fix_auto_parallel_find_loss_bug

mindspore/ccsrc/frontend/parallel/step_parallel.cc

@@ -711,61 +711,6 @@ int32_t GetTupleGetItemIndex(const CNodePtr &cnode) {
   return tuple_index_value->cast<Int32ImmPtr>()->value();
 }
 
-// Judge whether the node is a loss, and if there are multiple outputs,
-// get which output is a grad according to the tuple getitem.
-// Currently, it is not supported that the sens is a tuple.
-LossNodeInfo GetLossNodeInfo(const AnfNodePtr &loss_node) {
-  MS_EXCEPTION_IF_NULL(loss_node);
-  FuncGraphPtr sub_graph = loss_node->func_graph();
-  MS_EXCEPTION_IF_NULL(sub_graph);
-  CNodePtr return_node = sub_graph->get_return();
-  MS_EXCEPTION_IF_NULL(return_node);
-  if (return_node->inputs().size() < 2) {
-    MS_LOG(EXCEPTION) << "Failure: " << return_node->ToString() << " size is smaller than 2";
-  }
-  AnfNodePtr pre_node = return_node->input(1);
-  MS_EXCEPTION_IF_NULL(pre_node);
-
-  LossNodeInfo node_info;
-
-  // return -> cast
-  auto pre_cnode = pre_node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(pre_cnode);
-  auto pre_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
-  if (pre_prim->name() == CAST && !pre_cnode->has_user_data<OperatorInfo>()) {
-    pre_node = pre_cnode->input(1);
-  }
-
-  // return -> loss
-  if (pre_node == loss_node) {
-    node_info.has_tuple_getitem = false;
-    node_info.dout_index = 0;
-    return node_info;
-  }
-
-  // return -> tuple_getitem -> loss
-  auto cnode = pre_node->cast<CNodePtr>();
-  MS_EXCEPTION_IF_NULL(cnode);
-  auto current_value = cnode->input(0)->cast<ValueNodePtr>();
-  MS_EXCEPTION_IF_NULL(current_value);
-  PrimitivePtr current_prim = current_value->value()->cast<PrimitivePtr>();
-  MS_EXCEPTION_IF_NULL(current_prim);
-  // size of common cnode is larger than 1
-  if (cnode->inputs().size() < 2) {
-    MS_LOG(EXCEPTION) << cnode->ToString() << " size( " << cnode->inputs().size() << " ) is smaller than 2";
-  }
-
-  if ((current_prim->name() == TUPLE_GETITEM) && (cnode->input(1) == loss_node)) {
-    // size of tuple_getitem cnode is 3
-    auto tuple_index = GetTupleGetItemIndex(cnode);
-    node_info.has_tuple_getitem = true;
-    node_info.dout_index = tuple_index;
-    return node_info;
-  }
-
-  MS_LOG(EXCEPTION) << "Invalid loss";
-}
-
 void InsertVirtualDivOp(const VirtualDivOp &virtual_div_op, const CNodePtr &node) {
   MS_EXCEPTION_IF_NULL(node);
   size_t node_size = node->inputs().size();
@@ -958,13 +903,13 @@ void InsertMirrorOps(const MirrorOps &mirror_ops, const CNodePtr &node) {
 }
 
 void BackwardCommunication(const OperatorInfoPtr &distribute_operator, const CNodePtr &node,
-                           const std::vector<std::pair<CNodePtr, CNodePtr>> &sens_loss_pairs) {
+                           const std::vector<std::pair<CNodePtr, LossNodeInfo>> &sens_loss_pairs) {
   MS_EXCEPTION_IF_NULL(distribute_operator);
   MS_EXCEPTION_IF_NULL(node);
 
   bool is_loss_cnode =
     std::any_of(sens_loss_pairs.begin(), sens_loss_pairs.end(),
-                [node](const std::pair<CNodePtr, CNodePtr> &element) { return element.second == node; });
+                [node](const std::pair<CNodePtr, LossNodeInfo> &element) { return element.second.loss_node == node; });
 
   MirrorOps mirror_ops = distribute_operator->mirror_ops();
   VirtualDivOp virtual_div_op = distribute_operator->virtual_div_op();
@@ -1819,7 +1764,20 @@ void ReshapeInit(const std::vector<AnfNodePtr> &all_nodes) {
   }
 }
 
