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

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
+#include <stdexcept>
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/executor.h"
@@ -53,8 +54,14 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
       }
     }
   }
+  std::vector<framework::LoDTensor> fetch_data;
+  std::exception_ptr eptr;
+  try {
+    fetch_data = underlying_executor_->Run(fetch_tensors);
+  } catch (...) {
+    eptr = std::current_exception();
+  }
 
-  auto fetch_data = underlying_executor_->Run(fetch_tensors);
   drop_scope_counter_ += 1;
   if (!fetch_tensors.empty() ||
       drop_scope_counter_ == strategy_.num_iteration_per_drop_scope_) {
@@ -69,7 +76,11 @@ FeedFetchList ScopeBufferedSSAGraphExecutor::Run(
       scope->DeleteScope(local_scope);
     }
   }
-  return fetch_data;
+  if (eptr) {
+    std::rethrow_exception(eptr);
+  } else {
+    return fetch_data;
+  }
 }
 }  // namespace details
 }  // namespace framework

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -78,6 +78,10 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     set.clear();
   };
 
+  // Clean run context
+  run_op_futures_.clear();
+  exception_.reset();
+
   // Step 3. Execution
   while (!pending_vars.empty()) {
     // 1. Run All Ready ops
@@ -96,16 +100,19 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     auto cur_ready_vars = ready_vars.PopAll(1, &timeout);
 
     if (timeout) {
-      std::lock_guard<std::mutex> l(exception_mu_);
+      std::unique_lock<std::mutex> l(exception_mu_);
       if (exception_) {
+        l.unlock();
+        for (auto &run_op_future : run_op_futures_) {
+          run_op_future.wait();
+        }
+        l.lock();
         std::exception *exp = exception_.get();
         if (dynamic_cast<platform::EOFException *>(exp)) {
           auto e = *static_cast<platform::EOFException *>(exp);
-          exception_.reset();
           throw e;
         } else if (dynamic_cast<platform::EnforceNotMet *>(exp)) {
           auto e = *static_cast<platform::EnforceNotMet *>(exp);
-          exception_.reset();
           throw e;
         } else {
           LOG(FATAL) << "Unknown exception.";
@@ -222,7 +229,7 @@ void ThreadedSSAGraphExecutor::RunOp(
     }
   };
   if (pool_) {
-    pool_->enqueue(op_run);
+    run_op_futures_.emplace_back(pool_->enqueue(op_run));
   } else {
     op_run();
   }

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -15,6 +15,7 @@
 #pragma once
 
 #include <deque>
+#include <list>
 #include <string>
 #include <unordered_set>
 #include <utility>
@@ -77,6 +78,8 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
 
  private:
   ExecutionStrategy strategy_;
+  // use std::list because clear(), push_back, and for_each are O(1)
+  std::list<std::future<void>> run_op_futures_;
 };
 
 }  // namespace details

paddle/fluid/framework/parallel_executor.cc

@@ -95,7 +95,7 @@ ParallelExecutor::ParallelExecutor(
   }
 
   if (member_->local_scopes_.size() != 1 && local_scopes.empty()) {
-    BCastParamsToGPUs(bcast_vars);
+    BCastParamsToDevs(bcast_vars);
   }
   // Startup Program has been run. All local scopes has correct parameters.
 
@@ -131,7 +131,7 @@ ParallelExecutor::ParallelExecutor(
       member_->places_, std::move(member_->executor_)));
 }
 
-void ParallelExecutor::BCastParamsToGPUs(
+void ParallelExecutor::BCastParamsToDevs(
     const std::unordered_set<std::string> &vars) const {
   // the the initializing bcast, all vars would be bcast from device(0),
   // otherwise
@@ -202,7 +202,11 @@ void ParallelExecutor::BCastParamsToGPUs(
 #endif
     } else {
       platform::CPUPlace cpu;
-      for (size_t i = 1; i < member_->places_.size(); ++i) {
+      for (size_t i = 0; i < member_->places_.size(); ++i) {
+        if ((initializing && i == 0) ||
+            (!initializing && static_cast<int>(i) == var_dev_id))
+          continue;
+
         auto local_scope = member_->local_scopes_[i];
         auto *t = local_scope->Var(var)->GetMutable<LoDTensor>();
         t->Resize(dims);

paddle/fluid/framework/parallel_executor.h

@@ -66,7 +66,7 @@ class ParallelExecutor {
   void Run(const std::vector<std::string> &fetch_tensors,
            const std::string &fetched_var_name);
 
-  void BCastParamsToGPUs(const std::unordered_set<std::string> &vars) const;
+  void BCastParamsToDevs(const std::unordered_set<std::string> &vars) const;
 
  private:
   ParallelExecutorPrivate *member_;

paddle/fluid/framework/reader.h

@@ -29,11 +29,11 @@ enum ReaderStatus { kRunning, kStopped };
 
 class ReaderBase {
  public:
-  void ReadNext(std::vector<LoDTensor>* out);
+  virtual void ReadNext(std::vector<LoDTensor>* out);
 
-  void Shutdown();
+  virtual void Shutdown();
 
-  void Start();
+  virtual void Start();
 
   // Return the readers which are the end of decorating chain. Basically
   // they are readers just before read op.
@@ -42,7 +42,7 @@ class ReaderBase {
   virtual ~ReaderBase();
 
  protected:
-  virtual void ReadNextImpl(std::vector<LoDTensor>* out) = 0;
+  virtual void ReadNextImpl(std::vector<LoDTensor>* out) {}
 
   virtual void ShutdownImpl() {}
 

paddle/fluid/operators/CMakeLists.txt

@@ -259,12 +259,15 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
 op_library(sequence_conv_op DEPS context_project)
 op_library(sequence_pool_op DEPS sequence_pooling)
 op_library(lstm_op DEPS sequence2batch lstm_compute)
+op_library(hierarchical_sigmoid_op DEPS matrix_bit_code)
 op_library(lstmp_op DEPS sequence2batch lstm_compute)
 op_library(gru_op DEPS sequence2batch gru_compute)
 op_library(recurrent_op DEPS executor)
 op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
 op_library(cos_sim_op DEPS cos_sim_functor)
 op_library(parallel_do_op DEPS executor)
+op_library(unsqueeze_op DEPS reshape_op)
+op_library(squeeze_op DEPS reshape_op)
 
 if (WITH_GPU)
     op_library(conv_op DEPS vol2col depthwise_conv im2col)

paddle/fluid/operators/hierarchical_sigmoid_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "paddle/fluid/operators/hierarchical_sigmoid_op.h"
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+/**
+ * Organize the classes into a binary tree. At each node, a sigmoid function
+ * is used to calculate the probability of belonging to the right branch.
+ * This idea is from "F. Morin, Y. Bengio (AISTATS 05):
+ * Hierarchical Probabilistic Neural Network Language Model."
+ *
+ * Here we uses a simple way of making the binary tree.
+ * Assuming the number of classes C = 6,
+ * The classes are organized as a binary tree in the following way:
+ *
+ * @code{.py}
+ * *-*-*- 2
+ * | | |- 3
+ * | |
+ * | |-*- 4
+ * |   |- 5
+ * |
+ * |-*- 0
+ *   |- 1
+ * @endcode
+ *
+ * where * indicates an internal node, and each leaf node represents a class.
+ * - Node 0 ... C-2 are internal nodes.
+ * - Node C-1 ... 2C-2 are leaf nodes.
+ * - Class c is represented by leaf node \f$c+C-1\f$.
+ *
+ * We assign an id for each node:
+ * - the id of root be 0.
+ * - the left child of a node i is 2*i+1.
+ * - the right child of a node i is 2*i+2.
+ *
+ * It's easy to see that:
+ * - the parent of node i is \f$\left\lfloor(i-1)/2\right\rfloor\f$.
+ * - the j-th level ancestor of node i is
+ * \f$\left\lfloor(i+1)/2^{j+1}\right\rfloor - 1\f$.
+ * - A node i is a left child of its parent if \f$(i-1)\%2==0\f$.
+ *
+ */
+
+class HierarchicalSigmoidOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("W"), "Input(W) should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("PreOut"),
+                   "Output(PreOut) should not be null.");
+    const int64_t batch_size = ctx->GetInputDim("X")[0];
+    std::vector<int64_t> output_shape({batch_size, 1});
+    ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
+        ctx.GetPlace());
+  }
+};
+
+template <typename AttrType>
+class HierarchicalSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor, required) The input tensor with shape [N, D], "
+             "where N is the size of mini-batch, and D is the feature size.");
+    AddInput("W",
+             "(Tensor, required), The parameters of hierarchical "
+             "sigmoid operator, each of them is a 2-D tensor, the shape is"
+             "[num_classes - 1, D].");
+    AddInput("Label",
+             "(Tensor, required), The labels of training data. It's a"
+             "tensor with shape [N, 1].");
+    AddInput("Bias",
+             "(Tensor, optional), The bias is a tensor with shape"
+             "[1, num_classes - 1].");
+    AddOutput("Out",
+              "(Tensor, required) The output of hierarchical sigmoid operator."
+              "The shape is [N, 1].");
+    AddOutput("PreOut",
+              "(Tensor, required) A intermedia 2-D tensor with shape "
+              "[batch_size, code_length], where code_length represents the "
+              "maximum path length from root to leaf nodes.")
+        .AsIntermediate();
+    AddAttr<AttrType>("num_classes", "(int, required), The number of classes")
+        .SetDefault(2);
+    AddComment(R"DOC(
+The hierarchical sigmoid operator organize the classes into a binary tree.
+At each node, a sigmoid function is used to calculate the probability of
+belonging to the right branch. This idea is from
+"F. Morin, Y. Bengio (AISTATS 05):
+Hierarchical Probabilistic Neural Network Language Model."
+      )DOC");
+  }
+};
+
+class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("W"), "Input(W) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("PreOut"),
+                   "Input(Preout) should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("W")),
+                   "Output(W@Grad should not be null.)");
+    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")));
+    if (ctx->HasOutput(framework::GradVarName("Bias"))) {
+      ctx->SetOutputDim(framework::GradVarName("Bias"),
+                        ctx->GetInputDim("Bias"));
+    }
+    ctx->SetOutputDim(framework::GradVarName("W"), ctx->GetInputDim("W"));
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
+        ctx.GetPlace());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(hierarchical_sigmoid, ops::HierarchicalSigmoidOp,
+                  ops::HierarchicalSigmoidOpMaker<int>,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(hierarchical_sigmoid_grad, ops::HierarchicalSigmoidGradOp);
+REGISTER_OP_CPU_KERNEL(
+    hierarchical_sigmoid,
+    ops::HierarchicalSigmoidOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::HierarchicalSigmoidOpKernel<paddle::platform::CPUDeviceContext,
+                                     double>);
+REGISTER_OP_CPU_KERNEL(
+    hierarchical_sigmoid_grad,
+    ops::HierarchicalSigmoidGradOpKernel<paddle::platform::CPUDeviceContext,
+                                         float>,
+    ops::HierarchicalSigmoidGradOpKernel<paddle::platform::CPUDeviceContext,
+                                         double>);

