Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into async_executor

paddle/fluid/framework/details/all_reduce_op_handle.cc

@@ -34,7 +34,7 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
       nccl_ctxs_(ctxs) {
   if (nccl_ctxs_) {
     for (auto &p : places_) {
-      this->dev_ctxes_[p] = nccl_ctxs_->DevCtx(p);
+      this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p));
     }
   }
 }
@@ -46,7 +46,7 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
 #endif
 
 void AllReduceOpHandle::RunImpl() {
-  platform::RecordEvent record_event(Name(), dev_ctxes_.begin()->second);
+  platform::RecordEvent record_event(Name(), dev_ctxes_.cbegin()->second);
 
   if (NoDummyInputSize() == 1) {
     return;  // No need to all reduce when GPU count = 1;
@@ -127,7 +127,7 @@ void AllReduceOpHandle::RunImpl() {
             *local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
         auto &p = places_[i];
         auto *var = scope.FindVar(out_var_handles[i]->name_);
-        auto *dev_ctx = dev_ctxes_[p];
+        auto *dev_ctx = dev_ctxes_.at(p);
 
         RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
           auto &tensor_gpu = *var->GetMutable<framework::LoDTensor>();

paddle/fluid/framework/details/broadcast_op_handle.h

@@ -44,7 +44,8 @@ struct BroadcastOpHandle : public OpHandleBase {
         nccl_ctxs_(nccl_ctxs) {
     if (nccl_ctxs_) {
       for (auto &p_ctx : nccl_ctxs_->contexts_) {
-        dev_ctxes_[platform::CUDAPlace(p_ctx.first)] = p_ctx.second.ctx_.get();
+        this->SetDeviceContext(platform::CUDAPlace(p_ctx.first),
+                               p_ctx.second.ctx_.get());
       }
     }
   }

paddle/fluid/framework/details/computation_op_handle.cc

@@ -37,7 +37,7 @@ void ComputationOpHandle::RunImpl() {
 bool ComputationOpHandle::NeedWait(VarHandleBase *in_var) {
   bool need_wait =
       in_var && in_var->GeneratedOp() &&
-      in_var->GeneratedOp()->DeviceContext(place_) != dev_ctxes_[place_];
+      in_var->GeneratedOp()->DeviceContext(place_) != dev_ctxes_.at(place_);
   return need_wait;
 }
 

paddle/fluid/framework/details/data_balance_op_handle.cc

@@ -28,7 +28,7 @@ DataBalanceOpHandle::DataBalanceOpHandle(
     : OpHandleBase(node), local_scopes_(local_scopes), places_(places) {
   if (ctxs) {
     for (auto &p : places_) {
-      this->dev_ctxes_[p] = ctxs->DevCtx(p);
+      this->SetDeviceContext(p, ctxs->DevCtx(p));
     }
   }
 }
@@ -89,8 +89,8 @@ void DataBalanceOpHandle::RunImpl() {
   PADDLE_ENFORCE_GT(places_.size(), 1,
                     "Data balance can only be enabled when the number of "
                     "places to run larger than 1.");
-  auto in_var_handles = DynamicCast<VarHandle>(inputs_);
-  auto out_var_handles = DynamicCast<VarHandle>(outputs_);
+  auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
+  auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
   PADDLE_ENFORCE(in_var_handles.size() % places_.size() == 0);
   PADDLE_ENFORCE_EQ(
       in_var_handles.size(), out_var_handles.size(),

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -92,13 +92,13 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
 
   size_t num_complete = 0;
   remaining_ = 0;
-  BlockingQueue<size_t> complete_q;
+  auto complete_q = std::make_shared<BlockingQueue<size_t>>();
   for (auto op : bootstrap_ops_) {
-    RunOpAsync(op_deps.get(), op, &complete_q);
+    RunOpAsync(op_deps.get(), op, complete_q);
   }
 
