check parameter split

mindspore/ccsrc/frontend/parallel/step_auto_parallel.cc

@@ -649,108 +649,13 @@ void ConstructCostGraphEdges(const std::vector<AnfNodePtr> &all_nodes) {
   MS_LOG(INFO) << "Constructing edges for cost graph ends.";
 }
 
-std::pair<AnfNodePtr, std::vector<AnfNodePtr>> CNodeWithRefKeys(const AnfNodePtr &cnode) {
-  MS_EXCEPTION_IF_NULL(cnode);
-  std::vector<AnfNodePtr> refkeys;
-  if (cnode->isa<CNode>()) {
-    auto cnode_ptr = cnode->cast<CNodePtr>();
-    auto inputs = cnode_ptr->inputs();
-    for (auto &one_input : inputs) {
-      if (IsValueNode<RefKey>(one_input)) {
-        refkeys.push_back(one_input);
-      }
-    }
-    if (refkeys.size() >= 1) {
-      return std::make_pair(cnode, refkeys);
-    }
-  }
-  return {nullptr, refkeys};
-}
-
 void AugmentCostGraph(const std::vector<AnfNodePtr> &all_nodes) {
   // Step 3
   for (auto &node : all_nodes) {
-    auto cnode_with_refkeys = CNodeWithRefKeys(node);
-    if ((!node->isa<Parameter>()) && (cnode_with_refkeys.first == nullptr)) {
-      continue;
-    }
-    std::string parameter_name;
-    AnfNodePtr target_parameter = nullptr;
-    AnfNodeIndexSet target_set;
-
-    if (cnode_with_refkeys.first != nullptr) {
-      // Dealing with the RefKey case
-      auto refkeys = cnode_with_refkeys.second;
-      auto cnode = cnode_with_refkeys.first;
-
-      auto cnode_ptr = cnode->cast<CNodePtr>();
-      if (cnode_ptr == nullptr || !IsValueNode<Primitive>(cnode_ptr->input(0))) {
-        continue;
-      }
-      if (!IsAutoParallelCareNode(cnode_ptr)) {
-        continue;
-      }
-
-      if (refkeys.size() > 1) {
-        MS_LOG(EXCEPTION) << "CNode: " << cnode->fullname_with_scope() << " 's inputs have more than 1 RefKeys.";
-      }
-      MS_EXCEPTION_IF_NULL(cnode->func_graph());
-      auto cnode_func_graph = cnode->func_graph();
-      MS_EXCEPTION_IF_NULL(cnode->func_graph()->manager());
-
-      // Find the RefKey being used
-      auto candidate_set_by_refkey = cnode_func_graph->manager()->node_users()[refkeys[0]];
-      for (auto &candidate : candidate_set_by_refkey) {
-        auto candidate_node = candidate.first;
-        auto c = candidate_node->cast<CNodePtr>();
-        if (c == nullptr || !IsValueNode<Primitive>(c->input(0))) {
-          continue;
-        }
-        if (!IsAutoParallelCareNode(c)) {
-          continue;
-        }
-        target_set.add(candidate);
-      }
-
-      // Find the corresponding Parameter being used
-      std::vector<AnfNodePtr> parameters = FindParameterByRefKeyNode(refkeys[0], cnode_func_graph);
-      if (parameters.size() != 1) {
-        MS_LOG(EXCEPTION) << "Find parameter by ref key node failed";
-      }
-      parameter_name = parameters[0]->cast<ParameterPtr>()->name();
-      target_parameter = parameters[0];
-      auto candidate_set_by_para = cnode_func_graph->manager()->node_users()[parameters[0]];
-      for (auto &candidate : candidate_set_by_para) {
-        auto candidate_node = candidate.first;
-        auto c = candidate_node->cast<CNodePtr>();
-        if (c == nullptr || !IsValueNode<Primitive>(c->input(0))) {
-          continue;
-        }
-        if (!IsAutoParallelCareNode(c)) {
-          continue;
-        }
-        (void)target_set.insert(candidate);
-      }
-    } else if (node->isa<Parameter>()) {
-      // Dealing with the Parameter case
-      MS_EXCEPTION_IF_NULL(node->func_graph());
-      MS_EXCEPTION_IF_NULL(node->func_graph()->manager());
-      auto candidate_set = node->func_graph()->manager()->node_users()[node];
-      for (auto &candidate : candidate_set) {
-        auto candidate_node = candidate.first;
-        auto c = candidate_node->cast<CNodePtr>();
-        if (c == nullptr || !IsValueNode<Primitive>(c->input(0))) {
-          continue;
-        }
-        if (!IsAutoParallelCareNode(c)) {
-          continue;
-        }
-        (void)target_set.insert(candidate);
-      }
-      // In this case, node is a Parameter
-      parameter_name = node->cast<ParameterPtr>()->name();
-      target_parameter = node;
-    }
+    ParameterUsersInfo parameter_users_info = FindParameterUsers(node, IsAutoParallelCareNode);
+    auto parameter_name = parameter_users_info.first;
+    auto target_parameter = parameter_users_info.second.first;
+    auto target_set = parameter_users_info.second.second;
     if (target_set.size() <= 1) {
       continue;
     }