paddle/fluid/operators/hierarchical_sigmoid_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#pragma once
+#include <iostream>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/clip_op.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/operators/math/matrix_bit_code.h"
+#include "paddle/fluid/platform/transform.h"
+namespace paddle {
+namespace operators {
+
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+using platform::Transform;
+
+template <typename DeviceContext, typename T>
+class HierarchicalSigmoidOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in = ctx.Input<framework::Tensor>("X");
+    auto* w = ctx.Input<framework::Tensor>("W");
+    auto* label = ctx.Input<framework::Tensor>("Label");
+    auto* bias = ctx.Input<framework::Tensor>("Bias");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+    auto* pre_out = ctx.Output<framework::Tensor>("PreOut");
+    size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
+    int64_t code_length = math::FindLastSet(num_classes - 1);
+    int64_t batch_size = in->dims()[0];
+    framework::Tensor sum;
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    auto* pre_out_data = pre_out->mutable_data<T>(
+        framework::make_ddim({batch_size, code_length}), ctx.GetPlace());
+    auto pre_out_mat = EigenMatrix<T>::From(*pre_out);
+    // Not all class(leaf) nodes' path lengths equal code_length, thus init as
+    // 0s can avoid out of path's loss.
+    math::SetConstant<DeviceContext, T> zero;
+    zero(dev_ctx, pre_out, static_cast<T>(0.0));
+    auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
+    math::RowwiseSum<DeviceContext, T> row_sum;
+    math::MatrixBitCodeFunctor<T> bit_code(num_classes, label->data<int64_t>());
+
+    std::vector<int64_t> sum_dims({batch_size, 1UL});
+    sum.mutable_data<T>(framework::make_ddim(sum_dims), ctx.GetPlace());
+    auto sum_mat = EigenMatrix<T>::From(sum);
+    out->mutable_data<T>(ctx.GetPlace());
+    auto out_mat = framework::EigenVector<T>::Flatten(*out);
+    if (bias) {
+      bit_code.Add(pre_out, *bias);
+    }
+    bit_code.Mul(pre_out, *w, *in);
+    // clip to [-40, 40]
+    Transform<DeviceContext> trans;
+    trans(ctx.template device_context<DeviceContext>(), pre_out_data,
+          pre_out_data + pre_out->numel(), pre_out_data,
+          ClipFunctor<T>(static_cast<T>(-40.0), static_cast<T>(40.0)));
+    bit_code.Sum(*pre_out, out, static_cast<T>(-1));
+    // use softrelu to calculate cross entropy
+    pre_out_mat.device(place) = (static_cast<T>(1.0) + pre_out_mat.exp()).log();
+    row_sum(dev_ctx, *pre_out, &sum);
+    // TODO(guosheng): Subtract the out of path's loss, since not all
+    // class(leaf) nodes' path lengths equal code_length. But it won't break the
+    // gradient check since both have the out of path's loss and will cancel out
+    // each other.
+    out_mat.device(place) = sum_mat + out_mat;
+  }
+};
+
+template <typename DeviceContext, typename T>
+class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* in = ctx.Input<framework::Tensor>("X");
+    auto* w = ctx.Input<framework::Tensor>("W");
+    auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* w_grad = ctx.Output<framework::Tensor>(framework::GradVarName("W"));
+    auto* bias_grad =
+        ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
+    auto* label = ctx.Input<framework::Tensor>("Label");
+    auto* pre_out = ctx.Input<framework::Tensor>("PreOut");
+    auto* out_grad =
+        ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    framework::Tensor pre_out_grad;
+
+    pre_out_grad.mutable_data<T>(pre_out->dims(), ctx.GetPlace());
+    in_grad->mutable_data<T>(ctx.GetPlace());
+    w_grad->mutable_data<T>(ctx.GetPlace());
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    math::SetConstant<DeviceContext, T> zero;
+    zero(dev_ctx, in_grad, static_cast<T>(0.0));
+    zero(dev_ctx, w_grad, static_cast<T>(0.0));
+
+    size_t num_classes = static_cast<size_t>(ctx.Attr<int>("num_classes"));
+    math::MatrixBitCodeFunctor<T> bit_code(num_classes, label->data<int64_t>());
+
+    auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
+    auto pre_out_mat = EigenMatrix<T>::From(*pre_out);
+    auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
+    auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
+    Eigen::array<int, 2> bcast({{1, static_cast<int>(pre_out_grad.dims()[1])}});
+
+    // softrelu derivative
+    pre_out_grad_mat.device(place) =
+        static_cast<T>(1.0) - static_cast<T>(1.0) / pre_out_mat.exp();
+    bit_code.Sub(&pre_out_grad);  // the gradient of clip(w * x + b)
+    pre_out_grad_mat.device(place) =
+        pre_out_grad_mat * out_grad_mat.broadcast(bcast);
+    // TODO(guosheng): multiply pre_out_grad with subgradient of clipping to
+    // be consistent with the clipping in forward.
+    if (bias_grad) {
+      bias_grad->mutable_data<T>(ctx.GetPlace());
+      zero(dev_ctx, bias_grad, static_cast<T>(0.0));
+      bit_code.AddGrad(pre_out_grad, bias_grad);
+    }
+    bit_code.MulGradWeight(pre_out_grad, w_grad, *in);
+    bit_code.MulGradError(pre_out_grad, *w, in_grad);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/CMakeLists.txt

@@ -51,6 +51,7 @@ math_library(sequence_padding)
 math_library(sequence_pooling DEPS math_function)
 math_library(sequence_scale)
 math_library(softmax DEPS math_function)
+math_library(matrix_bit_code)
 math_library(unpooling)
 math_library(vol2col)
 

paddle/fluid/operators/math/math_function_impl.h

@@ -155,7 +155,7 @@ class RowwiseSum<platform::CPUDeviceContext, T> {
     PADDLE_ENFORCE_EQ(in_dims.size(), 2U);
     auto height = in_dims[0];
     auto size = in_dims[1];
-    PADDLE_ENFORCE_EQ(out->numel(), size);
+    PADDLE_ENFORCE_EQ(out->numel(), height);
 
     T* out_buf = out->mutable_data<T>(out->place());
     const T* in_buf = input.data<T>();

paddle/fluid/operators/math/matrix_bit_code.cc

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "paddle/fluid/operators/math/matrix_bit_code.h"
+#include <iostream>
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T>
+void MatrixBitCodeFunctor<T>::Add(framework::Tensor* tmat,
+                                  const framework::Tensor& vec) {
+  SimpleCodeTable code_table(num_classes_);
+  size_t batch_size = tmat->dims()[0];
+  size_t width = tmat->dims()[1];
+  for (size_t i = 0; i < batch_size; ++i) {
+    auto code = code_table(static_cast<size_t>(ids_[i]));
+    int code_length = code.get_length();
+    for (int j = 0; j < code_length; ++j) {
+      size_t index = code.calc_index(j);
+      tmat->data<T>()[i * width + j] += vec.data<T>()[index];
+    }
+  }
+}
+
+template <typename T>
+void MatrixBitCodeFunctor<T>::AddGrad(const framework::Tensor& tmat,
+                                      framework::Tensor* vec) {
+  SimpleCodeTable code_table(num_classes_);
+  size_t batch_size = tmat.dims()[0];
+  size_t width = tmat.dims()[1];
+  for (size_t i = 0; i < batch_size; ++i) {
+    auto code = code_table(static_cast<size_t>(ids_[i]));
+    int code_length = code.get_length();
+    for (int j = 0; j < code_length; ++j) {
+      size_t index = code.calc_index(j);
+      vec->data<T>()[index] += tmat.data<T>()[i * width + j];
+    }
+  }
+}
+
+template <typename T>
+void MatrixBitCodeFunctor<T>::Sum(const framework::Tensor& tmat,
+                                  framework::Tensor* sum, T scale_sum) {
+  SimpleCodeTable code_table(num_classes_);
+  size_t num_samples = tmat.dims()[0];
+  size_t o_width = tmat.dims()[1];
+  for (size_t i = 0; i < num_samples; ++i) {
+    T sm = static_cast<T>(0.0);
+    auto code = code_table(static_cast<size_t>(ids_[i]));
+    int code_length = code.get_length();
+    for (int j = 0; j < code_length; ++j) {
+      if (code.calc_bit(j)) {
+        // calc_bit starts from right most bit, while data in tmat[i] is in the
+        // reverse order.
+        sm += tmat.data<T>()[i * o_width + j];
+      }
+    }
+    sum->data<T>()[i] = scale_sum * sm;
+  }
+}
+
+template <typename T>
+void MatrixBitCodeFunctor<T>::Mul(framework::Tensor* tmat,
+                                  const framework::Tensor& weight,
+                                  const framework::Tensor& input) {
+  SimpleCodeTable code_table(num_classes_);
+  size_t num_samples = tmat->dims()[0];
+  size_t tmat_width = tmat->dims()[1];
+  size_t input_width = input.dims()[1];
+  size_t weight_width = weight.dims()[1];
+  auto tmat_value = tmat->data<T>();
+  auto weight_value = weight.data<T>();
+  auto input_value = input.data<T>();
+  for (size_t i = 0; i < num_samples; ++i) {
+    auto code = code_table(static_cast<size_t>(ids_[i]));
+    int code_length = code.get_length();
+    for (int j = 0; j < code_length; ++j) {
+      size_t index = code.calc_index(j);
+      T sum = static_cast<T>(0.0);
+      for (size_t k = 0; k < input_width; ++k) {
+        sum += weight_value[weight_width * index + k] *
+               input_value[input_width * i + k];
+      }
+      tmat_value[i * tmat_width + j] += sum;
+    }
+  }
+}
+
+template <typename T>
+void MatrixBitCodeFunctor<T>::MulGradWeight(const framework::Tensor& tmat,
+                                            framework::Tensor* weight,
+                                            const framework::Tensor& input) {
+  SimpleCodeTable code_table(num_classes_);
+  size_t num_samples = tmat.dims()[0];
+  size_t input_width = input.dims()[1];
+  size_t tmat_width = tmat.dims()[1];
+  size_t weight_width = weight->dims()[1];
+  auto tmat_value = tmat.data<T>();
+  auto weight_value = weight->data<T>();
+  auto input_value = input.data<T>();
+  for (size_t i = 0; i < num_samples; ++i) {
+    auto code = code_table(static_cast<size_t>(ids_[i]));
+    int code_length = code.get_length();
+    for (int j = 0; j < code_length; ++j) {
+      size_t index = code.calc_index(j);
+
+      for (size_t k = 0; k < input_width; ++k) {
+        weight_value[weight_width * index + k] +=
+            tmat_value[i * tmat_width + j] * input_value[input_width * i + k];
+      }
+    }
+  }
+}
+
+template <typename T>
+void MatrixBitCodeFunctor<T>::MulGradError(const framework::Tensor& tmat,
+                                           const framework::Tensor& weight,
+                                           framework::Tensor* input) {
+  SimpleCodeTable code_table(num_classes_);
+  size_t num_samples = tmat.dims()[0];
+  size_t tmat_width = tmat.dims()[1];
+  size_t input_width = input->dims()[1];
+  size_t weight_width = weight.dims()[1];
+  auto tmat_value = tmat.data<T>();
+  auto weight_value = weight.data<T>();
+  auto input_value = input->data<T>();
+
+  for (size_t i = 0; i < num_samples; ++i) {
+    auto code = code_table(static_cast<size_t>(ids_[i]));
+    int code_length = code.get_length();
+    for (int j = 0; j < code_length; ++j) {
+      size_t index = code.calc_index(j);
+
+      for (size_t k = 0; k < input_width; ++k) {
+        input_value[input_width * i + k] +=
+            tmat_value[i * tmat_width + j] *
+            weight_value[weight_width * index + k];
+      }
+    }
+  }
+}
+
+template <typename T>
+void MatrixBitCodeFunctor<T>::Sub(framework::Tensor* tmat) {
+  SimpleCodeTable code_table(num_classes_);
+  size_t num_samples = tmat->dims()[0];
+  size_t o_width = tmat->dims()[1];
+  for (size_t i = 0; i < num_samples; ++i) {
+    auto code = code_table(static_cast<size_t>(ids_[i]));
+    int code_length = code.get_length();
+    for (int j = 0; j < code_length; ++j) {
+      if (code.calc_bit(j)) {
+        tmat->data<T>()[i * o_width + j] -= 1;
+      }
+    }
+  }
+}
+
+template class MatrixBitCodeFunctor<float>;
+template class MatrixBitCodeFunctor<double>;
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/math/matrix_bit_code.h

