Enable ngraph through build_strategy

paddle/fluid/framework/details/CMakeLists.txt

@@ -87,11 +87,18 @@ cc_library(fast_threaded_ssa_graph_executor SRCS fast_threaded_ssa_graph_executo
         DEPS fetch_op_handle ssa_graph_executor scope simple_threadpool device_context)
 cc_test(fused_broadcast_op_test SRCS fused_broadcast_op_handle_test.cc DEPS fused_broadcast_op_handle)
 
+if(WITH_NGRAPH) 
+  set(NGRAPH_BS_DEPS ngraph)
+else()
+  set(NGRAPH_BS_DEPS)
+endif()
+
 cc_library(build_strategy SRCS build_strategy.cc DEPS
         graph_viz_pass multi_devices_graph_pass
         multi_devices_graph_print_pass multi_devices_graph_check_pass
         fuse_elewise_add_act_pass multi_batch_merge_pass 
         fuse_relu_depthwise_conv_pass
         lock_free_optimize_pass
         coalesce_grad_tensor_pass fuse_all_reduce_op_pass backward_optimizer_op_deps_pass
-        fuse_adam_op_pass fuse_sgd_op_pass fuse_momentum_op_pass)
+        fuse_adam_op_pass fuse_sgd_op_pass fuse_momentum_op_pass
+        ${NGRAPH_BS_DEPS})

paddle/fluid/framework/details/build_strategy.cc

@@ -27,6 +27,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h"
 
 DECLARE_bool(use_mkldnn);
+DECLARE_bool(use_ngraph);
 
 namespace paddle {
 namespace framework {
@@ -53,6 +54,8 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
                         "sequential_execution_pass");
     AppendPassWithCheck(strategy_.sync_batch_norm_, "sync_batch_norm_pass");
 
+    AppendPassToUseNgraph("ngraph_subgraph_pass");
+
     AppendOpFusePasses();
     AppendPrintGraphPass("graph_viz_pass", "_fused_graph");
 
@@ -220,6 +223,22 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
 #endif
   }
 
+  void AppendPassToUseNgraph(const std::string &pass_name) {
+#ifdef PADDLE_WITH_NGRAPH
+    if (FLAGS_use_ngraph) {
+      if (strategy_.reduce_ != BuildStrategy::ReduceStrategy::kAllReduce) {
+        LOG(WARNING) << "Currently ngraph_subgraph_pass works under AllReduce,"
+                        "please set FLAGS_use_ngraph=false.";
+      } else {
+        AppendPass(pass_name);
+      }
+    }
+#else
+    PADDLE_ENFORCE_NE(FLAGS_use_ngraph, true,
+                      "Please compile with NGRAPH first to use NGRAPH");
+#endif
+  }
+
  private:
   BuildStrategy strategy_;
 };
@@ -360,3 +379,6 @@ USE_PASS(runtime_context_cache_pass);
 #ifdef PADDLE_WITH_MKLDNN
 USE_PASS(mkldnn_placement_pass);
 #endif
+#ifdef PADDLE_WITH_NGRAPH
+USE_PASS(ngraph_subgraph_pass);
+#endif

paddle/fluid/framework/executor.cc

@@ -39,11 +39,11 @@ limitations under the License. */
 
 #ifdef PADDLE_WITH_NGRAPH
 #include "paddle/fluid/operators/ngraph/ngraph_engine.h"
-DEFINE_bool(use_ngraph, false, "Use NGRAPH to run");
 #endif
 
 DECLARE_bool(benchmark);
 DEFINE_bool(use_mkldnn, false, "Use MKLDNN to run");
+DEFINE_bool(use_ngraph, false, "Use NGRAPH to run");
 
 namespace paddle {
 namespace framework {

paddle/fluid/framework/ir/ngraph_subgraph_pass.cc

@@ -47,8 +47,8 @@ std::string GenerateEngineKey(const std::set<std::string> &engine_inputs,
   return engine_key;
 }
 
-void NgraphSubgraphPass::ApplyImpl(ir::Graph *graph) const {
-  PADDLE_ENFORCE(graph);
+void NgraphSubgraphPass::ApplyImpl(Graph *graph) const {
+  PADDLE_ENFORCE_NOT_NULL(graph);
   FusePassBase::Init("ngraph_subgraph_pass", graph);
 
   std::unordered_set<Node *> nodes2delete;
@@ -66,15 +66,13 @@ void NgraphSubgraphPass::ApplyImpl(ir::Graph *graph) const {
     if (node->IsOp() && !ANAT::Agent(node).subgraph()->empty()) {
       OpDesc *op_desc = node->Op();
       op_desc->SetType("ngraph_engine");
-      for (auto it = ANAT::Agent(node).subgraph()->begin();
-           it != ANAT::Agent(node).subgraph()->end(); ++it) {
-      }
 