   while (num_complete != op_deps->size()) {
-    size_t num_comp = complete_q.Pop();
+    size_t num_comp = complete_q->Pop();
     if (num_comp == -1UL) {
       int remaining = 0;
       while (true) {
@@ -107,7 +107,7 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
           break;
         }
         for (int i = 0; i < remaining; ++i) {
-          complete_q.Pop();
+          complete_q->Pop();
         }
       }
       exception_.ReThrow();
@@ -120,7 +120,8 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
 }
 void FastThreadedSSAGraphExecutor::RunOpAsync(
     std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
-    OpHandleBase *op, BlockingQueue<size_t> *complete_q) {
+    OpHandleBase *op,
+    const std::shared_ptr<BlockingQueue<size_t>> &complete_q) {
   ++remaining_;
   this->pool_.enqueue([=] {
     OpHandleBase *op_to_run = op;
@@ -144,7 +145,7 @@ void FastThreadedSSAGraphExecutor::RunOpAsync(
             if (op_to_run == nullptr) {
               op_to_run = pending_op;
             } else {
-              this->RunOpAsync(op_deps, pending_op, complete_q);
+              RunOpAsync(op_deps, pending_op, complete_q);
             }
           }
         }
@@ -156,8 +157,7 @@ void FastThreadedSSAGraphExecutor::RunOpAsync(
 }
 void FastThreadedSSAGraphExecutor::PrepareAtomicOpDeps() {
   atomic_op_deps_ = pool_.enqueue([&] {
-    std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps =
-        new std::unordered_map<OpHandleBase *, std::atomic<int>>;
+    auto *op_deps = new std::unordered_map<OpHandleBase *, std::atomic<int>>;
     for (auto &pair : op_deps_) {
       (*op_deps)[pair.first] = pair.second;
     }

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h

@@ -50,7 +50,8 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
   std::atomic<int> remaining_;
 
   void RunOpAsync(std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
-                  OpHandleBase *op, BlockingQueue<size_t> *complete_q);
+                  OpHandleBase *op,
+                  const std::shared_ptr<BlockingQueue<size_t>> &complete_q);
 
   void PrepareAtomicOpDeps();
 

paddle/fluid/framework/details/gather_op_handle.cc

@@ -36,7 +36,7 @@ void GatherOpHandle::RunImpl() {
 
   VarHandle *out_var_handle;
   {
-    auto out_var_handles = DynamicCast<VarHandle>(outputs_);
+    auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
     PADDLE_ENFORCE_EQ(out_var_handles.size(), 1,
                       "The number of output should be one.");
     out_var_handle = out_var_handles.front();
@@ -99,7 +99,7 @@ void GatherOpHandle::RunImpl() {
   Tensor *out_tensor = out_value->mutable_value();
 
   // copy
-  auto dev_ctx = dev_ctxes_[out_var_handle->place_];
+  auto dev_ctx = dev_ctxes_.at(out_var_handle->place_);
   RunAndRecordEvent(out_var_handle->place_, [in_tensors, out_tensor, &dev_ctx,
                                              t_out_p] {
     int s = 0, e = 0;

paddle/fluid/framework/details/op_handle_base.cc

@@ -103,7 +103,7 @@ void OpHandleBase::WaitInputVarGenerated() {
 void OpHandleBase::WaitInputVarGenerated(const platform::Place &place) {
   for (auto *in : inputs_) {
     if (NeedWait(in)) {
-      in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_[place]);
+      in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_.at(place));
     }
   }
 }

paddle/fluid/framework/details/reduce_op_handle.cc

@@ -27,7 +27,7 @@ namespace framework {
 namespace details {
 
 void ReduceOpHandle::RunImpl() {
-  platform::RecordEvent record_event(Name(), dev_ctxes_.begin()->second);
+  platform::RecordEvent record_event(Name(), dev_ctxes_.cbegin()->second);
 
   if (places_.size() == 1) return;
   // the input and output may have dummy var.

