[Cherry-pick 2.0]Fix isolated var fetch bug

paddle/fluid/framework/details/multi_devices_helper.cc

@@ -210,6 +210,32 @@ std::vector<std::unique_ptr<ir::Graph>> TrySeparateToMultipleSingleDeviceGraphs(
     g->Set(kGraphDepVars, new GraphDepVars());
   }
 
+  std::vector<VarHandle *> isolated_var_handles;
+  for (auto *node : graph->Nodes()) {
+    if (!node->IsWrappedBy<VarHandleBase>()) {
+      continue;
+    }
+
+    auto &var_handle_base = node->Wrapper<VarHandleBase>();
+    auto *var_handle = dynamic_cast<VarHandle *>(&var_handle_base);
+    if (var_handle && var_handle->PendingOps().empty() &&
+        var_handle->GeneratedOp() == nullptr) {
+      isolated_var_handles.emplace_back(var_handle);
+    }
+  }
+
+  for (auto *var_handle : isolated_var_handles) {
+    auto dev_idx = var_handle->scope_idx();
+    auto &src_vars = graph->Get<GraphVars>(kGraphVars)[dev_idx];
+    auto *dst_graph = graphs[dev_idx].get();
+    auto &dst_vars = dst_graph->Get<GraphVars>(kGraphVars)[0];
+    VLOG(10) << "Move isolated var " << var_handle->Name() << " at device "
+             << dev_idx;
+    dst_graph->AddNode(graph->RemoveNode(var_handle->Node()).release());
+    dst_vars[var_handle->Name()].emplace_back(var_handle);
+    src_vars.erase(var_handle->Name());
+  }
+
   for (auto &pair : op_to_dev_idx) {
     auto *op = pair.first;
     auto dev_idx = pair.second;

paddle/fluid/framework/ir/graph.cc

@@ -44,11 +44,14 @@ std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
     all_vars.emplace(var->Name(), var);
   }
 
+  auto not_visited_vars = all_vars;
+
   for (auto *op : program.Block(0).AllOps()) {
     ir::Node *node = CreateOpNode(op);
     // For input args, reuse the same var name if it was created before.
     // Otherwise, create a new one.
     for (auto &each_var_name : op->InputArgumentNames()) {
+      not_visited_vars.erase(each_var_name);
       ir::Node *var = nullptr;
       if (var_nodes.find(each_var_name) != var_nodes.end()) {
         var = var_nodes.at(each_var_name).back();
@@ -68,6 +71,7 @@ std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
     // For output args, always create a new var.
     std::unordered_set<std::string> out_arg_set;
     for (auto &each_var_name : op->OutputArgumentNames()) {
+      not_visited_vars.erase(each_var_name);
       if (each_var_name != kEmptyVarName) {
         PADDLE_ENFORCE_EQ(out_arg_set.count(each_var_name), 0,
                           platform::errors::InvalidArgument(
@@ -91,6 +95,16 @@ std::map<std::string, std::vector<ir::Node *>> Graph::InitFromProgram(
       var->inputs.push_back(node);
     }
   }
+
+  for (auto &pair : not_visited_vars) {
+    const auto &var_name = pair.first;
+    auto *var_desc = pair.second;
+    if (var_name != kEmptyVarName) {
+      VLOG(10) << "Create isolated var node " << var_name;
+      var_nodes[var_name].push_back(CreateVarNode(var_desc));
+    }
+  }
+
   Set<const std::vector<OpDesc *>>(
       details::kStaleProgramOpDescs,
       new std::vector<OpDesc *>(program.Block(0).AllOps()));

paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.cc

@@ -174,9 +174,15 @@ void MultiDevSSAGraphBuilderBase::ApplyImpl(ir::Graph *graph) const {
   auto nodes = graph->ReleaseNodes();
   ir::Graph &result = *graph;
 
+  std::vector<ir::Node *> isolated_vars;
+
   for (auto &node : nodes) {
     if (node->IsVar() && node->Var()) {
       all_vars_.emplace(node->Name(), node->Var());
+
+      if (node->inputs.empty() && node->outputs.empty()) {
+        isolated_vars.emplace_back(node.get());
+      }
     }
   }
 
@@ -185,6 +191,10 @@ void MultiDevSSAGraphBuilderBase::ApplyImpl(ir::Graph *graph) const {
   result.Set(details::kGraphDepVars, new details::GraphDepVars);
   result.Set(kGraphOps, new GraphOps);
 
+  for (auto *var_node : isolated_vars) {
+    CreateIsolatedVarNode(&result, var_node);
+  }
+
   bool is_forwarding = true;
 
   for (ir::Node *node : sorted_ops) {
@@ -582,6 +592,15 @@ bool MultiDevSSAGraphBuilderBase::IsSparseGradient(
   return all_vars_.at(og)->GetType() == proto::VarType::SELECTED_ROWS;
 }
 
