bililear_data_parallel():
 
 
 def test_bilinear_model_parallel1():
+    """
+    Feature: test ResizeBilinear model parallel strategy
+    Description: shard N/C
+    Expectation: compile success
+    """
     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),)
@@ -109,7 +121,12 @@ def test_bilinear_model_parallel1():
     compile_net(net)
 
 
-def test_bilinear_model_parallel2():
+def test_bilinear_repeated_calc():
+    """
+    Feature: test ResizeBilinear repeated calculation parallel strategy
+    Description: only shard batch dimension, but shard num smaller than device num
+    Expectation: compile success
+    """
     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),)
@@ -119,18 +136,33 @@ def test_bilinear_model_parallel2():
 
 
 def test_bilinear_auto_parallel():
+    """
+    Feature: test ResizeBilinear auto parallel
+    Description:
+    Expectation: compile success
+    """
     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)
+    """
+    Feature: test ResizeBilinear semi auto parallel, and has not set strategy for it
+    Description:
+    Expectation: compile success
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_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():
+    """
+    Feature: test ResizeNearestNeighbor data parallel strategy
+    Description: only shard batch dimension
+    Expectation: compile success
+    """
     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),)
@@ -139,7 +171,57 @@ def test_neighbor_data_parallel():
     compile_net(net)
 
 
-def test_neighbor_model_parallel1():
+def test_neighbor_model_parallel_align_corners_shard_HW():
+    """
+    Feature: test ResizeNearestNeighbor model parallel strategy
+    Description: the align_corners is True, and shard N/C/H/W
+    Expectation: compile failed
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
+    strategy2 = ((2, 2, 2, 2),)
+    net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1, align_corners=True,
+               strategy1=strategy1, strategy2=strategy2)
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_neighbor_out_strategy():
+    """
+    Feature: test ResizeNearestNeighbor to set output parallel strategy
+    Description:
+    Expectation: compile failed
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
+    strategy2 = ((2, 2, 2, 2),)
+    out_strategy = ((2, 2, 2, 2),)
+    net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1,
+               strategy1=strategy1, strategy2=strategy2, out_strategy=out_strategy)
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_neighbor_model_parallel_align_corners_shard_NC():
+    """
+    Feature: test ResizeNearestNeighbor model parallel strategy
+    Description: the align_corners is True, and shard N/C
+    Expectation: compile success
+    """
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
+    strategy2 = ((4, 4, 1, 1),)
+    net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1, align_corners=True,
+               strategy1=strategy1, strategy2=strategy2)
+    compile_net(net)
+
+
+def test_neighbor_model_parallel_align_corners_is_false():
+    """
+    Feature: test ResizeNearestNeighbor model parallel strategy
+    Description: the align_corners is False, and shard N/C/H/W
+    Expectation: compile success
+    """
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
     strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
     strategy2 = ((2, 2, 2, 2),)
@@ -149,6 +231,11 @@ def test_neighbor_model_parallel1():
 
 
 def test_neighbor_auto_parallel():
+    """
+    Feature: test ResizeNearestNeighbor auto parallel
+    Description:
+    Expectation: compile success
+    """
     context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
     net = Net2(_w1, _w2, out_channel=8, kernel_size=2, pad_mode="same", stride=1)
     compile_net(net)