+/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#pragma once
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/platform/device_context.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+/**
+ * SimpleCodeTable class should support 3 functions:
+ *
+ * size_t size()
+ *   return the number of ids
+ *
+ * int get_max_code_length()
+ *   return the maximal code length
+ *
+ * SimpleCode operator()(size_t i)
+ *   return the i-th code. Code class is descriebed below.
+ *
+ * SimpleCode class should support 3 functions:
+ *
+ * int get_length()
+ *   return the length of the code
+ *
+ * size_t cal_index(int bit)
+ *   bit ranges from 0 to get_length() - 1
+ *   return the index for the (1+bit) level parent
+ *
+ * bool calc_bit(int bit)
+ *   return true if the bit level parent is the right child of (1+bit) level
+ *   parent
+ *
+ */
+
+/**
+ * return the 1-based index of the highest bit set
+ *
+ * for x > 0:
+ * \f[
+ *    FindLastSet(x) = 1 + \floor*{\log_{2}x}
+ * \f]
+ */
+inline constexpr size_t FindLastSet(size_t x) {
+  return std::is_same<size_t, unsigned int>::value
+             ? (x ? 8 * sizeof(x) - __builtin_clz(x) : 0)
+             : (std::is_same<size_t, unsigned long>::value  // NOLINT
+                    ? (x ? 8 * sizeof(x) - __builtin_clzl(x) : 0)
+                    : (x ? 8 * sizeof(x) - __builtin_clzll(x) : 0));
+}
+
+struct SimpleCode {
+  SimpleCode(size_t code, size_t num_classes) : c_(code + num_classes) {}
+  /**
+   * Here the id of root shoud be 1 rather than 0, thus the encoding of class c
+   * is `c + num_classes` and all siblings can get the same weight indice using
+   * prefixes.
+   * Weight index is the prefixes of encoding, thus leave out the right most
+   * bit in calc_index.
+   * Binary classification path is the suffixes of encoding, thus leave out the
+   * left most bit in calc_bit.
+   */
+  inline size_t calc_index(int bit) const { return (c_ >> (bit + 1)) - 1; }
+  inline bool calc_bit(int bit) const { return c_ & (1 << bit); }
+  inline int get_length() const { return FindLastSet(c_) - 1; }
+
+ private:
+  size_t c_;
+};
+
+struct SimpleCodeTable {
+  explicit SimpleCodeTable(size_t num_classes) : num_classes_(num_classes) {}
+  SimpleCode operator()(size_t code) const {
+    return SimpleCode(code, num_classes_);
+  }
+  size_t size() const { return num_classes_; }
+  int get_max_code_length() const { return FindLastSet(num_classes_ - 1); }
+
+ private:
+  size_t num_classes_;
+};
+
+template <typename T>
+class MatrixBitCodeFunctor {
+ public:
+  explicit MatrixBitCodeFunctor(size_t num_classes, const int64_t* ids)
+      : num_classes_(num_classes), ids_(ids) {}
+  /* For j < code_length
+       tmat(i, j) += vec(0, index(i, j))
+  */
+  void Add(framework::Tensor* tmat, const framework::Tensor& vec);
+
+  /* For j < code_length
+       vec(0, index(i, j)) += tmat(i, j)
+  */
+  void AddGrad(const framework::Tensor& tmat, framework::Tensor* vec);
+
+  /* For j < code_length
+    sum(i, 0) = \sum_j bit(i, j) * tmat(i, j)
+  */
+  void Sum(const framework::Tensor& tmat, framework::Tensor* sum, T scale_sum);
+
+  /* For j < code_length
+       tmat(i, j) -= bit(i, j)
+  */
+  void Sub(framework::Tensor* tmat);
+  /* For j < code_length
+       input.row(i) += tmat(i, j) * weight.row(index(i, j))
+  */
+  void Mul(framework::Tensor* tmat, const framework::Tensor& weight,
+           const framework::Tensor& input);
+
+  /* For index(i, j) >= 0:
+      weight.row(index(i, j)) += tmat(i, j) * input.row(i)
+  */
+  void MulGradWeight(const framework::Tensor& tmat, framework::Tensor* weight,
+                     const framework::Tensor& input);
+  /* For j < code_length
+    input.row(i) += tmat(i, j) * weight.row(index(i, j))
+  */
+  void MulGradError(const framework::Tensor& tmat,
+                    const framework::Tensor& weight, framework::Tensor* input);
+
+  size_t num_classes_;
+  const int64_t* ids_;
+};
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/reader/blocking_queue.h

@@ -81,6 +81,15 @@ class BlockingQueue {
     }
   }
 
+  void ReOpen() {
+    std::lock_guard<std::mutex> lock(mutex_);
+    closed_ = false;
+    std::deque<T> new_deque;
+    queue_.swap(new_deque);
+    send_cv_.notify_all();
+    receive_cv_.notify_all();
+  }
+
   void Close() {
     std::lock_guard<std::mutex> lock(mutex_);
     closed_ = true;

paddle/fluid/operators/reader/create_batch_reader_op.cc

@@ -23,7 +23,7 @@ class BatchReader : public framework::DecoratedReader {
   BatchReader(const std::shared_ptr<ReaderBase>& reader, int batch_size,
               bool discard_leftover)
       : DecoratedReader(reader),
-        batch_size_(batch_size),
+        batch_size_(static_cast<size_t>(batch_size)),
         discard_leftover_(discard_leftover) {
     buffer_.reserve(batch_size_);
   }
@@ -31,7 +31,7 @@ class BatchReader : public framework::DecoratedReader {
   void ReadNextImpl(std::vector<framework::LoDTensor>* out) override;
 
  private:
-  int batch_size_;
+  size_t batch_size_;
   bool discard_leftover_;
   std::vector<std::vector<framework::LoDTensor>> buffer_;
 };
@@ -78,7 +78,7 @@ class CreateBatchReaderOpMaker : public DecoratedReaderMakerBase {
 void BatchReader::ReadNextImpl(std::vector<framework::LoDTensor>* out) {
   buffer_.clear();
   buffer_.reserve(batch_size_);
-  for (int i = 0; i < batch_size_; ++i) {
+  for (size_t i = 0; i < batch_size_; ++i) {
     buffer_.push_back(std::vector<framework::LoDTensor>());
     reader_->ReadNext(&buffer_.back());
     if (buffer_.back().empty()) {
@@ -95,9 +95,9 @@ void BatchReader::ReadNextImpl(std::vector<framework::LoDTensor>* out) {
     // if buffer_ is empty, the 'out' will return as an empty vector.
     return;
   }
-  int out_num = buffer_[0].size();
+  size_t out_num = buffer_[0].size();
   out->reserve(out_num);
-  for (int j = 0; j < out_num; ++j) {
+  for (size_t j = 0; j < out_num; ++j) {
     // Merge shape and check date type
     std::type_index batch_type = buffer_[0][j].type();
     framework::DDim batch_shape = buffer_[0][j].dims();

paddle/fluid/operators/reader/create_py_reader_op.cc

@@ -27,19 +27,17 @@ class PyReader : public framework::FileReader {
     queue_ = queue;
   }
 
-  void ReadNextImpl(std::vector<framework::LoDTensor>* out) override {
+  void ReadNext(std::vector<framework::LoDTensor>* out) override {
     bool success;
     *out = queue_->Pop(&success);
     if (!success) out->clear();
   }
 
- private:
-  void ShutdownImpl() override { /* TODO */
-  }
+  void Shutdown() override { queue_->Close(); }
 
-  void StartImpl() override { /* TODO */
-  }
+  void Start() override { queue_->ReOpen(); }
 
+ private:
   std::shared_ptr<LoDTensorBlockingQueue> queue_;
 };
 

paddle/fluid/operators/reader/lod_tensor_blocking_queue.h

@@ -58,12 +58,15 @@ class LoDTensorBlockingQueue {
 
   inline size_t Size() const { return queue_.Size(); }
 
-  inline void Close() { return queue_.Close(); }
+  inline void ReOpen() { queue_.ReOpen(); }
+
+  inline void Close() { queue_.Close(); }
 
   inline bool IsClosed() const { return queue_.IsClosed(); }
 
  private:
-  void CheckDims(const std::vector<framework::LoDTensor>& lod_tensor_vec) {
+  void CheckDims(
+      const std::vector<framework::LoDTensor>& lod_tensor_vec) const {
     PADDLE_ENFORCE(dims_.size() == lod_tensor_vec.size(),
                    "Expect input size is %d but found %s", dims_.size(),
                    lod_tensor_vec.size());

paddle/fluid/operators/squeeze_op.cc

+/* Copyright (c) 2018 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. */
+
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+class SqueezeOpInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of SqueezeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of SqueezeOp should not be null.");
+
+    const auto &x_dims = ctx->GetInputDim("X");
+    // Check input tensor dims (<6) Eigen limit.
+    PADDLE_ENFORCE(x_dims.size() <= 6,
+                   "Invalid dimnesions, the rank of Input(X) "
+                   "should be in the range of [1, 6] (Eigen limit).");
+
+    const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
+    for (int a : axes) {
+      PADDLE_ENFORCE_LT(a, x_dims.size(),
+                        "The squeeze axis should be less than input "
+                        "tensor's rank.");
+    }
+
+    auto out_dims = GetOutputShape(axes, x_dims);
+    ctx->SetOutputDim("Out", out_dims);
+    if (x_dims[0] == out_dims[0]) {
+      // Only pass LoD when the first dimension of output and Input(X)
+      // are the same.
+      ctx->ShareLoD("X", "Out");
+    }
+  }
+
+  static framework::DDim GetOutputShape(const std::vector<int> squeeze_dims,
+                                        const framework::DDim &in_dims) {
+    size_t num_squeeze_dims = squeeze_dims.size();
+    int cnt_squeezed_dims = 0;
+    bool should_squeeze[9] = {false};
+
+    // Determines number of dimensions of output tensor after squeeze.
+    // Mark and count the dimensions need to be squeezed
+    if (num_squeeze_dims == 0) {
+      for (int idx = 0; idx < in_dims.size(); ++idx) {
+        if (in_dims[idx] == 1) {
+          should_squeeze[idx] = true;
+          ++cnt_squeezed_dims;
+        }
+      }
+    } else {
+      for (size_t idx = 0; idx < num_squeeze_dims; ++idx) {
+        int current = squeeze_dims[idx] < 0 ? squeeze_dims[idx] + in_dims.size()
+                                            : squeeze_dims[idx];
+        // Check current index, the upper limit has beed checked in line 36.
+        PADDLE_ENFORCE(current >= 0,
+                       "Invalid axis, the negative axis is out of range.");
+        PADDLE_ENFORCE(in_dims[current] == 1,
+                       "Invalid axis index, the axis that will be squeezed "
+                       "should be equal to 1.");
+
+        if (!(should_squeeze[current])) {
+          ++cnt_squeezed_dims;
+        }
+        should_squeeze[current] = true;
+      }
+    }
+
+    // Make output dimensions
+    std::vector<int64_t> output_shape(in_dims.size() - cnt_squeezed_dims, 0);
+    for (int in_idx = 0, out_idx = 0; in_idx < in_dims.size(); ++in_idx) {
+      if (!should_squeeze[in_idx]) {
+        output_shape[out_idx++] = in_dims[in_idx];
+      }
+    }
+
+    return framework::make_ddim(output_shape);
+  }
+};
+
+class SqueezeOp : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto &axes = Attr<std::vector<int>>("axes");
+    auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
+    auto out_dims = SqueezeOpInferShape::GetOutputShape(axes, x_dims);
+
+    framework::AttributeMap attrs;
+    attrs["shape"] = framework::vectorize2int(out_dims);
+    attrs["inplace"] = Attr<bool>("inplace");
+    // Invoke Reshape Op
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape", {{"X", {Input("X")}}, {"Shape", {}}},
+        {{"Out", {Output("Out")}}}, attrs);
+    reshape_op->Run(scope, place);
+  }
+};
+
+class SqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor). The input tensor of squeeze operator.");
+    AddOutput("Out", "(Tensor). The output tensor of squeeze operator.");
+    AddAttr<std::vector<int>>("axes",
+                              "(std::vector<int>). List of integers,"
+                              " indicating the dimensions to squeeze.")
+        .SetDefault({});
+    AddAttr<bool>("inplace",
+                  "(default: false) Squeeze the source tensor's shape without "
+                  "memory copy. When Attr(inplace) is set true, the output "
+                  "tensor shares memory with Input(X), otherwise, a new output "
+                  "tensor is created, and its data are copied from Input(x).")
+        .SetDefault(false);
+    AddComment(R"DOC(
+        Squeeze Operator.
+        
+        Remove single-dimensional entries from the shape of a tensor. 
+        Takes a parameter axes with a list of axes to squeeze. 
+        If axes is not provided, all the single dimensions will be removed from the shape. 
+        If an axis is selected with shape entry not equal to one, an error is raised.
+        
+        Examples:
+        Case 1:
+          Given 
+            X.shape = (1, 3, 1, 5)
+          and
+            axes = [0]
+          we get:
+            Out.shape = (3, 1, 5)
+
+        Case 2:
+          Given
+            X.shape = (1, 3, 1, 5)
+          and 
+            axes = []
+          we get:
+            Out.shape = (3, 5)
+    )DOC");
+  }
+};
+
+class SqueezeGradInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *context) const override {
+    context->SetOutputDim(framework::GradVarName("X"),
+                          context->GetInputDim("X"));
+    context->ShareLoD("X", framework::GradVarName("X"));
+  }
+};
+
+class SqueezeGradOp : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto dx_name = Output(framework::GradVarName("X"));
+    auto dout_name = Input(framework::GradVarName("Out"));
+    auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
+    framework::AttributeMap attrs;
+    attrs["shape"] = framework::vectorize2int(x_dims);
+    attrs["inplace"] = Attr<bool>("inplace");
+
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape", {{"X", {dout_name}}, {"Shape", {}}}, {{"Out", {dx_name}}},
+        attrs);
+    reshape_op->Run(scope, place);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+// Tell linker to use reshape op
+USE_OP(reshape);
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(squeeze, ops::SqueezeOp, ops::SqueezeOpMaker,
+                  ops::SqueezeOpInferShape,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(squeeze_grad, ops::SqueezeGradOp, ops::SqueezeGradInferShape);