-CNodePtr FindLossCNode(const FuncGraphPtr &func_graph) {
+CNodePtr HandleDependLoss(const CNodePtr &cnode) {
+  // Handle return->depend->loss
+  auto prim = GetValueNode<PrimitivePtr>(cnode->input(0));
+  MS_EXCEPTION_IF_NULL(prim);
+  if (prim->name() == DEPEND) {
+    auto depend_before = cnode->input(1)->cast<CNodePtr>();
+    MS_EXCEPTION_IF_NULL(depend_before);
+    return HandleDependLoss(depend_before);
+  }
+  return cnode;
+}
+
+LossNodeInfo FindLossCNode(const FuncGraphPtr &func_graph) {
+  LossNodeInfo loss_node_info;
   MS_EXCEPTION_IF_NULL(func_graph);
   CNodePtr return_node = func_graph->get_return();
   MS_EXCEPTION_IF_NULL(return_node);
@@ -1831,9 +1789,9 @@ CNodePtr FindLossCNode(const FuncGraphPtr &func_graph) {
 
   auto pre_cnode = pre_node->cast<CNodePtr>();
   if (pre_cnode == nullptr) {
-    return nullptr;
+    return loss_node_info;
   }
-
+  pre_cnode = HandleDependLoss(pre_cnode);
   auto current_prim = GetValueNode<PrimitivePtr>(pre_cnode->input(0));
   // return -> cast
   if (current_prim->name() == CAST && !pre_cnode->has_user_data<OperatorInfo>()) {
@@ -1845,7 +1803,8 @@ CNodePtr FindLossCNode(const FuncGraphPtr &func_graph) {
   // notice: the GetNext op has not input
   if (INVALID_LOSS_OPS.find(current_prim->name()) != INVALID_LOSS_OPS.end()) {
     MS_LOG(INFO) << "The loss is: " << current_prim->name();
-    return pre_cnode;
+    loss_node_info.loss_node = pre_cnode;
+    return loss_node_info;
   }
 
   // size of common cnode is larger than 1
@@ -1855,36 +1814,34 @@ CNodePtr FindLossCNode(const FuncGraphPtr &func_graph) {
 
   // return -> tuple_getitem -> loss
   if (current_prim->name() == TUPLE_GETITEM) {
+    auto tuple_index = GetTupleGetItemIndex(pre_cnode);
     AnfNodePtr pre_pre_node = pre_cnode->input(1);
     MS_EXCEPTION_IF_NULL(pre_pre_node);
 
     auto pre_pre_cnode = pre_pre_node->cast<CNodePtr>();
-    auto value = pre_pre_cnode->input(0)->cast<ValueNodePtr>();
-    MS_EXCEPTION_IF_NULL(value);
-    PrimitivePtr prim = value->value()->cast<PrimitivePtr>();
-    MS_EXCEPTION_IF_NULL(prim);
-    MS_LOG(DEBUG) << "The loss name is " << prim->name();
-    return pre_pre_cnode;
+    loss_node_info.has_tuple_getitem = true;
+    loss_node_info.dout_index = tuple_index;
+    loss_node_info.loss_node = pre_pre_cnode;
+    return loss_node_info;
   }
 
   // return -> make_tuple
   if (current_prim->name() == MAKE_TUPLE) {
     MS_LOG(WARNING) << "The loss have make_tuple, it is not supported";
-    return nullptr;
+    return loss_node_info;
   }
 
   // return -> loss
+  loss_node_info.loss_node = pre_cnode;
   MS_LOG(DEBUG) << "The loss name is " << current_prim->name();
-  return pre_cnode;
+  return loss_node_info;
 }
 
-TensorLayouts GetLossNodeGradOutputLayout(const CNodePtr &loss_cnode) {
+TensorLayouts GetLossNodeGradOutputLayout(const LossNodeInfo &node_info) {
   TensorLayouts ret;
+  auto loss_cnode = node_info.loss_node;
   MS_EXCEPTION_IF_NULL(loss_cnode);
-  AnfNodePtr node = loss_cnode->cast<AnfNodePtr>();
-  MS_EXCEPTION_IF_NULL(node);
 