paddle/fluid/framework/details/reduce_op_handle.h

@@ -46,7 +46,8 @@ struct ReduceOpHandle : public OpHandleBase {
         nccl_ctxs_(nccl_ctxs) {
     if (nccl_ctxs_) {
       for (auto &p_ctx : nccl_ctxs_->contexts_) {
-        dev_ctxes_[platform::CUDAPlace(p_ctx.first)] = p_ctx.second.ctx_.get();
+        this->SetDeviceContext(platform::CUDAPlace(p_ctx.first),
+                               p_ctx.second.ctx_.get());
       }
     }
   }

paddle/fluid/framework/details/rpc_op_handle.cc

@@ -38,7 +38,7 @@ void RPCOpHandle::RunImpl() {
       continue;
     }
     if (in->GeneratedOp()) {
-      in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_[p]);
+      in->GeneratedOp()->RecordWaitEventOnCtx(dev_ctxes_.at(p));
     }
   }
   auto &tmp_scope = local_scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();

paddle/fluid/framework/details/scale_loss_grad_op_handle.cc

@@ -27,7 +27,7 @@ ScaleLossGradOpHandle::ScaleLossGradOpHandle(ir::Node *node, size_t num_dev,
       coeff_(static_cast<float>(1.0 / num_dev)),
       scope_(scope),
       place_(place) {
-  dev_ctxes_[place_] = dev_ctx;
+  this->SetDeviceContext(place_, dev_ctx);
 }
 
 ScaleLossGradOpHandle::~ScaleLossGradOpHandle() {}
@@ -46,8 +46,8 @@ void ScaleLossGradOpHandle::RunImpl() {
   } else {
 #ifdef PADDLE_WITH_CUDA
     this->RunAndRecordEvent([&] {
-      auto stream =
-          static_cast<platform::CUDADeviceContext *>(this->dev_ctxes_[place_])
+      auto stream = static_cast<platform::CUDADeviceContext *>(
+                        this->dev_ctxes_.at(place_))
                         ->stream();
       memory::Copy(boost::get<platform::CUDAPlace>(place_), tmp,
                    platform::CPUPlace(), &coeff_, sizeof(float), stream);

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -39,7 +39,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
       new platform::RecordEvent("ThreadedSSAGraphExecutorPrepare", nullptr));
   std::unordered_map<OpHandleBase *, size_t> pending_ops;
   std::unordered_set<VarHandleBase *> pending_vars;
-  BlockingQueue<VarHandleBase *> ready_vars;
+  auto ready_vars = std::make_shared<BlockingQueue<VarHandleBase *>>();
   std::unordered_set<OpHandleBase *> ready_ops;
   // For ops (e.g. nccl_all_reduce) that need to coordinate multiple
   // streams from multiple GPUs, it's faster to buffer them and schedule
@@ -51,12 +51,12 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
   for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
     for (auto &name_pair : var_map) {
       for (auto &version_pair : name_pair.second) {
-        InsertPendingVar(&pending_vars, &ready_vars, version_pair.get());
+        InsertPendingVar(&pending_vars, ready_vars.get(), version_pair.get());
       }
     }
   }
   for (auto &var : graph_->Get<details::GraphDepVars>(details::kGraphDepVars)) {
-    InsertPendingVar(&pending_vars, &ready_vars, var.get());
+    InsertPendingVar(&pending_vars, ready_vars.get(), var.get());
   }
 
   for (auto &op : graph_->Get<details::GraphOps>(details::kGraphOps)) {
@@ -73,12 +73,12 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
   FeedFetchList fetch_data(fetch_tensors.size());
 
   InsertFetchOps(fetch_tensors, &fetch_ops, &fetch_dependencies, &pending_ops,
-                 &pending_vars, &ready_vars, &fetch_data);
+                 &pending_vars, ready_vars.get(), &fetch_data);
 
   auto run_all_ops = [&](std::unordered_set<OpHandleBase *> &set) {
     for (auto *op : set) {
       running_ops_++;
-      RunOp(&ready_vars, op);
+      RunOp(ready_vars, op);
     }
     set.clear();
   };
@@ -87,7 +87,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
   run_op_futures_.clear();
   exception_holder_.Clear();
   event.reset(nullptr);
-
   // Step 3. Execution
   while (!pending_vars.empty()) {
     // 1. Run All Ready ops
@@ -103,7 +102,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
 
     // 2. Find ready variable
     bool timeout;
-    auto cur_ready_vars = ready_vars.PopAll(1, &timeout);
+    auto cur_ready_vars = ready_vars->PopAll(1, &timeout);
 
     if (timeout) {
       if (exception_holder_.IsCaught()) {
@@ -133,7 +132,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     }
   }
   PADDLE_ENFORCE(ready_ops.empty());
-
   // Wait FetchOps.
   ClearFetchOp(graph_.get(), &fetch_ops);
 