paddle/fluid/operators/unsqueeze_op.cc

+/* Copyright (c) 2018 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. */
+
+#include <string>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+class UnsqueezeOpInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of UnsqueezeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of UnsqueezeOp should not be null.");
+
+    const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
+    const auto &x_dims = ctx->GetInputDim("X");
+    // Validity Check: input tensor dims (<6).
+    PADDLE_ENFORCE(x_dims.size() <= 6,
+                   "Invalid dimensions, the rank of Input(X) "
+                   "should be in the range of [1, 6] (Eigen limit)");
+    auto out_dims = GetOutputShape(axes, x_dims);
+    ctx->SetOutputDim("Out", out_dims);
+    if (x_dims[0] == out_dims[0]) {
+      // Only pass LoD when the first dimension of output and Input(X)
+      // are the same.
+      ctx->ShareLoD("X", "Out");
+    }
+  }
+
+  static framework::DDim GetOutputShape(const std::vector<int> unsqz_dims,
+                                        const framework::DDim &in_dims) {
+    int output_size = in_dims.size() + static_cast<int>(unsqz_dims.size());
+    int cur_output_size = in_dims.size();
+    std::vector<int64_t> output_shape(output_size, 0);
+
+    // Validity Check: rank range.
+    PADDLE_ENFORCE(output_size <= 6,
+                   "The output tensor's rank should be less than 6.");
+
+    for (int axis : unsqz_dims) {
+      int cur = axis < 0 ? axis + cur_output_size + 1 : axis;
+      // Vaildity Check: the axis bound
+      PADDLE_ENFORCE(
+          cur >= 0 && cur <= cur_output_size,
+          "The unsqueeze dims must be within range of current rank.");
+      // Move old axis, and insert new axis
+      for (int i = cur_output_size; i >= cur; --i) {
+        if (output_shape[i] == 1) {
+          // Move axis
+          output_shape[i + 1] = 1;
+          output_shape[i] = 0;
+        }
+      }
+      output_shape[cur] = 1;
+      // Add the output size.
+      cur_output_size++;
+    }
+
+    // Make output shape
+    for (int in_idx = 0, out_idx = 0; out_idx < output_size; ++out_idx) {
+      if (output_shape[out_idx] == 0) {
+        output_shape[out_idx] = in_dims[in_idx++];
+      }
+    }
+
+    return framework::make_ddim(output_shape);
+  }
+};
+
+class UnsqueezeOp : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto &axes = Attr<std::vector<int>>("axes");
+    auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
+    auto out_dims = UnsqueezeOpInferShape::GetOutputShape(axes, x_dims);
+
+    framework::AttributeMap attrs;
+    attrs["shape"] = framework::vectorize2int(out_dims);
+    attrs["inplace"] = Attr<bool>("inplace");
+    // Invoke Reshape op.
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape", {{"X", {Input("X")}}, {"Shape", {}}},
+        {{"Out", {Output("Out")}}}, attrs);
+    reshape_op->Run(scope, place);
+  }
+};
+
+class UnsqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor). The input tensor of unsqueeze operator.");
+    AddOutput("Out", "(Tensor). The output tensor of unsqueeze operator.");
+    AddAttr<std::vector<int>>("axes",
+                              "(std::vector<int>). List of integers,"
+                              " indicating the dimensions to be inserted")
+        .AddCustomChecker([](const std::vector<int> &axes) {
+          PADDLE_ENFORCE(!axes.empty(),
+                         "Invalid axes, The unsqueeze axes is empty.");
+          // Validity Check: axes dims (<6).
+          PADDLE_ENFORCE(static_cast<int>(axes.size()) < 6,
+                         "Invalid dimensions, dynamic dimensions should be "
+                         "within [1, 6] dimensions (Eigen limit).");
+          // Validity Check: the range of unsqueeze aixs.
+          for (int axis : axes) {
+            PADDLE_ENFORCE(axis < 6,
+                           "Invalid dimensions, input axis should be"
+                           " within [1, 6] dimensions (Eigen limit).");
+          }
+        });
+    AddAttr<bool>(
+        "inplace",
+        "(default: false) Unsqueeze the source tensor's shape without "
+        "memory copy. When Attr(inplace) is set true, the output "
+        "tensor shares memory with Input(X), otherwise, a new output "
+        "tensor is created, and its data are copied from Input(x).")
+        .SetDefault(false);
+    AddComment(R"DOC(
+    Unsqueeze Operator.
+    
+    Insert single-dimensional entries to the shape of a tensor. 
+    Takes one required argument axes, a list of dimensions that will be inserted. 
+    Dimension indices in axes are as seen in the output tensor. 
+
+    For example: 
+      Given a tensor such that tensor with shape [3, 4, 5], 
+      then Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]
+    )DOC");
+  }
+};
+
+class UnsqueezeGradInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext *ctx) const override {
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+    ctx->ShareLoD("X", framework::GradVarName("X"));
+  }
+};
+
+class UnsqueezeGradOp : public framework::OperatorBase {
+ public:
+  using OperatorBase::OperatorBase;
+
+ private:
+  void RunImpl(const framework::Scope &scope,
+               const platform::Place &place) const override {
+    auto dx_name = Output(framework::GradVarName("X"));
+    auto dout_name = Input(framework::GradVarName("Out"));
+    auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
+
+    framework::AttributeMap attrs;
+    attrs["shape"] = framework::vectorize2int(x_dims);
+    attrs["inplace"] = Attr<bool>("inplace");
+
+    auto reshape_op = framework::OpRegistry::CreateOp(
+        "reshape", {{"X", {dout_name}}, {"Shape", {}}}, {{"Out", {dx_name}}},
+        attrs);
+    reshape_op->Run(scope, place);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+// Tell linker to use reshape op.
+USE_OP(reshape);
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(unsqueeze, ops::UnsqueezeOp, ops::UnsqueezeOpMaker,
+                  ops::UnsqueezeOpInferShape,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(unsqueeze_grad, ops::UnsqueezeGradOp,
+                  ops::UnsqueezeGradInferShape);

paddle/fluid/pybind/pybind.cc

@@ -14,6 +14,7 @@ limitations under the License. */
 #include <Python.h>
 #include <algorithm>
 #include <map>
+#include <memory>
 #include <mutex>  // NOLINT // for call_once
 #include <string>
 #include <unordered_map>
@@ -66,6 +67,14 @@ bool IsCompiledWithCUDA() {
 #endif
 }
 
+bool IsCompiledWithDIST() {
+#ifdef PADDLE_WITH_DIST
+  return true;
+#else
+  return false;
+#endif
+}
+
 PYBIND11_PLUGIN(core) {
   py::module m("core", "C++ core of PaddlePaddle");
 
@@ -302,7 +311,8 @@ All parameter, weight, gradient are variables in Paddle.
       ::paddle::operators::reader::LoDTensorBlockingQueue;
   using LoDTensorBlockingQueueHolder =
       ::paddle::operators::reader::LoDTensorBlockingQueueHolder;
-  py::class_<LoDTensorBlockingQueue>(m, "LoDTensorBlockingQueue", "")
+  py::class_<LoDTensorBlockingQueue, std::shared_ptr<LoDTensorBlockingQueue>>(
+      m, "LoDTensorBlockingQueue", "")
       .def("push",
            [](LoDTensorBlockingQueue &self,
               const std::vector<framework::LoDTensor> &lod_tensor_vec) {
@@ -317,7 +327,7 @@ All parameter, weight, gradient are variables in Paddle.
   m.def("init_lod_tensor_blocking_queue",
         [](Variable &var, size_t capacity,
            const std::vector<std::vector<int64_t>> &shapes)
-            -> LoDTensorBlockingQueue * {
+            -> std::shared_ptr<LoDTensorBlockingQueue> {
               std::vector<DDim> dims(shapes.size());
               std::transform(shapes.begin(), shapes.end(), dims.begin(),
                              [](const std::vector<int64_t> &shape) {
@@ -325,9 +335,9 @@ All parameter, weight, gradient are variables in Paddle.
                              });
               auto *holder = var.GetMutable<LoDTensorBlockingQueueHolder>();
               holder->InitOnce(capacity, dims);
-              return holder->GetQueue().get();
+              return holder->GetQueue();
             },
-        py::return_value_policy::reference);
+        py::return_value_policy::copy);
 
   py::class_<Scope>(m, "Scope", "")
       .def("var",
@@ -508,6 +518,7 @@ All parameter, weight, gradient are variables in Paddle.
         [](bool init_p2p) { framework::InitDevices(init_p2p); });
 
   m.def("is_compiled_with_cuda", IsCompiledWithCUDA);
+  m.def("is_compiled_with_dist", IsCompiledWithDIST);
 #ifdef PADDLE_WITH_CUDA
   m.def("is_float16_supported", [](const platform::CUDAPlace &place) -> bool {
     // Only GPUs with Compute Capability >= 53 support float16
@@ -534,6 +545,8 @@ All parameter, weight, gradient are variables in Paddle.
       });
 
   py::class_<LoDTensorArray>(m, "LoDTensorArray")
+      .def("__init__",
+           [](LoDTensorArray &instance) { new (&instance) LoDTensorArray(); })
       .def("__getitem__",
            [](LoDTensorArray &self, size_t i) { return &self.at(i); },
            py::return_value_policy::reference)
@@ -656,7 +669,7 @@ All parameter, weight, gradient are variables in Paddle.
                   const std::string &, Scope *, std::vector<Scope *> &,
                   const ExecutionStrategy &, const BuildStrategy &, size_t,
                   size_t>())
-      .def("bcast_params", &ParallelExecutor::BCastParamsToGPUs)
+      .def("bcast_params", &ParallelExecutor::BCastParamsToDevs)
       // NOTE: even we return a vec<Scope*>* to Python use reference policy.
       // We still cannot get local_scope from this vector, since the element
       // of vec<Scope*> will be freed by Python GC. We can only return Scope*

python/CMakeLists.txt

@@ -92,8 +92,15 @@ install(DIRECTORY ${PADDLE_PYTHON_PACKAGE_DIR}
     DESTINATION opt/paddle/share/wheels
 )
 
