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

AUTHORS.md

@@ -4,6 +4,7 @@
 | backyes | Yan-Fei Wang |
 | baiyfbupt | Yi-Fan Bai |
 | beckett1124 | Bin Qi |
+| ChengduoZH | Cheng-Duo Zhao|
 | chengxiaohua1105 | Xiao-Hua Cheng |
 | cxwangyi, yiwangbaidu, wangkuiyi | Yi Wang |
 | cxysteven | Xing-Yi Cheng |

doc/fluid/howto/index_cn.rst

@@ -4,5 +4,5 @@
 .. toctree::
   :maxdepth: 1
 
+  inference/index_cn.rst
   optimization/index_cn.rst
-  inference/inference_support_in_fluid.md

doc/fluid/howto/index_en.rst

@@ -5,4 +5,3 @@ HOW TO
   :maxdepth: 1
 
   optimization/index_en.rst
-  inference/inference_support_in_fluid.md

doc/fluid/howto/inference/build_and_install_lib_cn.rst

+安装与编译C++预测库
+===========================
+
+直接下载安装
+-------------
+
+======================   ========================================
+版本说明                            C++预测库   
+======================   ========================================
+cpu_avx_mkl              `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/fluid.tgz>`_ 
+cpu_avx_openblas         `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/fluid.tgz>`_
+cpu_noavx_openblas       `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/fluid.tgz>`_
+cuda7.5_cudnn5_avx_mkl   `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
+cuda8.0_cudnn5_avx_mkl   `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/fluid.tgz>`_
+cuda8.0_cudnn7_avx_mkl   `fluid.tgz <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/fluid.tgz>`_
+======================   ========================================
+
+从源码编译
+----------
+用户也可以从 PaddlePaddle 核心代码编译C++预测库，只需在编译时配制下面这些编译选项：
+
+=================   =========
+选项                 值   
+=================   =========
+CMAKE_BUILD_TYPE    Release
+FLUID_INSTALL_DIR   安装路径    
+WITH_FLUID_ONLY     ON（推荐）
+WITH_SWIG_PY        OFF（推荐
+WITH_PYTHON         OFF（推荐）
+WITH_GPU            ON/OFF
+WITH_MKL            ON/OFF
+=================   =========
+
+建议按照推荐值设置，以避免链接不必要的库。其它可选编译选项按需进行设定。
+
+下面的代码片段从github拉取最新代码，配制编译选项（需要将PADDLE_ROOT替换为PaddlePaddle预测库的安装路径）：
+
+  .. code-block:: bash
+
+     pip install paddlepaddle-gpu
+     PADDLE_ROOT=/path/of/capi
+     git clone https://github.com/PaddlePaddle/Paddle.git
+     cd Paddle
+     mkdir build
+     cd build
+     cmake -DFLUID_INSTALL_DIR=$PADDLE_ROOT \
+           -DCMAKE_BUILD_TYPE=Release \
+           -DWITH_FLUID_ONLY=ON \
+           -DWITH_SWIG_PY=OFF \
+           -DWITH_PYTHON=OFF \
+           -DWITH_MKL=OFF \
+           -DWITH_GPU=OFF  \
+           ..
+      make
+      make inference_lib_dist
+
+成功编译后，使用C++预测库所需的依赖（包括：（1）编译出的PaddlePaddle预测库和头文件；（2）第三方链接库和头文件；（3）版本信息与编译选项信息）
+均会存放于PADDLE_ROOT目录中。目录结构如下：
+
+  .. code-block:: text
+
+     PaddleRoot/
+     ├── CMakeCache.txt
+     ├── paddle
+     │   └── fluid
+     │       ├── framework
+     │       ├── inference
+     │       ├── memory
+     │       ├── platform
+     │       ├── pybind
+     │       └── string
+     ├── third_party
+     │   ├── boost
+     │   │   └── boost
+     │   ├── eigen3
+     │   │   ├── Eigen
+     │   │   └── unsupported
+     │   └── install
+     │       ├── gflags
+     │       ├── glog
+     │       ├── mklml
+     │       ├── protobuf
+     │       ├── snappy
+     │       ├── snappystream
+     │       └── zlib
+     └── version.txt
+     
+version.txt 中记录了该预测库的版本信息，包括Git Commit ID、使用OpenBlas或MKL数学库、CUDA/CUDNN版本号，如：
+
+  .. code-block:: text
+
+     GIT COMMIT ID: c95cd4742f02bb009e651a00b07b21c979637dc8
+     WITH_MKL: ON
+     WITH_GPU: ON
+     CUDA version: 8.0
+     CUDNN version: v5

doc/fluid/howto/inference/index_cn.rst

+预测库
+------------
+
+.. toctree::
+  :maxdepth: 1
+
+  build_and_install_lib_cn.rst
+  inference_support_in_fluid_cn.md

doc/fluid/howto/inference/inference_support_in_fluid.md → doc/fluid/howto/inference/inference_support_in_fluid_cn.md