@@ -206,7 +204,8 @@ void ThreadedSSAGraphExecutor::InsertPendingVar(
 }
 
 void ThreadedSSAGraphExecutor::RunOp(
-    BlockingQueue<VarHandleBase *> *ready_var_q, details::OpHandleBase *op) {
+    const std::shared_ptr<BlockingQueue<VarHandleBase *>> &ready_var_q,
+    details::OpHandleBase *op) {
   auto op_run = [ready_var_q, op, this] {
     try {
       if (VLOG_IS_ON(10)) {

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -51,7 +51,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
   ~ThreadedSSAGraphExecutor() {}
 
  private:
-  void RunOp(BlockingQueue<VarHandleBase *> *ready_var_q,
+  void RunOp(const std::shared_ptr<BlockingQueue<VarHandleBase *>> &ready_var_q,
              details::OpHandleBase *op);
 
  private:

paddle/fluid/framework/lod_tensor_array.h

@@ -19,81 +19,7 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
-// NOTE The vector<LoDTensor> can't be replaced with the class LoDTensorArray
-// directly, because there are many vector<LoDTensor> used accross the project,
-// and some of them are treated as LoDTensorArray.
-#if !defined(PADDLE_ON_INFERENCE)
-
 using LoDTensorArray = std::vector<LoDTensor>;
 
-#else  // !PADDLE_ON_INFERENCE
-
-#pragma message "LoDTensorArray is replaced with the inference one."
-/*
- * A LoDTensorArray which will not deallocate buffer when resized, fix the data
- * diff in inference, and more performance friendly in the concurrency
- * scenerios.
- */
-class LoDTensorArray {
- public:
-  LoDTensorArray() = default;
-
-  using iterator = std::vector<LoDTensor>::iterator;
-  using const_iterator = std::vector<LoDTensor>::const_iterator;
-
-  const_iterator begin() const { return array_.begin(); }
-  const_iterator end() const { return array_.begin() + size_; }
-  iterator begin() { return array_.begin(); }
-  iterator end() { return array_.begin() + size_; }
-
-  void push_back(const LoDTensor& x) {
-    if (size_ < array_.size()) {
-      array_[size_++] = x;
-    } else {
-      array_.push_back(x);
-      ++size_;
-    }
-  }
-  void resize(size_t size) {
-    if (array_.size() < size) {
-      array_.resize(size);
-    }
-    size_ = size;
-  }
-
-  void emplace_back() { array_.emplace_back(); }
-
-  void emplace_back(LoDTensor&& x) { array_.emplace_back(std::move(x)); }
-
-  LoDTensor& back() { return array_.back(); }
-
-  size_t space() const { return array_.size(); }
-
-  void reserve(size_t size) {
-    // Naive warning to tell user this array might be to large. The memory and
-    // buffer used by this TensorArray will not be deleted during the training
-    // and inference phase, so attention not to make it expand too long.
-    if (size > 800UL) {
-      LOG(WARNING) << "TensorArray has more than 800 items";
-    }
-    array_.reserve(size);
-  }
-
-  bool empty() const { return size_ == 0UL; }
-  void clear() { size_ = 0UL; }
-
-  LoDTensor& operator[](size_t id) { return array_[id]; }
-  const LoDTensor& operator[](size_t id) const { return array_[id]; }
-  LoDTensor& at(size_t id) { return array_.at(id); }
-  const LoDTensor& at(size_t id) const { return array_.at(id); }
-
-  size_t size() const { return size_; }
-
- private:
-  size_t size_{0};
-  std::vector<LoDTensor> array_;
-};
-#endif  // !PADDLE_ON_INFERENCE
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/inference/api/demo_ci/simple_on_word2vec.cc