-find_program(PATCHELF_EXECUTABLE patchelf)
-if(NOT PATCHELF_EXECUTABLE)
-  message(FATAL_ERROR "patchelf not found, please install it.\n"
-          "For Ubuntu, the command is: apt-get install -y patchelf.")
-endif()
+if(APPLE)
+  find_program(INSTALL_NAME_TOOL_EXECUTABLE install_name_tool)
+  if(NOT INSTALL_NAME_TOOL_EXECUTABLE)
+    message(FATAL_ERROR "install_name_tool not found, please check.\n")
+  endif()
+else(APPLE)
+  find_program(PATCHELF_EXECUTABLE patchelf)
+  if(NOT PATCHELF_EXECUTABLE)
+    message(FATAL_ERROR "patchelf not found, please install it.\n"
+            "For Ubuntu, the command is: apt-get install -y patchelf.")
+  endif()
+endif(APPLE)

python/paddle/fluid/__init__.py

@@ -44,7 +44,7 @@ import metrics
 import transpiler
 from param_attr import ParamAttr, WeightNormParamAttr
 from data_feeder import DataFeeder
-from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
+from core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
 from transpiler import DistributeTranspiler, InferenceTranspiler, \
     memory_optimize, release_memory
 from concurrency import (Go, make_channel, channel_send, channel_recv,
@@ -65,13 +65,14 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
               'io',
               'initializer',
               'layers',
-              'transpiler'
+              'transpiler',
               'nets',
               'optimizer',
               'learning_rate_decay',
               'backward',
               'regularizer',
               'LoDTensor',
+              'LoDTensorArray',
               'CPUPlace',
               'CUDAPlace',
               'CUDAPinnedPlace',
@@ -121,6 +122,9 @@ def __bootstrap__():
         'eager_delete_scope', 'use_mkldnn', 'initial_cpu_memory_in_mb',
         'init_allocated_mem'
     ]
+    if core.is_compiled_with_dist():
+        read_env_flags.append('rpc_deadline')
+
     if core.is_compiled_with_cuda():
         read_env_flags += [
             'fraction_of_gpu_memory_to_use', 'cudnn_deterministic'

python/paddle/fluid/layers/io.py

@@ -24,7 +24,8 @@ from layer_function_generator import generate_layer_fn, templatedoc
 __all__ = [
     'data', 'BlockGuardServ', 'ListenAndServ', 'Send', 'Recv',
     'open_recordio_file', 'open_files', 'read_file', 'shuffle', 'batch',
-    'double_buffer', 'random_data_generator', 'Preprocessor', 'load'
+    'double_buffer', 'random_data_generator', 'py_reader', 'Preprocessor',
+    'load'
 ]
 
 
@@ -445,6 +446,88 @@ def random_data_generator(low, high, shapes, lod_levels, for_parallel=True):
     return monkey_patch_reader_methods(main_prog_var)
 
 
+def py_reader(capacity, shapes, dtypes, lod_levels=None):
+    """
+    Create a reader and blocking queue for data feeding in Python
+    
+    This layer returns a Reader Variable and a BlockingQueue.
+    The BlockingQueue provides `push()` method to push a `LoDTensorArray` 
+    object into the queue in Python side. In C++ side, the Reader 
+    Variable would invoke `pop()` method of the queue to retrieve the 
+    feeding data. The process of feeding data in Python side and fetching 
+    data in C++ side can run in parallel. The BlockingQueue should be closed 
+    using `close()` method when unused.
+
+    Args:
+       capacity(int): The maximum capacity of the BlockingQueue.
+       shapes(list): List of tuples which declaring data shapes.
+       dtypes(list): List of strs which declaring data type. 
+       lod_levels(list): List of ints which declaring data lod_level.
+
+    Returns:
+       tuple(Variable, BlockingQueue):
+       A Reader Variable from which we can get feeding data.
+       
+       A BlockingQueue object for data feeding.
+
+    Examples:
+
+        .. code-block:: python
+
+            reader, queue = fluid.layers.py_reader(
+                                             capacity=10,
+                                             shapes=[[-1,3,224,224], [-1,1]],
+                                             dtypes=['float32', 'int64'])
+            # Via the reader, we can use 'read_file' layer to get data:
+            image, label = fluid.layers.read_file(reader)
+            
+            # Via the blocking queue, we can feed data using threads
+            def feed_data(queue, feed_images, feed_labels):
+                for feed_image, feed_label in zip(feed_images, feed_labels):
+                    data = core.LoDTensorArray()
+                    data.append(feed_image)
+                    data.append(feed_label)
+                    queue.push(data)
+            
+            thread = threading.Thread(target=feed_data, args=(queue, feed_images, feed_labels))
+            thread.start()
+    """
+    dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
+    shape_concat = []
+    ranks = []
+
+    for shape in shapes:
+        shape_concat.extend(shape)
+        ranks.append(len(shape))
+
+    if lod_levels is None:
+        lod_levels = [0] * len(shapes)
+
+    queue_name = unique_name('lod_tensor_blocking_queue')
+    var = global_scope().var(queue_name)
+    feed_queue = core.init_lod_tensor_blocking_queue(var, capacity, shapes)
+
+    startup_blk = default_startup_program().current_block()
+    startup_var = startup_blk.create_var(name=unique_name('create_py_reader'))
+    startup_blk.append_op(
+        type='create_py_reader',
+        inputs={'blocking_queue': queue_name},
+        outputs={'Out': [startup_var]},
+        attrs={
+            'shape_concat': shape_concat,
+            'lod_levels': lod_levels,
+            'ranks': ranks
+        })
+
+    startup_var.desc.set_dtypes(dtypes)
+    startup_var.persistable = True
+
+    main_prog_var = _copy_reader_var_(default_main_program().current_block(),
+                                      startup_var)
+
+    return monkey_patch_reader_methods(main_prog_var), feed_queue
+
+
 def open_files(filenames,
                shapes,
                lod_levels,

python/paddle/fluid/layers/nn.py

@@ -85,6 +85,7 @@ __all__ = [
     'transpose',
     'im2sequence',
     'nce',
+    'hsigmoid',
     'beam_search',
     'row_conv',
     'multiplex',
@@ -3871,6 +3872,74 @@ def nce(input,
     return cost / (num_neg_samples + 1)
 
 
+def hsigmoid(input, label, num_classes, param_attr=None, bias_attr=None):
+    """
+    The hierarchical sigmoid operator is used to accelerate the training
+    process of language model. This operator organizes the classes into a 
+    complete binary tree, each leaf node represents a class(a word) and each
+    internal node acts as a binary classifier. For each word there's a unique
+    path from root to it's leaf node, hsigmoid calculate the cost for each
+    internal node on the path, and sum them to get a total cost. hsigmoid can
+    achive a acceleration from :math:`O(N)` to :math:`O(logN)`, where :math:`N`
+    represents the size of word dict.
+
+    Refer to `Hierarchical Probabilistic Neural Network Language Model
+    <http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf>`_
+    
+    Args:
+        input (Variable): The input tensor variable with shape 
+            :math:`[N \\times D]`, where :math:`N` is the size of mini-batch,
+            and :math:`D` is the feature size.
+        label (Variable): The tensor variable contains labels of training data.
+            It's a tensor with shape is :math:`[N \\times 1]`.
+        num_classes: (int), The number of classes, must not be less than 2.
+        param_attr (ParamAttr|list of ParamAttr, default None): The parameter
+             attribute for learnable parameters/weights of this layer.
+        bias_attr (ParamAttr|list of ParamAttr, default None):  The parameter 
+             attribute for the bias of this layer. If it is set to False, no
+             bias will be applied.
+
+    Returns:
+        Out: (Tensor) The cost of hierarchical sigmoid operator. the shape is [N, 1]
+
+    Examples:
+
+        .. code-block:: python
+
+            x = fluid.layers.data(name='x', shape=[2], dtype='float32')
+            y = fluid.layers.data(name='y', shape=[1], dtype='int64')
+            out = fluid.layers.hsigmoid(input=x, label=y, num_classes=6)
+    """
+
+    helper = LayerHelper('hierarchical_sigmoid', **locals())
+    dtype = helper.input_dtype()
+    out = helper.create_tmp_variable(dtype)
+    pre_out = helper.create_tmp_variable(dtype)
+    dim = input.shape[1]
+    if num_classes < 2:
+        raise ValueError("num_classes must not be less than 2.")
+    weights = helper.create_parameter(
+        attr=helper.param_attr,
+        shape=[num_classes - 1, dim],
+        is_bias=False,
+        dtype=input.dtype)
+    inputs = {"X": input, "W": weights, "Label": label}
+    if helper.bias_attr:
+        bias = helper.create_parameter(
+            attr=helper.bias_attr,
+            shape=[1, num_classes - 1],
+            is_bias=True,
+            dtype=input.dtype)
+        inputs['Bias'] = bias
+    helper.append_op(
+        type="hierarchical_sigmoid",
+        inputs=inputs,
+        outputs={"Out": out,
+                 "PreOut": pre_out},
+        attrs={"num_classes": num_classes})
+    return out
+
+
 def transpose(x, perm, name=None):
     """
     Permute the dimensions of `input` according to `perm`.

python/paddle/fluid/optimizer.py

@@ -123,7 +123,7 @@ class Optimizer(object):
         """
         pass
 
-    def _finish_update(self, block):
+    def _finish_update(self, block, parameters):
         """Finish any custom updates needed
            before completing an optimization step
 
@@ -132,7 +132,7 @@ class Optimizer(object):
             parameters: list of parameter variables for the optimizer
 
         Returns:
-            list of finish ops or None
+            None
         """
         pass
 
@@ -236,7 +236,8 @@ class Optimizer(object):
 
             # Get custom finish ops for subclasses
             # FIXME: Need to fix this once we figure out how to handle dependencies
-            self._finish_update(loss.block)
+            self._finish_update(loss.block,
+                                [p[0] for p in parameters_and_grads])
 
             end = len(global_block.ops)
             return global_block.slice_ops(start, end)
@@ -486,6 +487,8 @@ class AdamOptimizer(Optimizer):
     """
     _moment1_acc_str = "moment1"
     _moment2_acc_str = "moment2"
+    _beta1_pow_acc_str = "beta1_pow_acc"
+    _beta2_pow_acc_str = "beta2_pow_acc"
 
     def __init__(self,
                  learning_rate=0.001,
@@ -507,32 +510,22 @@ class AdamOptimizer(Optimizer):
     def _create_accumulators(self, block, parameters):
         assert isinstance(block, framework.Block)
 