-# Fluid Inference使用指南
+# 使用指南
 
 ## 目录：
 
 - Python Inference API
-- 编译Fluid Inference库
 - Inference C++ API
 - Inference实例
 - Inference计算优化
@@ -55,62 +54,6 @@
     return [program, feed_target_names, fetch_targets]
   ```
 
-
-## 编译Fluid Inference库
-
-  - **不需要额外的CMake选项**
-    - 1、 配置CMake命令，更多配置请参考[源码编译PaddlePaddle](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/build_from_source_cn.html)
-      ```bash
-      $ git clone https://github.com/PaddlePaddle/Paddle.git
-      $ cd Paddle
-      $ mkdir build
-      $ cd build
-      $ cmake -DCMAKE_INSTALL_PREFIX=your/path/to/paddle_inference_lib \
-          -DCMAKE_BUILD_TYPE=Release \
-          -DWITH_PYTHON=ON \
-          -DWITH_MKL=OFF \
-          -DWITH_GPU=OFF \
-          ..
-      ```
-
-    - 2、 编译PaddlePaddle
-      ```bash
-      $ make
-      ```
-
-    - 3、 部署。执行如下命令将PaddlePaddle Fluid Inference库部署到`your/path/to/paddle_inference_lib`目录。
-      ```bash
-      $ make inference_lib_dist
-      ```
-
-- 目录结构
-
-  ```bash
-  $ cd your/path/to/paddle_inference_lib
-  $ tree
-  .
-  |-- paddle
-  |   `-- fluid
-  |       |-- framework
-  |       |-- inference
-  |       |   |-- io.h
-  |       |   `-- libpaddle_fluid.so
-  |       |-- memory
-  |       |-- platform
-  |       `-- string
-  |-- third_party
-  |   |-- eigen3
-  |   `-- install
-  |       |-- gflags
-  |       |-- glog
-  |       `-- protobuf
-  `-- ...
-  ```
-
-  假设`PADDLE_ROOT=your/path/to/paddle_inference_lib`。
-
-
-
 ## 链接Fluid Inference库
 - 示例项目([链接](https://github.com/luotao1/fluid_inference_example.git))
 

paddle/contrib/inference/CMakeLists.txt

@@ -17,32 +17,21 @@ if(APPLE)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
 endif(APPLE)
 
-function(inference_api_test TARGET_NAME TEST_SRC)
+function(inference_api_test TARGET_NAME)
     set(options "")
     set(oneValueArgs "")
     set(multiValueArgs ARGS)
     cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
     set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
-    set(arg_list "")
+    cc_test(test_paddle_inference_${TARGET_NAME}
+            SRCS test_paddle_inference_${TARGET_NAME}.cc
+            DEPS paddle_fluid_api paddle_inference_api
+            ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
     if(inference_test_ARGS)
-        foreach(arg ${inference_test_ARGS})
-            list(APPEND arg_list "_${arg}")
-        endforeach()
-    else()
-        list(APPEND arg_list "_")
+        set_tests_properties(test_paddle_inference_${TARGET_NAME}
+                 PROPERTIES DEPENDS "${inference_test_ARGS}")
     endif()
-    foreach(arg ${arg_list})
-        string(REGEX REPLACE "^_$" "" arg "${arg}")
-        cc_test(${TARGET_NAME}
-                SRCS ${TEST_SRC}
-                DEPS paddle_fluid_api paddle_inference_api
-                ARGS --dirname=${PYTHON_TESTS_DIR}/book/)
-        # TODO(panyx0178): Figure out how to add word2vec and image_classification
-        # as deps.
-        # set_tests_properties(${TARGET_NAME}
-        #         PROPERTIES DEPENDS ${DEP_TEST})
-    endforeach()
 endfunction(inference_api_test)
 
 
@@ -50,9 +39,11 @@ cc_library(paddle_inference_api
     SRCS paddle_inference_api.cc paddle_inference_api_impl.cc
     DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
 
-cc_test(test_paddle_inference_api
-        SRCS test_paddle_inference_api.cc
-        DEPS paddle_inference_api)
+if(WITH_TESTING)
+    cc_test(test_paddle_inference_api
+            SRCS test_paddle_inference_api.cc
+            DEPS paddle_inference_api)
 
-inference_api_test(test_paddle_inference_api_impl
-                   test_paddle_inference_api_impl.cc)
+    inference_api_test(api_impl
+                       ARGS test_word2vec test_image_classification)