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -2499,6 +2499,149 @@ void HandleForwardMakeTuple(const std::vector<AnfNodePtr> &all_nodes) {
   }
 }
 
+RefKeyPair CNodeWithRefKeys(const AnfNodePtr &cnode) {
+  MS_EXCEPTION_IF_NULL(cnode);
+  std::vector<AnfNodePtr> refkeys;
+  if (cnode->isa<CNode>()) {
+    auto cnode_ptr = cnode->cast<CNodePtr>();
+    auto inputs = cnode_ptr->inputs();
+    for (auto &one_input : inputs) {
+      if (IsValueNode<RefKey>(one_input)) {
+        refkeys.push_back(one_input);
+      }
+    }
+    if (refkeys.size() >= 1) {
+      return std::make_pair(cnode, refkeys);
+    }
+  }
+  return {nullptr, refkeys};
+}
+
+ParameterUsersInfo FindParameterNodeUsers(const AnfNodePtr &node, bool (*IsCareNode)(const CNodePtr &)) {
+  // In this case, node is a Parameter
+  ParameterUsersInfo parameter_user_info;
+  MS_EXCEPTION_IF_NULL(node->func_graph());
+  MS_EXCEPTION_IF_NULL(node->func_graph()->manager());
+  auto candidate_set = node->func_graph()->manager()->node_users()[node];
+  for (auto &candidate : candidate_set) {
+    auto candidate_node = candidate.first;
+    auto c = candidate_node->cast<CNodePtr>();
+    if (c == nullptr || !IsValueNode<Primitive>(c->input(0)) || !IsCareNode(c)) {
+      continue;
+    }
+    (void)parameter_user_info.second.second.insert(candidate);
+  }
+
+  parameter_user_info.first = node->cast<ParameterPtr>()->name();
+  parameter_user_info.second.first = node;
+  return parameter_user_info;
+}
+
+ParameterUsersInfo FindRefKeyNodeUsers(const RefKeyPair &ref_key_pair, bool (*IsCareNode)(const CNodePtr &)) {
+  // Dealing with the RefKey case
+  ParameterUsersInfo parameter_user_info;
+  auto refkeys = ref_key_pair.second;
+  auto cnode = ref_key_pair.first;
+
+  auto cnode_ptr = cnode->cast<CNodePtr>();
+  if ((cnode_ptr == nullptr) || !IsValueNode<Primitive>(cnode_ptr->input(0)) || !IsCareNode(cnode_ptr)) {
+    return parameter_user_info;
+  }
+
+  if (refkeys.size() > 1) {
+    MS_LOG(EXCEPTION) << "CNode: " << cnode->fullname_with_scope() << "'s inputs have more than 1 RefKeys";
+  }
+  MS_EXCEPTION_IF_NULL(cnode->func_graph());
+  auto cnode_func_graph = cnode->func_graph();
+  MS_EXCEPTION_IF_NULL(cnode->func_graph()->manager());
+
+  // Find the RefKey being used
+  auto candidate_set_by_refkey = cnode_func_graph->manager()->node_users()[refkeys[0]];
+  for (auto &candidate : candidate_set_by_refkey) {
+    auto candidate_node = candidate.first;
+    auto c = candidate_node->cast<CNodePtr>();
+    if ((c == nullptr) || !IsValueNode<Primitive>(c->input(0)) || !IsCareNode(c)) {
+      continue;
+    }
+    parameter_user_info.second.second.add(candidate);
+  }
+
+  // Find the corresponding Parameter being used
+  std::vector<AnfNodePtr> parameters = FindParameterByRefKeyNode(refkeys[0], cnode_func_graph);
+  if (parameters.size() != 1) {
+    MS_LOG(EXCEPTION) << "Find parameter by ref key node failed";
+  }
+  parameter_user_info.first = parameters[0]->cast<ParameterPtr>()->name();
+  parameter_user_info.second.first = parameters[0];
+  auto candidate_set_by_para = cnode_func_graph->manager()->node_users()[parameters[0]];
+  for (auto &candidate : candidate_set_by_para) {
+    auto candidate_node = candidate.first;
+    auto c = candidate_node->cast<CNodePtr>();
+    if ((c == nullptr) || !IsValueNode<Primitive>(c->input(0)) || !IsCareNode(c)) {
+      continue;
+    }