-        main_block = block.program.global_block()
-        # Create beta1 and beta2 power tensors
-        beta_shape = [1]
-        self._beta1_pow_acc = self.helper.create_global_variable(
-            name=unique_name.generate('beta1_pow_acc'),
-            dtype='float32' if self._dtype == None else self._dtype,
-            shape=beta_shape,
-            lod_level=0,
-            persistable=True)
-        self.helper.set_variable_initializer(
-            self._beta1_pow_acc, initializer=Constant(self._beta1))
-
-        self._beta2_pow_acc = self.helper.create_global_variable(
-            name=unique_name.generate('beta2_pow_acc'),
-            dtype='float32' if self._dtype == None else self._dtype,
-            shape=beta_shape,
-            lod_level=0,
-            persistable=True)
-
-        self.helper.set_variable_initializer(
-            self._beta2_pow_acc, initializer=Constant(self._beta2))
-
         # Create accumulator tensors for first and second moments
         for p in parameters:
             self._add_accumulator(self._moment1_acc_str, p)
             self._add_accumulator(self._moment2_acc_str, p)
+            self._add_accumulator(
+                name=self._beta1_pow_acc_str,
+                param=p,
+                dtype='float32',
+                fill_value=self._beta1,
+                shape=[1])
+            self._add_accumulator(
+                name=self._beta2_pow_acc_str,
+                param=p,
+                dtype='float32',
+                fill_value=self._beta2,
+                shape=[1])
 
     def _append_optimize_op(self, block, param_and_grad):
         assert isinstance(block, framework.Block)
@@ -541,6 +534,11 @@ class AdamOptimizer(Optimizer):
                                         param_and_grad[0])
         moment2 = self._get_accumulator(self._moment2_acc_str,
                                         param_and_grad[0])
+        beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
+                                              param_and_grad[0])
+        beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
+                                              param_and_grad[0])
+
         # create the adam optimize op
         adam_op = block.append_op(
             type=self.type,
@@ -550,8 +548,8 @@ class AdamOptimizer(Optimizer):
                 "LearningRate": self._create_param_lr(param_and_grad),
                 "Moment1": moment1,
                 "Moment2": moment2,
-                "Beta1Pow": self._beta1_pow_acc,
-                "Beta2Pow": self._beta2_pow_acc
+                "Beta1Pow": beta1_pow_acc,
+                "Beta2Pow": beta2_pow_acc
             },
             outputs={
                 "ParamOut": param_and_grad[0],
@@ -566,24 +564,28 @@ class AdamOptimizer(Optimizer):
 
         return adam_op
 
-    def _finish_update(self, block):
+    def _finish_update(self, block, parameters):
         """Update Beta1 and Beta2 Power accumulators
         """
         assert isinstance(block, framework.Block)
         main_block = block.program.global_block()
-        scale_beta1 = main_block.append_op(
-            type="scale",
-            inputs={"X": self._beta1_pow_acc},
-            outputs={"Out": self._beta1_pow_acc},
-            attrs={"scale": self._beta1})
-
-        scale_beta2 = main_block.append_op(
-            type="scale",
-            inputs={"X": self._beta2_pow_acc},
-            outputs={"Out": self._beta2_pow_acc},
-            attrs={"scale": self._beta2})
-
-        return [scale_beta1, scale_beta2]
+        for param in parameters:
+            with param.block.program.optimized_guard(param):
+                beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
+                                                      param)
+                beta2_pow_acc = self._get_accumulator(self._beta2_pow_acc_str,
+                                                      param)
+                main_block.append_op(
+                    type="scale",
+                    inputs={"X": beta1_pow_acc},
+                    outputs={"Out": beta1_pow_acc},
+                    attrs={"scale": self._beta1})
+
+                main_block.append_op(
+                    type="scale",
+                    inputs={"X": beta2_pow_acc},
+                    outputs={"Out": beta2_pow_acc},
+                    attrs={"scale": self._beta2})
 
 
 class AdamaxOptimizer(Optimizer):
@@ -626,6 +628,7 @@ class AdamaxOptimizer(Optimizer):
     """
     _moment_acc_str = "moment"
     _inf_norm_acc_str = "inf_norm"
+    _beta1_pow_acc_str = "beta1_pow_acc"
 
     def __init__(self,
                  learning_rate=0.001,
@@ -645,21 +648,16 @@ class AdamaxOptimizer(Optimizer):
         self._epsilon = epsilon
 
     def _create_accumulators(self, block, parameters):
-        # Create beta1 power accumulator tensor
-        beta_shape = [1]
-        self._beta1_pow_acc = self.helper.create_global_variable(
-            name=unique_name.generate('beta1_pow_acc'),
-            dtype='float32' if self._dtype == None else self._dtype,
-            shape=beta_shape,
-            lod_level=0,
-            persistable=True)
-        self.helper.set_variable_initializer(
-            self._beta1_pow_acc, initializer=Constant(self._beta1))
-
         # Create accumulator tensors for first moment and infinity norm
         for p in parameters:
             self._add_accumulator(self._moment_acc_str, p)
             self._add_accumulator(self._inf_norm_acc_str, p)
+            self._add_accumulator(
+                name=self._beta1_pow_acc_str,
+                param=p,
+                dtype='float32',
+                fill_value=self._beta1,
+                shape=[1])
 
     def _append_optimize_op(self, block, param_and_grad):
         assert isinstance(block, framework.Block)
@@ -667,6 +665,8 @@ class AdamaxOptimizer(Optimizer):
         moment = self._get_accumulator(self._moment_acc_str, param_and_grad[0])
         inf_norm = self._get_accumulator(self._inf_norm_acc_str,
                                          param_and_grad[0])
+        beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
+                                              param_and_grad[0])
         # create the adamax optimize op
         adamax_op = block.append_op(
             type=self.type,
@@ -676,7 +676,7 @@ class AdamaxOptimizer(Optimizer):
                 "LearningRate": self._create_param_lr(param_and_grad),
                 "Moment": moment,
                 "InfNorm": inf_norm,
-                "Beta1Pow": self._beta1_pow_acc
+                "Beta1Pow": beta1_pow_acc
             },
             outputs={
                 "ParamOut": param_and_grad[0],
@@ -691,18 +691,20 @@ class AdamaxOptimizer(Optimizer):
 
         return adamax_op
 
-    def _finish_update(self, block):
+    def _finish_update(self, block, parameters):
         """Update Beta1 Power accumulator
         """
         assert isinstance(block, framework.Block)
         main_block = block.program.global_block()
-        scale_beta1 = main_block.append_op(
-            type="scale",
-            inputs={"X": self._beta1_pow_acc},
-            outputs={"Out": self._beta1_pow_acc},
-            attrs={"scale": self._beta1})
-
-        return [scale_beta1]
+        for param in parameters:
+            with param.block.program.optimized_guard(param):
+                beta1_pow_acc = self._get_accumulator(self._beta1_pow_acc_str,
+                                                      param)
+                main_block.append_op(
+                    type="scale",
+                    inputs={"X": beta1_pow_acc},
+                    outputs={"Out": beta1_pow_acc},
+                    attrs={"scale": self._beta1})
 
 
 class DecayedAdagradOptimizer(Optimizer):
@@ -1156,7 +1158,8 @@ class ModelAverage(Optimizer):
                 self.params_grads.append((param, grad))
 
         for param, grad in self.params_grads:
-            self._append_average_accumulate_op(param)
+            with param.block.program.optimized_guard(param):
+                self._append_average_accumulate_op(param)
 
         self.apply_program = Program()
         block = self.apply_program.global_block()

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

-#   Copyright (c) 2018 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 paddle.fluid as fluid
-import unittest
-import os
-import tempfile
-
-
-class TestCheckpoint(unittest.TestCase):
-    def setUp(self):
-        self.dirname = tempfile.mktemp()
-        self.max_num_checkpoints = 3
-        self.epoch_interval = 1
-        self.step_interval = 1
-        self.trainer_id = 0
-        self.chief = self.trainer_id == 0
-        self.place = fluid.CPUPlace()
-        self.epoch_id = 100
-        self.step_id = 20
-
-    def test_checkpoint(self):
-        self.save_checkpoint()
-        serial = fluid.io.get_latest_checkpoint_serial(self.dirname)
-        self.assertTrue(serial >= 0)
-        trainer_args = ["epoch_id", "step_id"]
-        epoch_id, step_id = fluid.io.load_trainer_args(
-            self.dirname, serial, self.trainer_id, trainer_args)
-        self.assertEqual(self.step_id, int(step_id))
-        self.assertEqual(self.epoch_id, int(epoch_id))
-
-        program = fluid.Program()
-        with fluid.program_guard(program):
-            exe = fluid.Executor(self.place)
-            fluid.io.load_checkpoint(exe, self.dirname, serial, program)
-
-        fluid.io.clean_checkpoint(self.dirname, delete_dir=True)
-        self.assertFalse(os.path.isdir(self.dirname))
-
-    def save_checkpoint(self):
-        config = fluid.CheckpointConfig(self.dirname, self.max_num_checkpoints,
-                                        self.epoch_interval, self.step_interval)
-
-        trainer_args = {}
-        trainer_args["epoch_id"] = self.epoch_id
-        trainer_args["step_id"] = self.step_id
-
-        program = fluid.Program()
-        with fluid.program_guard(program):
-            program.global_block().create_var(
-                name="scale_0",
-                psersistable=True,
-                dtype="float32",
-                shape=[32, 32])
-
-            exe = fluid.Executor(self.place)
-            for i in xrange(10):
-                fluid.io.save_checkpoint(exe, config.checkpoint_dir,
-                                         self.trainer_id, trainer_args, program,
-                                         config.max_num_checkpoints)
-
-
-if __name__ == '__main__':
-    unittest.main()

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

+#   Copyright (c) 2018 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 math
+from op_test import OpTest
+
+
+def find_latest_set(num):
+    return 1 + int(math.floor(math.log(num, 2)))
+
+
+class CodeTable(object):
+    def __init__(self, num_classes, code):
+        self.c = num_classes + code
+
+    def cal_index(self, bit):
+        return (self.c >> (bit + 1)) - 1
+
+    def get_length(self):
+        return find_latest_set(self.c) - 1
+
+    def cal_bit(self, bit):
+        return self.c & (1 << bit)
+
+
+def hsigmoid(x, w, label, bias, num_classes):
+    batch_size = x.shape[0]
+    code_length = find_latest_set(num_classes - 1)
+    code_table = [0 for _ in range(code_length)]
+    pre_output = np.zeros((batch_size, code_length))
+    pre_sum = np.zeros((batch_size, 1))
+    out = np.zeros((batch_size, 1)).astype("float32")
+    for i in range(batch_size):
+        code_table = CodeTable(num_classes, label[i])
+        length = code_table.get_length()
+        for j in range(length):
+            idx = code_table.cal_index(j)
+            pre_output[i][j] += bias[0][idx]
+    for i in range(batch_size):
+        code_table = CodeTable(num_classes, label[i])
+        length = code_table.get_length()
+        for j in range(length):
+            idx = code_table.cal_index(j)
+            pre_output[i][j] += np.dot(w[idx], x[i])
+    # clip[-40.0, 40.0]
+    pre_output = np.clip(pre_output, -40.0, 40.0)
+    # out(i, 0) = \sum_j  bit(i, j) * preout(i, j)
+    for i in range(batch_size):
+        code_table = CodeTable(num_classes, label[i])
+        length = code_table.get_length()
+        sum = 0.0
+        for j in range(length):
+            if code_table.cal_bit(j):
+                sum += pre_output[i][j]
+        out[i] = -1.0 * sum
+    # soft relu
+    pre_output = np.log(1 + np.exp(pre_output))
+    pre_sum = pre_output.sum(1).reshape((batch_size, 1))
+    out += pre_sum
+    return pre_output, out
+
+
+class TestHSigmoidOp(OpTest):
+    def setUp(self):
+        self.op_type = "hierarchical_sigmoid"
+        num_classes = 6
+        feature_size = 8
+        batch_size = 4
+        x = np.random.random((batch_size, feature_size)).astype("float32")
+        w = np.random.random((num_classes - 1, feature_size)).astype("float32")
+        label = np.random.randint(0, num_classes, (batch_size, 1))
+        bias = np.random.random((1, num_classes - 1)).astype("float32")
+        self.attrs = {'num_classes': num_classes}
+        self.inputs = {'X': x, 'W': w, 'Label': label, 'Bias': bias}
+        pre_output, out = hsigmoid(x, w, label, bias, num_classes)
+        self.outputs = {'PreOut': pre_output, 'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['Bias', 'X', 'W'], ['Out'], no_grad_set=set('Label'))
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -174,6 +174,16 @@ class TestBook(unittest.TestCase):
                     x=dat, label=lbl))
         print(str(program))
 