       CreateNgraphEngineOp(node, graph);
 
       std::unordered_set<const Node *> nodes2remove(
           ANAT::Agent(node).subgraph()->begin(),
           ANAT::Agent(node).subgraph()->end());
+
       GraphSafeRemoveNodes(graph, nodes2remove);
     }
   }
@@ -85,70 +83,100 @@ void NgraphSubgraphPass::ApplyImpl(ir::Graph *graph) const {
       nodes2remove.insert(node);
     }
   }
+
   framework::ir::GraphSafeRemoveNodes(graph, nodes2remove);
-  std::vector<ir::Node *> nodes = ir::TopologySortOperations(*graph);
+  // std::vector<ir::Node *> nodes = ir::TopologySortOperations(*graph);
 }
 
-void NgraphSubgraphPass::CreateNgraphEngineOp(framework::ir::Node *node,
-                                              Graph *graph) const {
-  auto *op_desc = node->Op();
+bool IsValid(std::string name) {
+  return name.find(Node::kControlDepVarName) == std::string::npos;
+}
+
+void UpdateNgraphIO(Node *node, Graph *graph,
+                    std::vector<std::string> *input_names,
+                    std::vector<std::string> *output_names) {
+  bool is_test = true, has_fetch = false;
+  for (Node *node : graph->Nodes()) {
+    if (node->IsOp() && node->Name().find("_grad") != std::string::npos) {
+      is_test = false;
+    }
+    if (node->IsVar() && node->Var()) {
+      for (auto out : node->outputs) {
+        if (out->Name() == "fetch") has_fetch = true;
+      }
+    }
+  }
+  if (is_test && has_fetch) {
+    for (auto *x : node->inputs) {
+      (*input_names).emplace_back(x->Name());
+    }
+    for (auto *x : node->outputs) {
+      (*output_names).emplace_back(x->Name());
+    }
+    return;
+  }
+
   auto &subgraph = *ANAT::Agent(node).subgraph();
-  PADDLE_ENFORCE(!subgraph.empty());
+  std::unordered_set<std::string> inputs;
+  std::unordered_set<std::string> outputs;
+  for (auto *node : subgraph) {
+    for (auto in : node->inputs) {
+      auto name = in->Name();
+      if (!IsValid(name)) continue;
+      if (!outputs.count(name) && !inputs.count(name)) {
+        (*input_names).emplace_back(name);
+        inputs.insert(name);
+      }
+    }
+    for (auto out : node->outputs) {
+      auto name = out->Name();
+      if (!IsValid(name)) continue;
+      outputs.insert(name);
+      (*output_names).emplace_back(name);
+    }
+  }
+}
 
-  framework::ProgramDesc *program_desc =
-      Get<framework::ProgramDesc *>("program");
-  const framework::BlockDesc &main_block =
-      program_desc->Block(framework::kRootBlockIndex);
-  framework::BlockDesc *new_block = program_desc->AppendBlock(main_block);
+void NgraphSubgraphPass::CreateNgraphEngineOp(Node *node, Graph *graph) const {
+  auto &subgraph = *ANAT::Agent(node).subgraph();
+  PADDLE_ENFORCE_NE(subgraph.empty(), true, "subgraph cannot be empty");
 
   framework::proto::BlockDesc block_proto;
   framework::BlockDesc block_desc(nullptr, &block_proto);
   block_desc.Proto()->set_parent_idx(-1);
   block_desc.Proto()->set_idx(0);
   for (auto *node : subgraph) {
-    auto *new_block_op = new_block->AppendOp();
     auto *op = block_desc.AppendOp();
-    *new_block_op->Proto() = *node->Op()->Proto();
     *op->Proto() = *node->Op()->Proto();
   }
-
-  std::set<std::string> input_names;
-  std::set<std::string> input_names_with_id;
-  for (auto *x : node->inputs) {
-    input_names.insert(x->Name());
-    input_names_with_id.insert(x->Name() + std::to_string(x->id()));
-  }
-  op_desc->SetInput(
-      "Xs", std::vector<std::string>(input_names.begin(), input_names.end()));
-
-  std::set<std::string> output_names;
-  std::set<std::string> output_names_with_id;
-
-  for (auto *x : node->outputs) {
-    output_names.insert(x->Name());
-    output_names_with_id.insert(x->Name() + std::to_string(x->id()));
-  }
-  op_desc->SetOutput(
-      "Ys", std::vector<std::string>(output_names.begin(), output_names.end()));
   auto *vars = block_desc.Proto()->mutable_vars();
-  for (framework::ir::Node *node : graph->Nodes()) {
+  for (Node *node : graph->Nodes()) {
     if (node->IsVar() && node->Var()) {
       *vars->Add() = *node->Var()->Proto();
     }
   }
+  PADDLE_ENFORCE_NE(block_desc.Proto()->vars().empty(), true,
+                    "the block has no var-desc");
 