-  LossNodeInfo node_info = GetLossNodeInfo(node);
   ValueNodePtr prim_anf_node = loss_cnode->input(0)->cast<ValueNodePtr>();
   MS_EXCEPTION_IF_NULL(prim_anf_node);
   PrimitivePtr prim = prim_anf_node->value()->cast<PrimitivePtr>();
@@ -2086,9 +2043,9 @@ std::set<FuncGraphPtr> FindForwardGraphByRootNodes(const AnfNodeSet &root_all_no
   return graph_set;
 }
 
-void StepSplitSens(const std::pair<CNodePtr, CNodePtr> &sens_loss_pair) {
+void StepSplitSens(const std::pair<CNodePtr, LossNodeInfo> &sens_loss_pair) {
   CNodePtr sens_node = sens_loss_pair.first;
-  CNodePtr loss_node = sens_loss_pair.second;
+  auto loss_node = sens_loss_pair.second;
   auto loss_grad_layout = GetLossNodeGradOutputLayout(loss_node);
   if (!loss_grad_layout.empty()) {
     SplitSens(sens_node, loss_grad_layout[0]);
@@ -2096,9 +2053,9 @@ void StepSplitSens(const std::pair<CNodePtr, CNodePtr> &sens_loss_pair) {
 }
 
 // Sens node satisfies the following conditions: cnode(sens)-->cnode(tuple_getitem)-->cnode-->cnode(J)
-std::vector<std::pair<CNodePtr, CNodePtr>> GetSensLossPairs(const FuncGraphPtr &root) {
+std::vector<std::pair<CNodePtr, LossNodeInfo>> GetSensLossPairs(const FuncGraphPtr &root) {
   MS_EXCEPTION_IF_NULL(root);
-  std::vector<std::pair<CNodePtr, CNodePtr>> sens_loss_pairs;
+  std::vector<std::pair<CNodePtr, LossNodeInfo>> sens_loss_pairs;
   for (auto &node : root->nodes()) {
     if (!node->isa<CNode>()) {
       continue;
@@ -2140,12 +2097,12 @@ std::vector<std::pair<CNodePtr, CNodePtr>> GetSensLossPairs(const FuncGraphPtr &
       MS_LOG(EXCEPTION) << "Sens can't find the corresponding graph.";
     }
     auto func_graph = GetValueNode<FuncGraphPtr>(expect_j_cnode->input(1));
-    auto loss_cnode = FindLossCNode(func_graph);
-    if (loss_cnode == nullptr) {
+    auto loss_node_info = FindLossCNode(func_graph);
+    if (loss_node_info.loss_node == nullptr) {
       MS_LOG(WARNING) << "Can not find the loss cnode";
       continue;
     }
-    std::pair<CNodePtr, CNodePtr> sens_loss_pair = std::make_pair(sens_cnode, loss_cnode);
+    std::pair<CNodePtr, LossNodeInfo> sens_loss_pair = std::make_pair(sens_cnode, loss_node_info);
     sens_loss_pairs.push_back(sens_loss_pair);
   }
   return sens_loss_pairs;
@@ -2157,7 +2114,7 @@ void ParallelCommunication(const FuncGraphPtr &root, const std::vector<AnfNodePt
   MS_EXCEPTION_IF_NULL(manager);
   TensorRedistribution tensor_redistribution;
 
-  std::vector<std::pair<CNodePtr, CNodePtr>> sens_loss_pairs = GetSensLossPairs(root);
+  std::vector<std::pair<CNodePtr, LossNodeInfo>> sens_loss_pairs = GetSensLossPairs(root);
   bool has_backward = !sens_loss_pairs.empty();
   // split sens must before inserting the operators.
   for (auto &pair : sens_loss_pairs) {
@@ -2372,7 +2329,7 @@ std::set<FuncGraphPtr> ForwardGraph(const FuncGraphPtr &root) {
 std::vector<AnfNodePtr> FindRootForwardCNode(const FuncGraphPtr &graph, const AnfNodeSet &all_nodes) {
   MS_EXCEPTION_IF_NULL(graph);
   std::vector<AnfNodePtr> root_forward_nodes;
-  auto loss_cnode = FindLossCNode(graph);
+  auto loss_cnode = FindLossCNode(graph).loss_node;
   if (loss_cnode == nullptr) {
     MS_LOG(WARNING) << "Can not find the loss cnode";
     return root_forward_nodes;

mindspore/ccsrc/frontend/parallel/step_parallel.h

@@ -39,6 +39,7 @@ const uint64_t kUSecondInSecond = 1000000;
 struct LossNodeInfo {
   bool has_tuple_getitem = false;
   int dout_index = 0;  // now don't support the sens is a tuple
+  CNodePtr loss_node = nullptr;
 };
 