+endif()

paddle/fluid/framework/block_desc.cc

@@ -200,7 +200,7 @@ BlockDesc::BlockDesc(ProgramDesc *prog, proto::BlockDesc *desc)
     vars_[var_desc.name()].reset(new VarDesc(var_desc));
   }
   for (const proto::OpDesc &op_desc : desc_->ops()) {
-    ops_.emplace_back(new OpDesc(op_desc, prog, this));
+    ops_.emplace_back(new OpDesc(op_desc, this));
   }
 }
 
@@ -209,7 +209,7 @@ BlockDesc::BlockDesc(const BlockDesc &other, proto::BlockDesc *desc,
     : prog_(prog), desc_(desc) {
   need_update_ = true;
   for (auto &op : other.ops_) {
-    ops_.emplace_back(new OpDesc(*op->Proto(), prog, this));
+    ops_.emplace_back(new OpDesc(*op, this));
   }
   for (auto &it : other.vars_) {
     auto *var = new VarDesc(*it.second);

paddle/fluid/framework/block_desc.h

@@ -105,7 +105,7 @@ class BlockDesc {
 
   size_t OpSize() const { return ops_.size(); }
 
-  OpDesc *Op(int idx) { return ops_.at(idx).get(); }
+  OpDesc *Op(int idx) const { return ops_.at(idx).get(); }
 
   void Flush();
 

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -11,11 +11,15 @@
 // 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/framework/details/multi_devices_graph_builder.h"
+#include <algorithm>
 #include <fstream>
+#include <string>
 #include <utility>
+#include <vector>
+
 #include "paddle/fluid/framework/details/broadcast_op_handle.h"
 #include "paddle/fluid/framework/details/computation_op_handle.h"
+#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
 #include "paddle/fluid/framework/details/reduce_op_handle.h"
 #include "paddle/fluid/framework/details/rpc_op_handle.h"
 #include "paddle/fluid/framework/details/scale_loss_grad_op_handle.h"
@@ -26,9 +30,6 @@
 #include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
 #endif
 
-#include <string>
-#include <vector>
-
 DEFINE_string(ssa_graph_path, "/tmp/ssa_graph.dot",
               "the ssa graph path only print with GLOG_v=10,"
               "default /tmp/graph.dot");
@@ -148,9 +149,9 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(
 
 std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
     const ProgramDesc &program) const {
-  std::unordered_map<std::string, proto::VarType::Type> var_types;
+  std::unordered_map<std::string, VarDesc *> all_vars;
   for (auto *var : program.Block(0).AllVars()) {
-    var_types[var->Name()] = var->GetType();
+    all_vars[var->Name()] = var;
   }
 
   auto graph = new SSAGraph();
@@ -167,12 +168,28 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
   auto send_vars = FindDistTrainSendVars(program);
   auto recv_vars = FindDistTrainRecvVars(program);
 
-  size_t cur_device_id = 0;
   std::vector<std::unordered_set<std::string>> var_name_on_devices;
   std::vector<std::unordered_set<std::string>> bcast_var_name_set;
   var_name_on_devices.resize(places_.size());
   bcast_var_name_set.resize(places_.size());
 
+  size_t cur_device_id = 0;
+  std::vector<int64_t> balance_grads(places_.size(), 0);
+
+  auto get_appropriate_dev = [&](std::string &g_name) -> size_t {
+    auto var_desc = all_vars.at(g_name);
+    PADDLE_ENFORCE_NOT_NULL(var_desc);
+    auto dim = framework::make_ddim(var_desc->GetShape());
+    int64_t numel = framework::product(dim);
+    PADDLE_ENFORCE_GE(numel, 0);
+    auto smallest =
+        std::min_element(std::begin(balance_grads), std::end(balance_grads));
+    size_t dev_id =
+        static_cast<size_t>(std::distance(std::begin(balance_grads), smallest));
+    balance_grads[dev_id] += numel;
+    return dev_id;
+  };
+
   bool is_forwarding = true;
   for (auto *op : program.Block(0).AllOps()) {
     if (boost::get<int>(
@@ -220,13 +237,13 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
 
               switch (strategy_.reduce_) {