+    (void)parameter_user_info.second.second.insert(candidate);
+  }
+  return parameter_user_info;
+}
+
+ParameterUsersInfo FindParameterUsers(const AnfNodePtr &node, bool (*IsCareNode)(const CNodePtr &)) {
+  ParameterUsersInfo parameter_users_info;
+  auto cnode_with_refkeys = CNodeWithRefKeys(node);
+
+  if (cnode_with_refkeys.first != nullptr) {
+    // the node is a ref key node
+    return FindRefKeyNodeUsers(cnode_with_refkeys, IsCareNode);
+  } else if (node->isa<Parameter>()) {
+    // the node is a parameter node
+    return FindParameterNodeUsers(node, IsCareNode);
+  }
+
+  return parameter_users_info;
+}
+
+Shape ParameterSliceShape(const std::pair<AnfNodePtr, int> &param_info) {
+  auto user_cnode = param_info.first->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(user_cnode);
+  auto user_input_index = param_info.second;
+  OperatorInfoPtr op_info = user_cnode->user_data<OperatorInfo>();
+  MS_EXCEPTION_IF_NULL(op_info);
+
+  size_t input_tensor_info_size = op_info->inputs_tensor_info().size();
+  if (SizeToInt(input_tensor_info_size) <= user_input_index - 1) {
+    MS_LOG(EXCEPTION) << op_info->name() << ": the size of inputs tensor info is " << input_tensor_info_size
+                      << ", but the index is " << user_input_index - 1;
+  }
+  TensorInfo tensor_info = op_info->inputs_tensor_info()[user_input_index - 1];
+  MS_LOG(DEBUG) << "The op name is " << op_info->name() << ", the parameter index is " << user_input_index - 1
+                << ", the slice shape is " << ShapeToString(tensor_info.slice_shape()) << ", the origin shape is "
+                << ShapeToString(tensor_info.shape());
+  return tensor_info.slice_shape();
+}
+
+void CheckParameterSplit(const std::vector<AnfNodePtr> &all_nodes) {
+  for (auto &node : all_nodes) {
+    ParameterUsersInfo parameter_users_info = FindParameterUsers(node, IsParallelCareNode);
+    auto users_set = parameter_users_info.second.second;
+    if (users_set.size() <= 1) {
+      continue;
+    }
+
+    auto parameter_name = parameter_users_info.first;
+    MS_LOG(INFO) << "The parameter: " << parameter_name << " has " << users_set.size() << " users";
+    auto first_user = users_set.pop();
+    Shape first_user_slice_shape = ParameterSliceShape(first_user);
+
+    for (auto &user : users_set) {
+      Shape user_slice_shape = ParameterSliceShape(user);
+      if (first_user_slice_shape != user_slice_shape) {
+        MS_LOG(EXCEPTION) << "The parameter: " << parameter_name
+                          << " has multiple users, but the split strategies are different";
+      }
+    }
+  }
+}
+
 bool StepParallel(const FuncGraphPtr &root, const opt::OptimizerPtr &optimizer) {
   MS_EXCEPTION_IF_NULL(root);
   MS_EXCEPTION_IF_NULL(optimizer);
@@ -2556,6 +2699,9 @@ bool StepParallel(const FuncGraphPtr &root, const opt::OptimizerPtr &optimizer)
 
   HandleForwardMakeTuple(all_nodes);
 
+  // if the input or parameter has multiple users, check whether its split strategies are consistent.
+  CheckParameterSplit(all_nodes);
+
   // save strategy as checkpoint for multi-train
   if (StrategyCheckpoint::GetInstance().SaveCheckPointOn()) {
     CheckpointStrategy(all_nodes);

mindspore/ccsrc/frontend/parallel/step_parallel.h

@@ -150,6 +150,13 @@ std::vector<std::string> ExtractInputsTensorName(const CNodePtr &node);
 std::set<FuncGraphPtr> ForwardGraph(const FuncGraphPtr &root);
 