@@ -70,8 +70,12 @@ void Main(bool use_gpu) {
     // The outputs' buffers are in CPU memory.
     for (size_t i = 0; i < std::min(static_cast<size_t>(5), num_elements);
          i++) {
-      CHECK_NEAR(static_cast<float*>(outputs.front().data.data())[i], result[i],
-                 0.001);
+      // Here will result random fail, for that the model is trained by CI, the
+      // train phase is not stable, so the result will be random.
+      // TODO(Superjomn) will restore after the model is upload.
+      // CHECK_NEAR(static_cast<float*>(outputs.front().data.data())[i],
+      // result[i],
+      // 0.001);
     }
   }
 }

paddle/fluid/operators/gather_op.cc

@@ -102,7 +102,9 @@ REGISTER_OPERATOR(gather, ops::GatherOp, ops::GatherOpMaker,
                   paddle::framework::DefaultGradOpDescMaker<true>);
 REGISTER_OPERATOR(gather_grad, ops::GatherGradOp);
 REGISTER_OP_CPU_KERNEL(gather, ops::GatherOpKernel<float>,
-                       ops::GatherOpKernel<int>, ops::GatherOpKernel<double>);
+                       ops::GatherOpKernel<double>, ops::GatherOpKernel<int>,
+                       ops::GatherOpKernel<int64_t>);
 REGISTER_OP_CPU_KERNEL(gather_grad, ops::GatherGradientOpKernel<float>,
+                       ops::GatherGradientOpKernel<double>,
                        ops::GatherGradientOpKernel<int>,
-                       ops::GatherGradientOpKernel<double>);
+                       ops::GatherGradientOpKernel<int64_t>);

paddle/fluid/operators/gather_op.cu

@@ -61,5 +61,11 @@ class GatherGradOpCUDAKernel : public framework::OpKernel<T> {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(gather, ops::GatherOpCUDAKernel<float>);
-REGISTER_OP_CUDA_KERNEL(gather_grad, ops::GatherGradOpCUDAKernel<float>);
+REGISTER_OP_CUDA_KERNEL(gather, ops::GatherOpCUDAKernel<float>,
+                        ops::GatherOpCUDAKernel<double>,
+                        ops::GatherOpCUDAKernel<int64_t>,
+                        ops::GatherOpCUDAKernel<int>);
+REGISTER_OP_CUDA_KERNEL(gather_grad, ops::GatherGradOpCUDAKernel<float>,
+                        ops::GatherGradOpCUDAKernel<double>,
+                        ops::GatherGradOpCUDAKernel<int64_t>,
+                        ops::GatherGradOpCUDAKernel<int>);

python/paddle/fluid/layers/nn.py

@@ -749,7 +749,7 @@ def dynamic_gru(input,
         attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
     batch_size = input.shape[0]
     inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
-    if h_0 != None:
+    if h_0:
         assert h_0.shape == (
             batch_size, size
         ), 'The shape of h0 should be(batch_size, %d)' % size
@@ -3020,7 +3020,8 @@ def sequence_pad(x, pad_value, maxlen=None, name=None):
 
             x = fluid.layers.data(name='y', shape=[10, 5],
                              dtype='float32', lod_level=1)
-            pad_value = fluid.layers.assign(input=numpy.array([0]))
+            pad_value = fluid.layers.assign(
+                input=numpy.array([0], dtype=numpy.float32))
             out = fluid.layers.sequence_pad(x=x, pad_value=pad_value)
     """
 

python/paddle/fluid/tests/book/high-level-api/image_classification/CMakeLists.txt

 file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
 string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
 
-# default test
-foreach(src ${TEST_OPS})
+if(NOT APPLE)
+    # default test
+    foreach(src ${TEST_OPS})
         py_test(${src} SRCS ${src}.py)
-endforeach()
+    endforeach()
+else()
+    foreach(src ${TEST_OPS})
+        if(${src} STREQUAL "test_image_classification_vgg")
+            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
+        elseif(${src} STREQUAL "test_image_classification_resnet")
+            message(WARNING "These tests has been disabled in OSX for random fail: \n" ${src})
+        elseif()
+            py_test(${src} SRCS ${src}.py)
+        endif()
+    endforeach()
+endif()

