!15766 modify tile op

From: @yangzhenzhang
Reviewed-by: @kisnwang,@stsuteng
Signed-off-by: @stsuteng

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

@@ -45,7 +45,6 @@ class SelectInfo : public OperatorInfo {
  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;

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

@@ -65,8 +65,19 @@ Status TileInfo::GetAttrs() {
   return SUCCESS;
 }
 
+// if some dimension of multiples > 1, split the multiple; otherwise, split the dimension of input
 Status TileInfo::CheckStrategy(const StrategyPtr &strategy) {
-  Shapes multiples = {full_multiples_};
+  Shape tmp;
+  for (size_t i = 0; i < full_multiples_.size(); ++i) {
+    if (full_multiples_[i] != 1) {
+      tmp.push_back(full_multiples_[i]);
+    } else {
+      tmp.push_back(inputs_shape_[0][i]);
+    }
+  }
+  Shapes multiples = {tmp};
+  MS_LOG(INFO) << name_ << ": The input shape is " << ShapeToString(inputs_shape_[0]) << ", the multiples is "
+               << ShapeToString(full_multiples_) << ", so the 'shape' can be split is " << ShapeToString(tmp);
   return CheckStrategyValue(strategy, multiples);
 }
 
@@ -74,7 +85,7 @@ Status TileInfo::InferDevMatrixShape() {
   MS_EXCEPTION_IF_NULL(strategy_);
   std::vector<Dimensions> stra = strategy_->GetInputDim();
   if (stra.empty()) {
-    MS_LOG(ERROR) << name_ << "The strategy is empty";
+    MS_LOG(ERROR) << name_ << ": The strategy is empty";
     return FAILED;
   }
   if (full_multiples_.size() != stra[0].size()) {
@@ -86,6 +97,9 @@ Status TileInfo::InferDevMatrixShape() {
 
   slice_multiples_ = full_multiples_;
   for (size_t i = 0; i < full_multiples_.size(); ++i) {
+    if (full_multiples_[i] == 1) {
+      continue;
+    }
     slice_multiples_[i] = slice_multiples_[i] / dev_matrix_shape_[i];
   }
   return SUCCESS;
@@ -95,13 +109,18 @@ Status TileInfo::InferTensorMap() {
   TensorMap input_tensor_map;
   TensorMap output_tensor_map;
   if (inputs_shape_.empty() || outputs_shape_.empty()) {
-    MS_LOG(ERROR) << name_ << "The inputs or outputs' shape is empty";
+    MS_LOG(ERROR) << name_ << ": The inputs or outputs' shape is empty";
     return FAILED;
   }
 
-  // the input tensor cannot be split
+  // if some dimension of multiples > 1, split the multiple; otherwise, split the dimension of input
   for (size_t i = 0; i < inputs_shape_[0].size(); ++i) {
-    input_tensor_map.push_back(MAP_NONE);
+    input_tensor_map.push_back(inputs_shape_[0].size() - i - 1);
+  }
+  for (size_t i = 0; i < inputs_shape_[0].size(); ++i) {
+    if (full_multiples_[i] != 1) {
+      input_tensor_map[i] = MAP_NONE;
+    }
   }
 
   // cannot use dev_matrix_shape_ replace outputs_shape_[0], because it may not be fully split in all devices.
@@ -163,11 +182,11 @@ Status TileInfo::InferTensorInfo() {
 void TileInfo::UpdateMultiples(const CNodePtr &cnode) {
   MS_EXCEPTION_IF_NULL(cnode);
   if (cnode->size() != 3) {
-    MS_LOG(EXCEPTION) << "The size of tile cnode's inputs must be 3";
+    MS_LOG(EXCEPTION) << name_ << ": The size of tile cnode's inputs must be 3";
   }
 
   if (!IsValueNode<ValueTuple>(cnode->input(2))) {
-    MS_LOG(EXCEPTION) << "The input[2] of tile cnode is not ValueTuple.";
+    MS_LOG(EXCEPTION) << name_ << ": The input[2] of tile cnode is not ValueTuple.";
   }
 
   auto func_graph = cnode->func_graph();
@@ -199,7 +218,14 @@ Status TileInfo::GenerateStrategies(int64_t stage_id) {
   Shape multiples_split(full_multiples_.size(), 1);
   Shapes splittable_inputs = {multiples_split};
 
