!3272 [AutoParallel] Adjust partition strategy of elementwise operators for...

!3272 [AutoParallel] Adjust partition strategy of elementwise operators for the implicit boardcast cases
Merge pull request !3272 from Chong/NewWideAndDeep

mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.cc

@@ -614,7 +614,6 @@ std::vector<int32_t> CopyIncomingOperatorInputStrategy(const std::vector<std::sh
 std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                                  const size_t iter_ops,
                                                                  std::vector<int32_t> basic_stra) {
-  std::vector<int32_t> s_empty = {};
   std::vector<std::vector<int32_t>> stra;
   MS_EXCEPTION_IF_NULL(ops[iter_ops]);
 
@@ -636,9 +635,99 @@ std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vect
   if (ops[iter_ops]->type() == L2_NORMALIZE) {
     return PrepareL2Normalize(ops, iter_ops, basic_stra);
   }
+  if (ops[iter_ops]->type() == TENSOR_ADD || ops[iter_ops]->type() == SUB || ops[iter_ops]->type() == MUL ||
+      ops[iter_ops]->type() == DIV) {
+    return CheckBroadcast(ops, iter_ops, basic_stra);
+  }
+
+  return CheckDivisible(ops, iter_ops, basic_stra);
+}
+
+// Function to deal with ops with broadcasting, like TensorAdd/Sub/Mul/Div etc.
+std::vector<std::vector<int32_t>> CheckBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t iter_ops, std::vector<int32_t> s) {
+  std::vector<std::vector<int32_t>> stra;
+
+  size_t first_tensor_dim = ops[iter_ops]->inputs_tensor_info()[0].shape().size();
+  size_t second_tensor_dim = ops[iter_ops]->inputs_tensor_info()[1].shape().size();
+
+  // Do Broadcasting in the second tensor.
+  if (second_tensor_dim < first_tensor_dim) {
+    bool braoadcast_first_tensor = false;
+    // Push back the first tensor's strategy.
+    stra.push_back(s);
+    // Push back the second tensor's strategy after applying broadcast.
+    stra.push_back(ApplyBroadcast(ops, iter_ops, s, second_tensor_dim, first_tensor_dim, braoadcast_first_tensor));
+  } else if (second_tensor_dim > first_tensor_dim) {  // Do Broadcasting in the first tensor.
+    bool braoadcast_first_tensor = true;
+    // Push back the first tensor's strategy after applying broadcast.
+    stra.push_back(ApplyBroadcast(ops, iter_ops, s, first_tensor_dim, second_tensor_dim, braoadcast_first_tensor));
+    // Push back the second tensor's strategy.
+    stra.push_back(s);
+  } else {  // Broadcasting can be ignored or No broadcasting needs to be applied.
+    stra = CheckDivisible(ops, iter_ops, s);
+  }
+
+  return stra;
+}
+
+std::vector<int32_t> ApplyBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
+                                    std::vector<int32_t> s, size_t target_tensor_dim, size_t refer_tensor_dim,
+                                    bool braoadcast_first_tensor) {
+  std::vector<int32_t> s_empty = {};
+  std::vector<int32_t> s_broadcast;
+  int target_tensor_index = 0;
+  int refer_tensor_index = 0;
+
+  // Indexing target and refer tensor.
+  if (braoadcast_first_tensor) {
+    target_tensor_index = 0;
+    refer_tensor_index = 1;
+  } else {
+    target_tensor_index = 1;
+    refer_tensor_index = 0;
+  }
+
+  // When target tensor with an empty dim.
+  if (target_tensor_dim == 0) {
+    return s_empty;
+  } else if (target_tensor_dim == 1) {  // When target tensor with a single dim.
+    bool broadcast_dim_found = false;
+    for (size_t iter = 0; iter < refer_tensor_dim; iter++) {
+      // Find and copy that dim's strategy from the refer tensor.
+      if ((ops[iter_ops]->inputs_tensor_info()[refer_tensor_index].shape()[iter] ==
+           ops[iter_ops]->inputs_tensor_info()[target_tensor_index].shape()[0]) &&
+          (ops[iter_ops]->inputs_tensor_info()[refer_tensor_index].shape()[iter] > 1) &&
+          (refer_tensor_dim == s.size())) {
+        s_broadcast.push_back(s.at(iter));
+        broadcast_dim_found = true;
+        break;
+      }
+    }
+    // Cannot decide which dim it is, push back one.
+    if (broadcast_dim_found == false) {
+      s_broadcast.push_back(1);
+    }
+  } else {
+    // Cannot decide which dim needs to do broadcast, push back one(s).
+    for (size_t iter = 0; iter < target_tensor_dim; iter++) {
+      s_broadcast.push_back(1);
+    }
+  }
 
+  return s_broadcast;
+}
+
+// Check whether the operator can be divided by the current strategy.
+std::vector<std::vector<int32_t>> CheckDivisible(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t iter_ops, std::vector<int32_t> basic_stra) {
+  std::vector<int32_t> s_empty = {};
+  std::vector<std::vector<int32_t>> stra;
+
+  // For all the input tensors.
   for (size_t iter_op_inputs = 0; iter_op_inputs < (size_t)ops[iter_ops]->inputs_tensor_info().size();
        iter_op_inputs++) {
+    // If input tensor is empty, return strategy as void.
     if (ops[iter_ops]->inputs_tensor_info()[iter_op_inputs].shape().size() == 0) {
       stra.push_back(s_empty);
       continue;
@@ -646,6 +735,8 @@ std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vect
 
     std::vector<int32_t> tmp_stra = basic_stra;
     bool modified = false;
+
+    // Make sure each tensor's dim shape is greater than 1. If not, push back strategy as 1 instead.
     for (size_t j = 0; j < (size_t)ops[iter_ops]->inputs_tensor_info()[iter_op_inputs].shape().size(); j++) {
       if (ops[iter_ops]->inputs_tensor_info()[iter_op_inputs].shape()[j] == 1) {
         tmp_stra[j] = 1;
@@ -658,6 +749,7 @@ std::vector<std::vector<int32_t>> GenerateStrategiesFromStrategy(const std::vect
       stra.push_back(basic_stra);
     }
   }
+
   return stra;
 }
 

mindspore/ccsrc/frontend/parallel/auto_parallel/rec_core/rec_generate_strategy.h

@@ -42,6 +42,13 @@ std::vector<std::vector<int32_t>> PrepareGatherV2(const std::vector<std::shared_
                                                   const size_t iter_ops, std::vector<int32_t> s);
 std::vector<std::vector<int32_t>> PrepareL2Normalize(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                      const size_t iter_ops, std::vector<int32_t> s);
+std::vector<std::vector<int32_t>> CheckBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t iter_ops, std::vector<int32_t> s);
+std::vector<int32_t> ApplyBroadcast(const std::vector<std::shared_ptr<OperatorInfo>> &ops, const size_t iter_ops,
+                                    std::vector<int32_t> s, size_t target_tensor_dim, size_t refer_tensor_dim,
+                                    bool braoadcast_first_tensor);
+std::vector<std::vector<int32_t>> CheckDivisible(const std::vector<std::shared_ptr<OperatorInfo>> &ops,
+                                                 const size_t iter_ops, std::vector<int32_t> s);
 std::vector<std::vector<int32_t>> MakeRecSearchStrategy(const std::shared_ptr<Graph> &graph,
                                                         const std::vector<std::shared_ptr<OperatorInfo>> &ops,
                                                         const size_t iter_graph, const size_t iter_ops);