+    def test_hsigmoid(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[2], dtype='float32')
+            y = layers.data(name='y', shape=[2], dtype='int64')
+            self.assertIsNotNone(
+                layers.hsigmoid(
+                    input=x, label=y, num_classes=2))
+        print(str(program))
+
     def test_sequence_expand(self):
         program = Program()
         with program_guard(program):

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

@@ -287,7 +287,7 @@ class TestAdamOptimizer(unittest.TestCase):
 
         # Check accumulators
         accumulators = adam_optimizer.get_accumulators()
-        self.assertEqual(len(accumulators), 2)
+        self.assertEqual(len(accumulators), 4)
         self.assertTrue(adam_optimizer.get_moment1_str() in accumulators)
         self.assertTrue(adam_optimizer.get_moment2_str() in accumulators)
         moment1_acc = accumulators[adam_optimizer.get_moment1_str()]
@@ -354,7 +354,7 @@ class TestAdamaxOptimizer(unittest.TestCase):
 
         # Check accumulators
         accumulators = adamax_optimizer.get_accumulators()
-        self.assertEqual(len(accumulators), 2)
+        self.assertEqual(len(accumulators), 3)
         self.assertTrue(adamax_optimizer.get_moment_str() in accumulators)
         self.assertTrue(adamax_optimizer.get_inf_norm_str() in accumulators)
         moment_acc = accumulators[adamax_optimizer.get_moment_str()]

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

+# Copyright (c) 2018 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 paddle.fluid as fluid
+import numpy as np
+from threading import Thread
+
+
+def feed_data(feed_queue, inputs):
+    for in_data in inputs:
+        feed_queue.push(in_data)
+
+
+class TestPyReader(unittest.TestCase):
+    def setUp(self):
+        self.capacity = 10
+        self.batch_size_min = 10
+        self.batch_size_max = 20
+        self.shapes = [(-1, 3, 2, 1), (-1, 1)]
+        self.lod_levels = [0, 0]
+        self.dtypes = ['float32', 'int64']
+        self.iterations = 20
+
+    def test_single_thread_main(self):
+        self.main(use_thread=False)
+
+    def test_multiple_thread_main(self):
+        self.main(use_thread=True)
+
+    def main(self, use_thread=False):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+            ) else fluid.CPUPlace()
+            executor = fluid.Executor(place)
+
+            data_file, feed_queue = fluid.layers.py_reader(
+                capacity=self.capacity,
+                dtypes=self.dtypes,
+                lod_levels=self.lod_levels,
+                shapes=self.shapes)
+
+            read_out_data = fluid.layers.read_file(data_file)
+            self.inputs = []
+
+            for i in range(self.iterations):
+                in_data = fluid.LoDTensorArray()
+                batch_size = np.random.random_integers(self.batch_size_min,
+                                                       self.batch_size_max)
+                for shape, dtype in zip(self.shapes, self.dtypes):
+                    next_data = np.random.uniform(
+                        low=0, high=1000,
+                        size=(batch_size, ) + shape[1:]).astype(dtype)
+                    in_data.append(executor.as_lodtensor(next_data))
+
+                self.inputs.append(in_data)
+
+            executor.run(fluid.default_startup_program())
+            self.outputs = []
+            if use_thread:
+                thread = Thread(
+                    target=feed_data, args=(feed_queue, self.inputs))
+                thread.start()
+                for in_data in self.inputs:
+                    self.outputs.append(
+                        executor.run(fetch_list=list(read_out_data)))
+            else:
+                for in_data in self.inputs:
+                    feed_queue.push(in_data)
+                    self.outputs.append(
+                        executor.run(fetch_list=list(read_out_data)))
+
+            feed_queue.close()
+            self.validate()
+
+    def validate(self):
+        self.assertEqual(len(self.inputs), len(self.outputs))
+        for in_data_list, out_data_list in zip(self.inputs, self.outputs):
+            self.assertEqual(len(in_data_list), len(out_data_list))
+            in_data_list_np = [
+                np.array(in_lod_tensor) for in_lod_tensor in in_data_list
+            ]
+            for in_data, out_data in zip(in_data_list_np, out_data_list):
+                self.assertTrue((in_data == out_data).all())
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+# Copyright (c) 2018 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 paddle.fluid as fluid
+import paddle.fluid.core as core
+import numpy as np
+import threading
+import multiprocessing
+import os
+
+
+def as_tensor(np_array_or_tensor, place=None):
+    if isinstance(np_array_or_tensor, fluid.LoDTensor):
+        return np_array_or_tensor
+
+    if place is None:
+        place = fluid.CPUPlace()
+
+    tensor = fluid.LoDTensor()
+    tensor.set(np_array_or_tensor, place)
+    return tensor
+
+
+def as_numpy(tensor_or_numpy):
+    return tensor_or_numpy if isinstance(
+        tensor_or_numpy, np.ndarray) else np.array(tensor_or_numpy)
+
+
+def feed_data(feed_queue, reader):
+    data_generator = reader()
+    while True:
+        data = next(data_generator, None)
+        if data is None or not feed_queue.push(data):
+            break
+
+
+def simple_fc_net(in_size,
+                  class_num,
+                  hidden_sizes,
+                  batch_size,
+                  queue_capacity,
+                  use_double_buffer=False):
+    reader, feed_queue = fluid.layers.py_reader(
+        capacity=queue_capacity,
+        shapes=[[-1, in_size], [-1, 1]],
+        lod_levels=[0, 0],
+        dtypes=['float32', 'int64'])
+    reader = fluid.layers.batch(reader, batch_size=batch_size)
+    if use_double_buffer:
+        reader = fluid.layers.double_buffer(reader)
+
+    in_data, label = fluid.layers.read_file(reader)
+
+    hidden = in_data
+    for hidden_size in hidden_sizes:
+        hidden = fluid.layers.fc(
+            hidden,
+            size=hidden_size,
+            act='tanh',
+            bias_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=1.0)))
+
+    predict_label = fluid.layers.fc(hidden, size=class_num, act='softmax')
+    loss = fluid.layers.mean(
+        fluid.layers.cross_entropy(
+            input=predict_label, label=label))
+
+    optimizer = fluid.optimizer.Adam()
+    optimizer.minimize(loss)
+    return in_data, label, loss, optimizer, feed_queue
+
+
+class TestPyReaderUsingExecutor(unittest.TestCase):
+    def setUp(self):
+        self.in_size = 1000
+        self.hidden_sizes = [50, 30, 20]
+        self.class_num = 10
+        self.batch_size = 32
+        self.iterations = 10
+        self.queue_capacity = 50
+
+    def test(self):
+        for use_cuda in [False, True]:
+            for use_parallel_executor in [False, True]:
+                for use_double_buffer in [False, True]:
+                    print('Test Parameters:'),
+                    print({
+                        'use_cuda': use_cuda,
+                        'use_parallel_executor': use_parallel_executor,
+                        'use_double_buffer': use_double_buffer
+                    })
+                    self.main(use_cuda, use_parallel_executor,
+                              use_double_buffer)
+
+    def random_reader(self):
+        def reader():
+            self.inputs = []
+            cnt = 0
+            while True:
+                tensors = fluid.LoDTensorArray()
+                in_data = np.random.uniform(
+                    low=0, high=1, size=(1, self.in_size)).astype('float32')
+                tensors.append(as_tensor(in_data))
+                label = np.random.random_integers(
+                    low=0, high=self.class_num - 1, size=(1, 1)).astype('int64')
+                tensors.append(as_tensor(label))
+
+                if cnt < self.iterations * self.batch_size * self.batch_size_times:
+                    if cnt % (self.batch_size * self.batch_size_times) == 0:
+                        self.inputs.append([in_data, label])
+                    else:
+                        self.inputs[-1][0] = np.concatenate(
+                            (self.inputs[-1][0], in_data), axis=0)
+                        self.inputs[-1][1] = np.concatenate(
+                            (self.inputs[-1][1], label), axis=0)
+                elif not self.use_double_buffer:
+                    break
+
+                yield tensors
+                cnt += 1
+
+            yield None
+
+        return reader
+
+    def main(self,
+             use_cuda=True,
+             use_parallel_executor=False,
+             use_double_buffer=False):
+        assert not use_cuda or use_cuda and core.is_compiled_with_cuda()
+
+        self.use_cuda = use_cuda
+        self.use_parallel_executor = use_parallel_executor
+        self.use_double_buffer = use_double_buffer
+
+        startup_program = fluid.Program()
+        main_program = fluid.Program()
+
+        with fluid.program_guard(main_program, startup_program):
+            in_data, label, loss, optimizer, feed_queue = simple_fc_net(
+                in_size=self.in_size,
+                class_num=self.class_num,
+                hidden_sizes=self.hidden_sizes,
+                batch_size=self.batch_size,
+                queue_capacity=self.queue_capacity,
+                use_double_buffer=self.use_double_buffer)
+
+            place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+            startup_exe = fluid.Executor(place)
+            startup_exe.run(startup_program)
+
+            if use_parallel_executor:
+                main_exe = fluid.ParallelExecutor(use_cuda, loss_name=loss.name)
+                if use_cuda:
+                    self.batch_size_times = core.get_cuda_device_count()
+                else:
+                    self.batch_size_times = int(
+                        os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+            else:
+                main_exe = startup_exe
+                self.batch_size_times = 1
+
+            reader = self.random_reader()
+            thread = threading.Thread(
+                target=feed_data, args=(feed_queue, reader))
+            thread.start()
+
+            self.outputs = []
+            for _ in range(self.iterations):
+                fetches = main_exe.run(fetch_list=[in_data.name, label.name])
+                fetches = [as_numpy(fetch) for fetch in fetches]
+                self.outputs.append(fetches)
+
+            feed_queue.close()
+            self.validate()
+
+    def validate(self):
+        self.assertEqual(len(self.inputs), len(self.outputs))
+        for batch_in, batch_out in zip(self.inputs, self.outputs):
+            self.assertEqual(len(batch_in), len(batch_out))
+            if self.use_parallel_executor and not self.use_double_buffer:
+                self.validate_unordered_batch(batch_in, batch_out)
+            else:
+                for in_data, out_data in zip(batch_in, batch_out):
+                    self.assertEqual(in_data.shape, out_data.shape)
+                    if not self.use_parallel_executor:
+                        self.assertTrue((in_data == out_data).all())
+
+    def validate_unordered_batch(self, batch_in, batch_out):
+        out_index_left_set = set(range(self.batch_size * self.batch_size_times))
+        mapping_num = 0
+        for i in range(self.batch_size * self.batch_size_times):
+            for j in out_index_left_set:
+                flag = True
+                for k in range(len(batch_in)):
+                    in_data = batch_in[k][i]
+                    out_data = batch_out[k][j]
+                    if (in_data != out_data).any():
+                        flag = False
+                        break
+
+                if flag:
+                    out_index_left_set.remove(j)
+                    mapping_num += 1
+                    break
+
+        self.assertEqual(mapping_num, self.batch_size * self.batch_size_times)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+# Copyright (c) 2018 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 paddle.fluid as fluid
+import paddle.v2 as paddle
+import numpy as np
+import unittest
+
+
+class TestReaderReset(unittest.TestCase):
+    def prepare_data(self):
+        def fake_data_generator():
+            for n in xrange(self.total_ins_num):
+                yield np.ones(self.ins_shape) * n, n
+
+        # Prepare data
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            reader = paddle.batch(fake_data_generator, batch_size=1)
+            feeder = fluid.DataFeeder(
+                feed_list=[
+                    fluid.layers.data(
+                        name='data', shape=[3], dtype='float32'),
+                    fluid.layers.data(
+                        name='label', shape=[1], dtype='int64'),
+                ],
+                place=fluid.CPUPlace())
+            fluid.recordio_writer.convert_reader_to_recordio_file(
+                self.data_file_name, reader, feeder)
+
+    def setUp(self):
+        self.use_cuda = fluid.core.is_compiled_with_cuda()
+        self.data_file_name = './reader_reset_test.recordio'
+        self.ins_shape = [3]
+        self.batch_size = 5
+        self.total_ins_num = self.batch_size * 20
+        self.test_pass_num = 100
+        self.prepare_data()
+
+    def main(self, with_double_buffer):
+        main_prog = fluid.Program()
+        startup_prog = fluid.Program()
+
+        with fluid.program_guard(main_prog, startup_prog):
+            data_reader_handle = fluid.layers.io.open_files(
+                filenames=[self.data_file_name],
+                shapes=[[-1] + self.ins_shape, [-1, 1]],
+                lod_levels=[0, 0],
+                dtypes=['float32', 'int64'],
+                thread_num=1,
+                pass_num=1)
+            data_reader = fluid.layers.io.batch(data_reader_handle,
+                                                self.batch_size)
+            if with_double_buffer:
+                data_reader = fluid.layers.double_buffer(data_reader)
+            image, label = fluid.layers.read_file(data_reader)
+            fetch_list = [image.name, label.name]
+
+        place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_prog)
+
+        build_strategy = fluid.BuildStrategy()
+        if with_double_buffer:
+            build_strategy.enable_data_balance = True
+        exec_strategy = fluid.ExecutionStrategy()
+        parallel_exe = fluid.ParallelExecutor(
+            use_cuda=self.use_cuda,
+            main_program=main_prog,
+            build_strategy=build_strategy,
+            exec_strategy=exec_strategy)
+
+        data_appeared = [False] * self.total_ins_num
+        pass_count = 0
+        while (True):
+            try:
+                data_val, label_val = parallel_exe.run(fetch_list,
+                                                       return_numpy=True)
+                ins_num = data_val.shape[0]
+                broadcasted_label = np.ones((ins_num, ) + tuple(
+                    self.ins_shape)) * label_val.reshape((ins_num, 1))
+                self.assertEqual(data_val.all(), broadcasted_label.all())
+                for l in label_val:
+                    self.assertFalse(data_appeared[l[0]])
+                    data_appeared[l[0]] = True
+
+            except fluid.core.EOFException:
+                pass_count += 1
+                if with_double_buffer:
+                    data_appeared = data_appeared[:-parallel_exe.device_count *
+                                                  self.batch_size]
+                for i in data_appeared:
+                    self.assertTrue(i)
+                if pass_count < self.test_pass_num:
+                    data_appeared = [False] * self.total_ins_num
+                    data_reader_handle.reset()
+                else:
+                    break
+
+    def test_all(self):
+        self.main(with_double_buffer=False)
+        self.main(with_double_buffer=True)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+#   Copyright (c) 2018 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
+
+from op_test import OpTest
+
+
+# Correct: General.
+class TestSqueezeOp(OpTest):
+    def setUp(self):
+        self.op_type = "squeeze"
+        self.init_test_case()
+        self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
+        self.init_attrs()
+        self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Out")
+
+    def init_test_case(self):
+        self.ori_shape = (1, 3, 1, 5)
+        self.axes = (0, 2)
+        self.new_shape = (3, 5)
+
+    def init_attrs(self):
+        self.attrs = {"axes": self.axes, "inplace": False}
+
+
+# Correct: There is mins axis.
+class TestSqueezeOp1(TestSqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (1, 3, 1, 5)
+        self.axes = (0, -2)
+        self.new_shape = (3, 5)
+
+
+# Correct: No axes input.
+class TestSqueezeOp2(TestSqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (1, 3, 1, 5)
+        self.axes = ()
+        self.new_shape = (3, 5)
+
+
+# Correct: Just part of axes be squeezed. 
+class TestSqueezeOp3(TestSqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (3, 1, 5, 1, 4, 1)
+        self.axes = (1, -1)
+        self.new_shape = (3, 5, 1, 4)
+
+
+# Correct: Inplace.
+class TestSqueezeOpInplace1(TestSqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (1, 3, 1, 5)
+        self.axes = (0, 2)
+        self.new_shape = (3, 5)
+
+    def init_attrs(self):
+        self.attrs = {"axes": self.axes, "inplace": True}
+
+
+# Correct: Inplace. There is mins axis.
+class TestSqueezeOpInplace2(TestSqueezeOp):
+    def inti_test_case(self):
+        self.ori_shape = (1, 3, 1, 5)
+        self.axes = (0, -2)
+        self.new_shape = (3, 5)
+
+    def init_attrs(self):
+        self.attrs = {"axes": self.axes, "inplace": True}
+
+
+# Correct: Inplace. No axes input.
+class TestSqueezeOpInplace3(TestSqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (1, 3, 1, 5)
+        self.axes = ()
+        self.new_shape = (3, 5)
+
+    def init_attrs(self):
+        self.attrs = {"axes": self.axes, "inplace": True}
+
+
+# Correct: Inpalce. Just part of axes be squeezed. 
+class TestSqueezeOpInplace4(TestSqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (3, 1, 5, 1, 4, 1)
+        self.axes = (1, -1)
+        self.new_shape = (3, 5, 1, 4)
+
+    def init_attrs(self):
+        self.attrs = {"axes": self.axes, "inplace": True}
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+# Copyright (c) 2018 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
+
+from op_test import OpTest
+
+
+# Correct: General.
+class TestUnsqueezeOp(OpTest):
+    def setUp(self):
+        self.init_test_case()
+        self.op_type = "unsqueeze"
+        self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
+        self.init_attrs()
+        self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(["X"], "Out")
+
+    def init_test_case(self):
+        self.ori_shape = (3, 5)
+        self.axes = (1, 2)
+        self.new_shape = (3, 1, 1, 5)
+
+    def init_attrs(self):
+        self.attrs = {"axes": self.axes, "inplace": False}
+
+
+# Correct: Single input index.
+class TestUnsqueezeOp1(TestUnsqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (3, 5)
+        self.axes = (-1, )
+        self.new_shape = (3, 5, 1)
+
+
+# Correct: Mixed input axis.
+class TestUnsqueezeOp2(TestUnsqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (3, 5)
+        self.axes = (0, -1)
+        self.new_shape = (1, 3, 5, 1)
+
+
+# Correct: There is duplicated axis.
+class TestUnsqueezeOp3(TestUnsqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (3, 2, 5)
+        self.axes = (0, 3, 3)
+        self.new_shape = (1, 3, 2, 1, 1, 5)
+
+
+# Correct: Reversed axes.
+class TestUnsqueezeOp4(TestUnsqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (3, 2, 5)
+        self.axes = (3, 1, 1)
+        self.new_shape = (3, 1, 1, 2, 5, 1)
+
+
+# Correct: Inplace.
+class TestUnsqueezeOpInplace1(TestUnsqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (3, 5)
+        self.axes = (0, 2)
+        self.new_shape = (1, 3, 1, 5)
+
+    def init_attrs(self):
+        self.attrs = {"axes": self.axes, "inplace": True}
+
+
+# Correct: Inplace. There is mins index.
+class TestUnsqueezeOpInplace2(TestUnsqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (3, 5)
+        self.axes = (0, -2)
+        self.new_shape = (1, 3, 1, 5)
+
+    def init_attrs(self):
+        self.attrs = {"axes": self.axes, "inplace": True}
+
+
+# Correct: Inplace. There is duplicated axis.
+class TestUnsqueezeOpInplace3(TestUnsqueezeOp):
+    def init_test_case(self):
+        self.ori_shape = (3, 2, 5)
+        self.axes = (0, 3, 3)
+        self.new_shape = (1, 3, 2, 1, 1, 5)
+
+    def init_attrs(self):
+        self.attrs = {"axes": self.axes, "inplace": True}
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -377,11 +377,6 @@ class DistributeTranspiler(object):
         # append it into the sub program.
 