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

@@ -17,6 +17,10 @@ if(NOT WITH_DISTRIBUTE)
     list(REMOVE_ITEM TEST_OPS test_listen_and_serv_op)
     LIST(REMOVE_ITEM TEST_OPS test_dist_mnist)
     LIST(REMOVE_ITEM TEST_OPS test_dist_word2vec)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_ctr)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_simnet_bow)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_mnist_batch_merge)
+    LIST(REMOVE_ITEM TEST_OPS test_dist_text_classification)
 endif(NOT WITH_DISTRIBUTE)
 
 list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
@@ -89,4 +93,6 @@ py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SE
 py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
 set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 150)
 py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
-py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
+if(NOT APPLE)
+    py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
+endif()

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

@@ -90,8 +90,10 @@ class TestDistMnist2x2(TestDistRunnerBase):
 
         inference_program = fluid.default_main_program().clone()
         # Optimization
-        opt = fluid.optimizer.AdamOptimizer(
-            learning_rate=0.001, beta1=0.9, beta2=0.999)
+        # TODO(typhoonzero): fix distributed adam optimizer
+        # opt = fluid.optimizer.AdamOptimizer(
+        #     learning_rate=0.001, beta1=0.9, beta2=0.999)
+        opt = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
 
         # Reader
         train_reader = paddle.batch(

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

@@ -22,6 +22,8 @@ import signal
 import subprocess
 import six
 import argparse
+import pickle
+import numpy as np
 
 import paddle.fluid as fluid
 
@@ -128,10 +130,15 @@ class TestDistRunnerBase(object):
             else:
                 return origin_batch
 
+        out_losses = []
         for _ in six.moves.xrange(RUN_STEP):
             loss, = exe.run(fetch_list=[avg_cost.name],
                             feed=feeder.feed(get_data()))
-            print(loss)
+            out_losses.append(loss[0])
+        if six.PY2:
+            print(pickle.dumps(out_losses))
+        else:
+            sys.stdout.buffer.write(pickle.dumps(out_losses))
 
 
 def runtime_main(test_class):
@@ -149,7 +156,7 @@ def runtime_main(test_class):
     parser.add_argument('--use_cuda', action='store_true')
     parser.add_argument('--use_reduce', action='store_true')
     parser.add_argument(
-        '--use_reader_alloc', action='store_true', required=False, default=True)
+        '--use_reader_alloc', action='store_true', required=False)
     parser.add_argument('--batch_size', required=False, type=int, default=2)
     parser.add_argument(
         '--batch_merge_repeat', required=False, type=int, default=1)
@@ -188,7 +195,7 @@ class TestDistBase(unittest.TestCase):
         self._pservers = 2
         self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
             self._find_free_port(), self._find_free_port())
-        self._python_interp = "python"
+        self._python_interp = sys.executable
         self._sync_mode = True
         self._enforce_place = None
         self._mem_opt = False
@@ -237,21 +244,6 @@ class TestDistBase(unittest.TestCase):
 
         return ps0_proc, ps1_proc, ps0_pipe, ps1_pipe
 
-    def _wait_ps_ready(self, pid):
-        retry_times = 50
-        while True:
-            assert retry_times >= 0, "wait ps ready failed"
-            time.sleep(3)
-            try:
-                # the listen_and_serv_op would touch a file which contains the listen port
-                # on the /tmp directory until it was ready to process all the RPC call.
-                os.stat("/tmp/paddle.%d.port" % pid)
-                return
-            except os.error as e:
-                sys.stderr.write('waiting for pserver: %s, left retry %d\n' %
-                                 (e, retry_times))
-                retry_times -= 1
-
     def _run_local(self,
                    model,
                    envs,
@@ -288,23 +280,20 @@ class TestDistBase(unittest.TestCase):
                 env=envs)
 
         local_out, local_err = local_proc.communicate()
-        local_ret = cpt.to_text(local_out)
 
         if check_error_log:
             err_log.close()
 
-        sys.stderr.write('local_stdout: %s\n' % local_ret)
+        sys.stderr.write('local_stdout: %s\n' % pickle.loads(local_out))
         sys.stderr.write('local_stderr: %s\n' % local_err)
 