+  // if some dimension of multiples > 1, split the multiple; otherwise, split the dimension of input
   std::vector<StrategyPtr> sp_vector;
+  Shape tmp_input_shape = full_multiples_;
+  for (size_t i = 0; i < full_multiples_.size(); ++i) {
+    if (full_multiples_[i] == 0) {
+      tmp_input_shape[i] = inputs_shape_[0][i];
+    }
+  }
   Shapes tmp_inputs_shape = {full_multiples_};
   if (GenerateStrategiesForIndependentInputs(stage_id, tmp_inputs_shape, splittable_inputs, &sp_vector) != SUCCESS) {
     return FAILED;

tests/ut/python/parallel/test_select.py

@@ -94,6 +94,15 @@ def test_select_model_parallel():
     compile_net(net)
 
 
+def test_select_mirror():
+    context.set_auto_parallel_context(
+        parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((1, 2, 2), (1, 2, 2))
+    strategy2 = ((1, 2, 2), (1, 2, 2), (1, 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)

tests/ut/python/parallel/test_tile.py

@@ -52,20 +52,38 @@ class Net2(Cell):
         out = self.tile(out, (8, 8, 4, 2))
         return out
 
+class Net3(Cell):
+    def __init__(self, weight, strategy1=None, strategy2=None, is_parameter=True):
+        super().__init__()
+        self.mul = P.Mul().shard(strategy1)
+        self.tile = P.Tile().shard(strategy2)
+        if is_parameter:
+            self.weight = Parameter(weight, "w1")
+        else:
+            self.weight = weight
+        self.mul2 = P.Mul()
+
+    def construct(self, x, b):
+        out = self.tile(self.weight, (8, 1, 1))
+        out = self.mul(x, out)
+        return out
+
 
 _x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_x1 = Tensor(np.ones([128, 16, 16]), dtype=ms.float32)
 _w1 = Tensor(np.ones([16, 16, 16]), dtype=ms.float32)
 _w2 = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w3 = Tensor(np.ones([128, 16, 16]), dtype=ms.float32)
 _b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
 
 
-def compile_net(net):
+def compile_net(net, x=_b, b=_b):
-    context.set_context(save_graphs=False)
+    context.set_context(save_graphs=True)
     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, _b)
+    _executor.compile(train_net, x, b)
     context.reset_auto_parallel_context()
 
 
@@ -101,6 +119,14 @@ def test_tile_tensor_no_full_split():
     compile_net(net)
 
 
+def test_tile_tensor_no_full_split2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    strategy1 = ((2, 2, 1), (2, 2, 1))
+    strategy2 = ((2, 2, 1),)
+    net = Net3(_w1, strategy1, strategy2)
+    compile_net(net, _x1, _b)
+
+
 def test_tile_output():
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((2, 2, 2), (2, 2, 2))
@@ -108,6 +134,7 @@ def test_tile_output():
     net = Net2(_w2, strategy1, strategy2)
     compile_net(net)
 
+
 def test_tile_output_no_full_split():
     context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
     strategy1 = ((2, 2, 2), (2, 2, 2))
@@ -123,7 +150,14 @@ def test_tile_no_strategy():
     net = Net2(_w2, strategy1, strategy2)
     compile_net(net)
 
+
 def test_tile_auto_parallel():
     context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
     net = Net2(_w2)
     compile_net(net)
+
+
+def test_tile_auto_parallel_2():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
+    net = Net3(_w1)
+    compile_net(net, _x1, _b)