 std::vector<AnfNodePtr> CreateInput(const Operator &op, const AnfNodePtr &node, const std::string &instance_name);
@@ -82,7 +83,7 @@ std::pair<bool, CNodePtr> FindCNode(const AnfNodePtr &anode, const std::string &
 void InsertMirrorOps(const MirrorOps &mirror_ops, const CNodePtr &node);
 
 void BackwardCommunication(const OperatorInfoPtr &distribute_operator, const CNodePtr &node,
-                           const std::vector<std::pair<CNodePtr, CNodePtr>> &sens_loss_pairs);
+                           const std::vector<std::pair<CNodePtr, LossNodeInfo>> &sens_loss_pairs);
 
 // Generate and init parallel operator
 OperatorInfoPtr OperatorInstance(const PrimitivePtr &prim, const PrimitiveAttrs &attrs,

mindspore/train/model.py

@@ -32,7 +32,6 @@ from ..nn.metrics import Loss
 from .. import nn
 from ..nn.wrap.cell_wrapper import _VirtualDatasetCell
 from ..context import ParallelMode
-from ..parallel._utils import _need_to_full, _to_full_tensor
 from ..parallel._cost_model_context import _set_multi_subgraphs
 from .dataset_helper import DatasetHelper, connect_network_with_dataset
 from . import amp
@@ -436,8 +435,6 @@ class Model:
 
             # for data sink dataset_helper only iter once, other wise iter epoch_size times.
             for inputs in dataset_helper:
-                if _need_to_full() and context.get_context("device_target") == "GPU":
-                    inputs = _to_full_tensor(inputs, self._device_number, self._global_rank)
                 cb_params.train_dataset_element = inputs
                 list_callback.step_begin(run_context)
                 outputs = self._train_network(*inputs)

tests/ut/python/parallel/test_mul_div_bn.py

@@ -0,0 +1,78 @@
+# 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 mindspore as ms
+import mindspore.context as context
+from mindspore.common.api import _executor
+from mindspore import Tensor, Parameter
+import mindspore.nn as nn
+from mindspore.nn import Cell, TrainOneStepCell, Momentum
+from mindspore.ops import operations as P
+
+
+class TwoInputBpropOperator(Cell):
+    def __init__(self):
+        super().__init__()
+        self.op = P.Mul()
+        self.bp = P.TensorAdd()
+
+    def construct(self, x, y):
+        return self.op(x, y)
+
+    def bprop(self, x, y, out, dout):
+        return self.bp(5, x), self.bp(y, 8)
+
+
+class ParallelFloorDivBpropNet(Cell):
+    def __init__(self, mul_size, test_size, strategy=None, strategy2=None):
+        super().__init__()
+        mul_np = np.full(mul_size, 0.5, dtype=np.float32)
+        floordiv_np = np.full(test_size, 0.1, dtype=np.float32)
+        self.mul_weight = Parameter(Tensor(mul_np), name="mul_weight")
+        self.floordiv_weight = Parameter(Tensor(floordiv_np), name="floordiv_weight")
+        self.mul = TwoInputBpropOperator()
+        self.floor_div = P.FloorDiv()
+        self.bn = nn.BatchNorm1d(num_features=96)
+        if strategy is not None:
+            self.mul.op.shard(strategy2)
+            self.mul.bp.shard(strategy2)
+            self.floor_div.shard(strategy)
+
+    def construct(self, inputs, label):
+        x = self.mul(inputs, self.mul_weight)
+        x = self.floor_div(x, self.floordiv_weight)
+        x = self.bn(x)
+        return x
+
+
+inputs_ = Tensor(np.random.randn(128, 96).astype(np.float32), dtype=ms.float32)
+label_ = Tensor(np.random.randn(128, 96).astype(np.float32), 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()
+    train_net.set_train()
+    _executor.compile(train_net, inputs_, label_)
+    context.reset_auto_parallel_context()
+
+
+def test_net():
+    context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=4, global_rank=0)
+    strategy = ((4, 1), (4, 1))
+    net = ParallelFloorDivBpropNet(mul_size=(128, 96), test_size=(128, 96), strategy=strategy, strategy2=strategy)
+    compile_net(net)