         global_ops = []
-        # HACK: optimization global ops only used to scale beta1 and beta2
-        # replace it with dependency engine.
-        for op in self.optimize_ops:
-            if self._is_adam_connected_op(op):
-                global_ops.append(op)
 
         def __append_optimize_op__(op, block, grad_to_block_id, merged_var,
                                    lr_ops):
@@ -1289,22 +1284,16 @@ class DistributeTranspiler(object):
         # If one op's input is another op's output or
         # one op's output is another op's input, we say
         # the two operator is connected.
-        def _append_inname_remove_beta(varname_list):
+        def _append_inname(varname_list):
             op_input_names = []
             for in_name in varname_list:
-                # HACK: remove beta1 and beta2 to avoid let all
-                # ops connected.
-                if in_name.startswith("beta2_pow_acc") or \
-                    in_name.startswith("beta1_pow_acc"):
-                    continue
-                else:
-                    op_input_names.append(in_name)
+                op_input_names.append(in_name)
             return op_input_names
 
-        op1_input_names = _append_inname_remove_beta(op1.desc.input_arg_names())
+        op1_input_names = _append_inname(op1.desc.input_arg_names())
         op1_output_names = op1.desc.output_arg_names()
 
-        op2_input_names = _append_inname_remove_beta(op2.desc.input_arg_names())
+        op2_input_names = _append_inname(op2.desc.input_arg_names())
         op2_output_names = op2.desc.output_arg_names()
 
         if set(op1_output_names) & set(op2_input_names) or \
@@ -1413,7 +1402,7 @@ class DistributeTranspiler(object):
 
     def _get_optimize_pass(self):
         """
-        Get optimizer operators, paramters and gradients from origin_program
+        Get optimizer operators, parameters and gradients from origin_program
         Returns:
             opt_ops (list): optimize operators.
             params_grads (dict): paramter->gradient.
@@ -1436,20 +1425,6 @@ class DistributeTranspiler(object):
                             origin_var_dict[param_name],
                             origin_var_dict[input_name]
                         ])
-            elif self._is_adam_connected_op(op):
-                opt_ops.append(op)
             else:
                 pass
         return opt_ops, params_grads
-
-    def _is_adam_connected_op(self, op):
-        """
-        A hack function to determinate whether the input operator
-        is connected to optimize operator.
-        """
-        if op.type == "scale":
-            for in_name in op.input_arg_names:
-                if in_name.startswith("beta1_pow_acc") or \
-                        in_name.startswith("beta2_pow_acc"):
-                    return True
-        return False

python/setup.py.in

@@ -42,12 +42,12 @@ def get_patch():
 
 def is_taged():
     try:
-        cmd = ['git', 'describe', '--exact-match', '--tags']
+        cmd = ['git', 'describe', '--exact-match', '--tags', 'HEAD', '2>/dev/null']
         git_tag = subprocess.Popen(cmd, stdout = subprocess.PIPE).communicate()[0].strip()
     except:
         return False
 
-    if git_tag.replace('v', '') == '@PADDLE_VERSION@':
+    if str(git_tag).replace('v', '') == '@PADDLE_VERSION@':
         return True
     else:
         return False