                 case BuildStrategy::ReduceStrategy::kReduce:
+                  cur_device_id = get_appropriate_dev(g_name);
                   CreateReduceOp(&result, g_name, cur_device_id);
                   var_name_on_devices[cur_device_id].emplace(g_name);
                   bcast_var_name_set[cur_device_id].emplace(p_name);
-                  cur_device_id = (cur_device_id + 1) % places_.size();
                   break;
                 case BuildStrategy::ReduceStrategy::kAllReduce:
-                  if (IsSparseGradient(var_types, g_name)) {
+                  if (IsSparseGradient(all_vars, g_name)) {
                     CreateReduceOp(&result, g_name, 0);
                     CreateBroadcastOp(&result, g_name, 0);
                   } else {
@@ -269,10 +286,10 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
 }
 
 bool MultiDevSSAGraphBuilder::IsSparseGradient(
-    const std::unordered_map<std::string, proto::VarType::Type> &var_types,
+    const std::unordered_map<std::string, VarDesc *> &all_vars,
     const std::string &og) const {
-  PADDLE_ENFORCE(var_types.count(og) != 0);
-  if (var_types.at(og) == proto::VarType::SELECTED_ROWS) {
+  PADDLE_ENFORCE(all_vars.count(og) != 0);
+  if (all_vars.at(og)->GetType() == proto::VarType::SELECTED_ROWS) {
     return true;
   }
   return false;

paddle/fluid/framework/details/multi_devices_graph_builder.h

@@ -106,7 +106,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
                          size_t src_dev_id) const;
 
   bool IsSparseGradient(
-      const std::unordered_map<std::string, proto::VarType::Type> &var_types,
+      const std::unordered_map<std::string, VarDesc *> &all_vars,
       const std::string &og) const;
 
  private:

paddle/fluid/framework/op_desc.cc

@@ -103,7 +103,7 @@ void OpDesc::CopyFrom(const OpDesc &op_desc) {
   need_update_ = true;
 }
 
-OpDesc::OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block)
+OpDesc::OpDesc(const proto::OpDesc &desc, BlockDesc *block)
     : desc_(desc), need_update_(false) {
   // restore inputs_
   int input_size = desc_.inputs_size();

paddle/fluid/framework/op_desc.h

@@ -33,13 +33,14 @@ class OpDesc {
   OpDesc(const std::string &type, const VariableNameMap &inputs,
          const VariableNameMap &outputs, const AttributeMap &attrs);
 
-  OpDesc(const proto::OpDesc &desc, ProgramDesc *prog, BlockDesc *block);
+  OpDesc(const proto::OpDesc &desc, BlockDesc *block);
 
   explicit OpDesc(BlockDesc *block) : block_(block) {}
 
   OpDesc(const OpDesc &other, BlockDesc *block) {
     *this = other;
     block_ = block;
+    need_update_ = true;
   }
 
   void CopyFrom(const OpDesc &op_desc);

paddle/fluid/framework/program_desc.cc

@@ -51,12 +51,15 @@ ProgramDesc::ProgramDesc(const ProgramDesc &o) {
     auto *block = desc_.mutable_blocks(i);
     blocks_.emplace_back(new BlockDesc(*o.blocks_[i], block, this));
   }
-  for (auto &block : blocks_) {
-    for (auto *op : block->AllOps()) {
-      for (const auto &attr : op->Proto()->attrs()) {
-        if (attr.type() == proto::AttrType::BLOCK) {
-          size_t blk_idx = attr.block_idx();
-          op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
+  for (size_t block_id = 0; block_id < blocks_.size(); ++block_id) {
+    auto all_ops = blocks_[block_id]->AllOps();
+    for (size_t op_id = 0; op_id < all_ops.size(); ++op_id) {
+      auto &op = all_ops[op_id];
+      for (const std::string &attr_name : op->AttrNames()) {
+        if (op->GetAttrType(attr_name) == proto::AttrType::BLOCK) {
+          int sub_block_id =
+              o.Block(block_id).Op(op_id)->GetBlockAttr(attr_name);
+          op->SetBlockAttr(attr_name, MutableBlock(sub_block_id));
         }
       }
     }