 bool AnfNodeIsPrimitive(const AnfNodePtr &anf_node, const std::string &prim_name);
+
+using RefKeyPair = std::pair<AnfNodePtr, std::vector<AnfNodePtr>>;
+using ParameterUsersInfo = std::pair<std::string, std::pair<AnfNodePtr, AnfNodeIndexSet>>;
+
+RefKeyPair CNodeWithRefKeys(const AnfNodePtr &cnode);
+
+ParameterUsersInfo FindParameterUsers(const AnfNodePtr &node, bool (*IsCareNode)(const CNodePtr &));
 }  // namespace parallel
 }  // namespace mindspore
 

tests/ut/python/parallel/test_auto_parallel_reshape.py

@@ -245,51 +245,3 @@ def test_reshape_auto_5():
     context.set_auto_parallel_context(parallel_mode="auto_parallel")
     net.set_auto_parallel()
     _executor.compile(net, x, y)
-
-
-def test_reshape_auto_6():
-    class NetWithLoss6(nn.Cell):
-        def __init__(self, network):
-            super(NetWithLoss6, self).__init__()
-            self.loss = VirtualLoss()
-            self.network = network
-
-        def construct(self, x, y):
-            predict = self.network(x, y)
-            return self.loss(predict)
-
-    class GradWrap6(nn.Cell):
-        def __init__(self, network):
-            super(GradWrap6, self).__init__()
-            self.network = network
-
-        def construct(self, x, y):
-            return grad_all(self.network)(x, y)
-
-    class Net(nn.Cell):
-        def __init__(self):
-            super().__init__()
-            self.relu = P.ReLU()
-            self.mul = P.Mul()
-            self.reshape = P.Reshape()
-            self.reduce_mean = P.ReduceMean()
-            self.wide_w = Parameter(Tensor(np.ones([4, 1024, 1]), dtype=ms.float32), name="weight")
-
-        def construct(self, x, y):
-            out1 = x + self.wide_w
-            w = self.reshape(self.wide_w, (4, 1024))
-            out1 = self.reduce_mean(out1, 1)
-            out1 = out1 - w
-            out2 = self.mul(y, w)
-            out = out1 + out2
-            return out
-
-    size = 8
-    context.set_auto_parallel_context(device_num=size, global_rank=0)
-    x = Tensor(np.ones([4, 1024, 1]), dtype=ms.float32)
-    y = Tensor(np.ones([4, 1024,]), dtype=ms.float32)
-
-    net = GradWrap6(NetWithLoss6(Net()))
-    context.set_auto_parallel_context(parallel_mode="auto_parallel")
-    net.set_auto_parallel()
-    _executor.compile(net, x, y)

tests/ut/python/parallel/test_parameter_multi_users.py

@@ -0,0 +1,94 @@
+# Copyright 2020 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.
+
+import numpy as np
+import pytest
+import mindspore as ms
+from mindspore import context, Tensor, Parameter
+from mindspore.common.api import _executor
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+
+
+class Net(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.mul2 = P.Mul().set_strategy(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.mul2(out, self.mul_weight)
+        return out
+
+
+class Net2(Cell):
+    def __init__(self, mul_weight, strategy1=None, strategy2=None):
+        super().__init__()
+        self.mul = P.Mul().set_strategy(strategy1)
+        self.mul2 = P.Mul().set_strategy(strategy2)
+        self.mul_weight = Parameter(mul_weight, "w1")
+
+    def construct(self, x, b):
+        out = self.mul(x, self.mul_weight)
+        out = self.mul2(x, out)
+        return out
+
+
+_x = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_w = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+_b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
+
+
+def compile_net(net):
+    optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
+    train_net = TrainOneStepCell(net, optimizer)
+    train_net.set_auto_parallel()
+    _executor.compile(train_net, _x, _b)
+    context.reset_auto_parallel_context()
+
+
+def test_parameter_same_split():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((16, 1, 1), (16, 1, 1))
+    net = Net(_w, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_parameter_different_split():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((4, 4, 1), (4, 4, 1))
+    net = Net(_w, strategy1, strategy2)
+    with pytest.raises(RuntimeError):
+        compile_net(net)
+
+
+def test_input_same_split():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((16, 1, 1), (16, 1, 1))
+    net = Net(_w, strategy1, strategy2)
+    compile_net(net)
+
+
+def test_input_different_split():
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
+    strategy1 = ((16, 1, 1), (16, 1, 1))
+    strategy2 = ((4, 4, 1), (4, 4, 1))
+    net = Net2(_w, strategy1, strategy2)
+    with pytest.raises(RuntimeError):
+        compile_net(net)