fix auto parallel gatherv2 bug

mindspore/ccsrc/parallel/ops_info/gather_v2_p_info.cc

@@ -57,6 +57,15 @@ Status GatherV2PInfo::CheckStrategy(const StrategyPtr &strategy) {
     return FAILED;
   }
 
+  // param slice shape need 32Byte aligned
+  auto param_shape = inputs_shape_.at(0);
+  auto param_strategy = strategy->GetInputDim().at(0);
+  auto slice_shape = param_shape.at(param_shape.size() - 1) / param_strategy.at(param_strategy.size() - 1);
+  if (slice_shape % 8 != 0) {
+    MS_LOG(ERROR) << name_ << ": Last dim of param slice shape need 32Byte aligned.";
+    return FAILED;
+  }
+
   // only support 1-dim and 2-dim param
   if (inputs_shape_.at(0).size() != 1 && inputs_shape_.at(0).size() != 2) {
     MS_LOG(ERROR) << name_ << ": Don't support param dim " << inputs_shape_.at(0).size();
@@ -71,14 +80,12 @@ Status GatherV2PInfo::CheckStrategy(const StrategyPtr &strategy) {
 
   // axis=0, index_shape(0)%param_strategy(0) must be 0
   Shape index_shape = inputs_shape_.at(1);
-  auto param_strategy = strategy->GetInputDim().at(0);
   if ((axis_ == 0) && (index_shape.at(0) % param_strategy.at(0) != 0)) {
     MS_LOG(ERROR) << name_ << ": index_shape(0) can't be divided by param_strategy(0).";
     return FAILED;
   }
 
   // axis != 0, param_shape(0)%(param_strategy(0)*param_strategy(axis)) must be 0
-  Shape param_shape = inputs_shape_.at(0);
   if (axis_ != 0 && param_shape.at(0) % (param_strategy.at(0) * param_strategy.at(IntToSize(axis_))) != 0) {
     MS_LOG(ERROR) << name_ << ": index_shape(0) can't be divided by (param_strategy(0)*param_strategy(axis)).";
     return FAILED;
@@ -158,12 +165,12 @@ Status GatherV2PInfo::InferDevMatrixShape() {
   } else {
     out_dev_matrix_shape_ = dev_matrix_shape_;
   }
-  auto product_out =
-    std::accumulate(out_dev_matrix_shape_.begin(), out_dev_matrix_shape_.end(), 1, std::multiplies<int>());
   CheckGlobalDeviceManager();
   size_t dev_num = g_device_manager->GetDeviceListByStageId(0).size();
-  if (product_out == 1) {
-    out_dev_matrix_shape_.insert(out_dev_matrix_shape_.begin(), dev_num);
+  auto param_product = std::accumulate(param_strategy.begin(), param_strategy.end(), 1, std::multiplies<int>());
+  auto index_product = std::accumulate(index_strategy.begin(), index_strategy.end(), 1, std::multiplies<int>());
+  if (param_product * index_product < SizeToInt(dev_num)) {
+    out_dev_matrix_shape_.insert(out_dev_matrix_shape_.begin(), SizeToInt(dev_num / (param_product * index_product)));
   }
 
   return SUCCESS;
@@ -174,7 +181,7 @@ Status GatherV2PInfo::InferTensorMap() {
   // param_strategy(axis) != 1
   size_t param_size = inputs_shape_.at(0).size();
   size_t index_size = inputs_shape_.at(1).size();
-  size_t total_size = dev_matrix_shape_.size();
+  size_t total_size = param_size + index_size;
   std::vector<int32_t> tensor_map_index;
   std::vector<int32_t> tensor_map_params;
   auto param_strategy = strategy_->GetInputDim().at(0);

tests/ut/python/parallel/test_gather_v2.py

@@ -67,8 +67,8 @@ def test_gatherv2_semi_auto0():
     net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
     net.set_auto_parallel()
 
-    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
-    y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
+    x = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
     _executor.compile(net, x, y)
 
 
@@ -79,8 +79,8 @@ def test_gatherv2_semi_auto1():
     net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
     net.set_auto_parallel()
 
-    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
-    y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
+    x = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
     _executor.compile(net, x, y)
 
 
@@ -91,8 +91,8 @@ def test_gatherv2_semi_auto2():
     net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
     net.set_auto_parallel()
 
-    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
-    y = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
+    x = Tensor(np.ones([64, 64]), dtype=ms.float32)
+    y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
     _executor.compile(net, x, y)
 
 
@@ -103,7 +103,7 @@ def test_gatherv2_semi_auto3():
     net = GradWrap(NetWithLoss(Net(1, strategy1, strategy2)))
     net.set_auto_parallel()
 
-    x = Tensor(np.ones([64, 32]), dtype=ms.float32)
+    x = Tensor(np.ones([64, 64]), dtype=ms.float32)
     y = Tensor(np.ones([64, 64, 64]), dtype=ms.float32)
     _executor.compile(net, x, y)