+void MultiDevSSAGraphBuilderBase::CreateIsolatedVarNode(
+    ir::Graph *graph, ir::Node *var_node) const {
+  for (size_t i = 0; i < places_.size(); ++i) {
+    VLOG(10) << "Create isolated var node " << var_node->Name() << " at device "
+             << i;
+    CreateOrGetLatestVarHandle(graph, var_node, places_[i], i);
+  }
+}
+
 void AllReduceSSAGraphBuilder::InsertCollectiveOp(
     ir::Graph *result, const std::string &p_name,
     const std::string &g_name) const {

paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h

@@ -94,6 +94,8 @@ class MultiDevSSAGraphBuilderBase : public ir::Pass {
   void CreateOpHandleIOs(ir::Graph *result, ir::Node *node,
                          size_t device_id) const;
 
+  void CreateIsolatedVarNode(ir::Graph *result, ir::Node *var_node) const;
+
 #if defined(PADDLE_WITH_NCCL)
   mutable platform::NCCLContextMap *nccl_ctxs_{nullptr};
   mutable platform::NCCLCommunicator *multi_nccl_ctxs_{nullptr};

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -366,6 +366,7 @@ set_tests_properties(test_parallel_executor_test_while_train test_parallel_execu
         test_data_norm_op test_imperative_using_non_zero_gpu test_fuse_bn_act_pass
         test_optimizer_in_control_flow test_dataloader_keep_order
         test_dataloader_unkeep_order
+        test_parallel_executor_fetch_isolated_var
         test_parallel_executor_inference_feed_partial_data
         test_parallel_ssa_graph_inference_feed_partial_data
         test_fetch_unmerged

python/paddle/fluid/tests/unittests/test_parallel_executor_fetch_isolated_var.py

@@ -0,0 +1,106 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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 unittest
+import numpy as np
+import six
+import paddle.fluid as fluid
+
+
+def enable_parallel_ssa_executor(enabled=True):
+    if fluid.is_compiled_with_cuda():
+        fluid.core.globals()['FLAGS_enable_parallel_graph'] = enabled
+
+
+class TestParallelExecutorFetchIsolatedVarBase(unittest.TestCase):
+    def build_network(self, is_training):
+        x = fluid.data(name='x', shape=[-1, 10], dtype='float32')
+        y = fluid.data(name='y', shape=[-1, 10], dtype='float32')
+        fc = fluid.layers.fc(x, size=30)
+        loss = fluid.layers.reduce_mean(fc)
+        if is_training:
+            adam = fluid.optimizer.Adam(learning_rate=1e-3)
+            adam.minimize(loss)
+
+        return loss, y
+
+    def exec_strategy(self, use_experimental_executor):
+        strategy = fluid.ExecutionStrategy()
+        strategy.use_experimental_executor = use_experimental_executor
+        return strategy
+
+    def places(self, use_gpu, dev_cnt):
+        if use_gpu:
+            return fluid.cuda_places(list(range(dev_cnt)))
+        else:
+            return fluid.cpu_places(dev_cnt)
+
+    def test_main(self):
+        for use_gpu in [False, True]:
+            for dev_cnt in [1, 2]:
+                for is_training in [False, True]:
+                    for use_experimental_executor in [False, True]:
+                        for use_parallel_ssa_executor in [False, True]:
+                            func = lambda: self.run_impl(use_gpu, dev_cnt, is_training, use_experimental_executor, use_parallel_ssa_executor)
+                            self.run_func_with_guard(func)
+
+    def run_impl(self, use_gpu, dev_cnt, is_training, use_experimental_executor,
+                 use_parallel_ssa_executor):
+        enable_parallel_ssa_executor(use_parallel_ssa_executor)
+
+        if fluid.is_compiled_with_cuda():
+            if fluid.core.globals()[
+                    'FLAGS_enable_parallel_graph'] and not use_gpu:
+                return
+        else:
+            if use_gpu:
+                return
+
+        loss, isolated_var = self.build_network(is_training)
+        loss_name = loss.name if is_training else None
+
+        places = self.places(use_gpu, dev_cnt)
+        exe = fluid.Executor(places[0])
+
+        exe.run(fluid.default_startup_program())
+
+        prog = fluid.CompiledProgram(fluid.default_main_program(
+        )).with_data_parallel(
+            loss_name=loss_name,
+            exec_strategy=self.exec_strategy(use_experimental_executor),
+            places=places)
+
+        BATCH_SIZE = 8 * dev_cnt
+        for _ in six.moves.range(10):
+            x_np = np.random.random(size=[BATCH_SIZE, 10]).astype('float32')
+            y_np = np.random.random(size=[BATCH_SIZE, 10]).astype('float32')
+
+            _, y_np_fetch = exe.run(prog,
+                                    feed={'x': x_np,
+                                          'y': y_np},
+                                    fetch_list=[loss, isolated_var])
+
+            self.assertTrue(np.array_equal(y_np, y_np_fetch))
+
+        enable_parallel_ssa_executor(False)
+
+    def run_func_with_guard(self, func):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            with fluid.unique_name.guard():
+                with fluid.scope_guard(fluid.Scope()):
+                    func()
+
+
+if __name__ == '__main__':
+    unittest.main()