-  PADDLE_ENFORCE(!block_desc.Proto()->vars().empty(),
-                 "the block has no var-desc");
-
-  op_desc->SetType("ngraph_engine");
+  std::vector<std::string> input_names;
+  std::vector<std::string> output_names;
+  UpdateNgraphIO(node, graph, &input_names, &output_names);
+  auto *op_desc = node->Op();
+  op_desc->SetInput(
+      "Xs", std::vector<std::string>(input_names.begin(), input_names.end()));
+  op_desc->SetOutput(
+      "Ys", std::vector<std::string>(output_names.begin(), output_names.end()));
 
   int sgs = subgraph.size();
-  std::string engine_key = GenerateEngineKey(
-      input_names_with_id, output_names_with_id, std::to_string(sgs));
+  std::string subgraph_str = block_desc.Proto()->SerializeAsString();
+  std::string engine_key =
+      std::to_string(std::hash<std::string>()(subgraph_str));
   std::vector<int> interval{0, sgs};
+  op_desc->SetType("ngraph_engine");
   op_desc->SetAttr("interval", interval);
-  op_desc->SetAttr("graph", block_desc.Proto()->SerializeAsString());
+  op_desc->SetAttr("graph", subgraph_str);
   op_desc->SetAttr("engine_key", engine_key);
+  op_desc->SetAttr("op_role", 0);
 }
 
 }  // namespace ir

paddle/fluid/inference/analysis/ir_passes/subgraph_detector.cc

@@ -21,6 +21,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/ir/graph_pattern_detector.h"
 #include "paddle/fluid/framework/ir/node.h"
 
+DECLARE_bool(use_ngraph);
+
 namespace paddle {
 namespace inference {
 namespace analysis {
@@ -398,6 +400,11 @@ void RemoveIntermediateOutputInSubgraph(const std::vector<Node *> &subgraph,
     }
   }
 
+  // In use for ngraph subgraph pass for parallel executor,
+  // this will remove all nodes, bypass this and let ngraph
+  // subgraph pass to process outputs
+  if (FLAGS_use_ngraph && valid_output.size() == 0) return;
+
   outputs->assign(valid_output.begin(), valid_output.end());
 }
 

python/paddle/fluid/tests/unittests/ngraph/test_parallel_executor_ngraph.py

@@ -0,0 +1,87 @@
+# Copyright (c) 2019 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.
+
+from __future__ import print_function
+
+from paddle.fluid.tests.unittests.simple_nets import simple_fc_net
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid import compiler
+import numpy as np
+import unittest
+import os
+import sys
+import math
+
+
+class TestPallelExecutorNgraph(unittest.TestCase):
+    def check_network_convergence(self, build_strategy=None):
+        os.environ['CPU_NUM'] = str(2)
+        main = fluid.Program()
+        startup = fluid.Program()
+        with fluid.program_guard(main, startup):
+            loss = simple_fc_net()
+            test_program = main.clone(for_test=True)
+
+            opt = fluid.optimizer.Adam(learning_rate=0.001)
+            opt.minimize(loss)
+
+            batch_size = 32
+            image = np.random.normal(size=(batch_size, 784)).astype('float32')
+            label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
+
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            exe.run(startup)
+            feed_dict = {'image': image, 'label': label}
+
+            train_cp = compiler.CompiledProgram(main).with_data_parallel(
+                loss_name=loss.name, build_strategy=build_strategy)
+            test_cp = compiler.CompiledProgram(test_program).with_data_parallel(
+                loss_name=loss.name,
+                build_strategy=build_strategy,
+                share_vars_from=train_cp)
+
+            for i in range(5):
+                _ = exe.run(train_cp, fetch_list=[loss.name], feed=feed_dict)
+                test_loss, = exe.run(test_cp,
+                                     fetch_list=[loss.name],
+                                     feed=feed_dict)
+                train_loss = exe.run(train_cp,
+                                     fetch_list=[loss.name],
+                                     feed=feed_dict)
+
+                avg_test_loss_val = np.array(test_loss).mean()
+                if math.isnan(float(avg_test_loss_val)):
+                    sys.exit("got NaN loss, testing failed.")
+
+                avg_train_loss_val = np.array(train_loss).mean()
+                if math.isnan(float(avg_train_loss_val)):
+                    sys.exit("got NaN loss, training failed.")
+
+                self.assertTrue(
+                    np.allclose(
+                        train_loss, test_loss, atol=1e-8),
+                    "Train loss: " + str(train_loss) + "\n Test loss:" +
+                    str(test_loss))
+
+    def test_parallel_testing(self):
+        build_strategy = fluid.BuildStrategy()
+        build_strategy.enable_inplace = False
+        build_strategy.memory_optimize = False
+        self.check_network_convergence(build_strategy=build_strategy)
+
+
+if __name__ == '__main__':
+    unittest.main()