-        local_losses = local_ret.split("\n")
-        return local_losses
+        return pickle.loads(local_out)
 
     def _run_cluster(self, model, envs, check_error_log):
         # Run dist train to compare with local results
         ps0, ps1, ps0_pipe, ps1_pipe = self.start_pserver(model,
                                                           check_error_log, envs)
-        self._wait_ps_ready(ps0.pid)
-        self._wait_ps_ready(ps1.pid)
+
         ps0_ep, ps1_ep = self._ps_endpoints.split(",")
 
         tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --trainers %d --is_dist"
@@ -339,8 +328,8 @@ class TestDistBase(unittest.TestCase):
         env0.update(envs)
         env1.update(envs)
 
-        print("tr0_cmd:{}, env0: {}".format(tr0_cmd, env0))
-        print("tr1_cmd:{}, env1: {}".format(tr1_cmd, env1))
+        print("tr0_cmd:{}".format(tr0_cmd))
+        print("tr1_cmd:{}".format(tr1_cmd))
         tr0_pipe = open("/tmp/tr0_err.log", "wb")
         tr1_pipe = open("/tmp/tr1_err.log", "wb")
 
@@ -356,9 +345,7 @@ class TestDistBase(unittest.TestCase):
             env=env1)
 
         tr0_out, tr0_err = tr0_proc.communicate()
-        tr0_loss_text = cpt.to_text(tr0_out)
         tr1_out, tr1_err = tr1_proc.communicate()
-        tr1_loss_text = cpt.to_text(tr1_out)
 
         # close trainer file
         tr0_pipe.close()
@@ -373,15 +360,13 @@ class TestDistBase(unittest.TestCase):
         ps1.terminate()
 
         # print log
-        sys.stderr.write('trainer 0 stdout:\n %s\n' % tr0_loss_text)
-        sys.stderr.write('trainer 0 stderr:\n %s\n' % tr0_err)
-        sys.stderr.write('trainer 1 stdout: %s\n' % tr1_loss_text)
+        sys.stderr.write('trainer 0 stdout: %s\n' % pickle.loads(tr0_out))
+        sys.stderr.write('trainer 0 stderr: %s\n' % tr0_err)
+        sys.stderr.write('trainer 1 stdout: %s\n' % pickle.loads(tr1_out))
         sys.stderr.write('trainer 1 stderr: %s\n' % tr1_err)
 
-        tr0_losses = tr0_loss_text.split("\n")
-        tr1_losses = tr1_loss_text.split("\n")
-
-        return tr0_losses, tr1_losses
+        # return tr0_losses, tr1_losses
+        return pickle.loads(tr0_out), pickle.loads(tr1_out)
 
     def check_with_place(self,
                          model_file,
@@ -411,9 +396,9 @@ class TestDistBase(unittest.TestCase):
                                                    check_error_log)
 
         for step_id in range(RUN_STEP):
-            local_loss = eval(local_losses[step_id])[0]
-            tr0_loss = eval(tr0_losses[step_id])[0]
-            tr1_loss = eval(tr1_losses[step_id])[0]
-            dist_loss = (tr0_loss + tr1_loss) / 2
-            print(str(local_loss) + ":" + str(dist_loss))
-            self.assertAlmostEqual(local_loss, dist_loss, delta=delta)
+            local_loss = local_losses[step_id]
+            tr0_loss = tr0_losses[step_id]
+            tr1_loss = tr1_losses[step_id]
+            dist_loss = (np.array([tr0_loss]) + np.array([tr1_loss])) / 2
+            print("=======", local_loss, ":", dist_loss[0], "=======")
+            self.assertAlmostEqual(local_loss, dist_loss[0], delta=delta)

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

@@ -23,16 +23,17 @@ class TestDistSeResneXt2x2(TestDistBase):
         self._use_reader_alloc = False
 
     def test_dist_train(self):
-        self.check_with_place("dist_se_resnext.py", delta=100)
+        self.check_with_place("dist_se_resnext.py", delta=1e-7)
 
 
 class TestDistseResnXt2x2WithMemopt(TestDistBase):
     def _setup_config(self):
         self._sync_mode = True
         self._mem_opt = True
+        self._use_reader_alloc = False
 
     def test_dist_train(self):
-        self.check_with_place("dist_se_resnext.py", delta=100)
+        self.check_with_place("dist_se_resnext.py", delta=1e-7)
 
 
 class TestDistSeResneXt2x2Async(TestDistBase):