@@ -86,6 +89,16 @@ ProgramDesc::ProgramDesc(const std::string &binary_str) {
   for (auto &block_desc : *desc_.mutable_blocks()) {
     blocks_.emplace_back(new BlockDesc(this, &block_desc));
   }
+  for (auto &block : blocks_) {
+    for (auto *op : block->AllOps()) {
+      for (const auto &attr : op->Proto()->attrs()) {
+        if (attr.type() == proto::AttrType::BLOCK) {
+          size_t blk_idx = attr.block_idx();
+          op->SetBlockAttr(attr.name(), this->MutableBlock(blk_idx));
+        }
+      }
+    }
+  }
 }
 
 const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {

paddle/fluid/inference/tensorrt/convert/activation_op.cc

@@ -24,7 +24,7 @@ class ReluOpConverter : public OpConverter {
   void operator()(const framework::proto::OpDesc& op) override {
     // Here the two nullptr looks strange, that's because the
     // framework::OpDesc's constructor is strange.
-    framework::OpDesc op_desc(op, nullptr, nullptr);
+    framework::OpDesc op_desc(op, nullptr);
     LOG(INFO) << "convert a fluid relu op to tensorrt activation layer whose "
                  "type is Relu";
     const nvinfer1::ITensor* input_tensor =

paddle/fluid/inference/tensorrt/convert/mul_op.cc

@@ -27,7 +27,7 @@ class MulOpConverter : public OpConverter {
   void operator()(const framework::proto::OpDesc& op) override {
     VLOG(4) << "convert a fluid mul op to tensorrt fc layer without bias";
 
-    framework::OpDesc op_desc(op, nullptr, nullptr);
+    framework::OpDesc op_desc(op, nullptr);
     // Declare inputs
     auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
     auto* input2 = engine_->GetITensor(op_desc.Input("Y")[0]);

paddle/fluid/inference/tensorrt/convert/ut_helper.h

@@ -104,7 +104,7 @@ class TRTConvertValidation {
     engine_->FreezeNetwork();
 
     // Declare outputs.
-    op_desc_.reset(new framework::OpDesc(desc, nullptr, nullptr));
+    op_desc_.reset(new framework::OpDesc(desc, nullptr));
 
     // Set Inputs.
     for (const auto& input : op_desc_->InputArgumentNames()) {

paddle/fluid/operators/bilinear_interp_op.cc

@@ -34,9 +34,22 @@ class BilinearInterpOp : public framework::OperatorWithKernel {
     int out_w = ctx->Attrs().Get<int>("out_w");
     PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4");
 
+    if (ctx->HasInput("OutSize")) {
+      auto out_size_dim = ctx->GetInputDim("OutSize");
+      PADDLE_ENFORCE_EQ(out_size_dim.size(), 1,
+                        "OutSize's dimension size must be 1");
+      PADDLE_ENFORCE_EQ(out_size_dim[0], 2, "OutSize's dim[0] must be 2");
+    }
     std::vector<int64_t> dim_out({dim_x[0], dim_x[1], out_h, out_w});
     ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
+  }
 };
 
 class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
@@ -45,6 +58,10 @@ class BilinearInterpOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X",
              "(Tensor) The input tensor of bilinear interpolation, "
              "This is a 4-D tensor with shape of (N x C x h x w)");
+    AddInput("OutSize",
+             "(Tensor) This is a 1-D tensor with two number. "
+             "The first number is height and the second number is width.")
+        .AsDispensable();
     AddOutput("Out",
               "(Tensor) The dimension of output is (N x C x out_h x out_w]");
 
@@ -78,6 +95,12 @@ class BilinearInterpOpGrad : public framework::OperatorWithKernel {
       ctx->SetOutputDim(framework::GradVarName("X"), dim_x);
     }
   }
+
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("X")->type()), ctx.GetPlace());
+  }
 };
 
 }  // namespace operators

paddle/fluid/operators/bilinear_interp_op.cu

@@ -102,10 +102,21 @@ class BilinearInterpOpCUDAKernel : public framework::OpKernel<T> {
     auto* input_t = ctx.Input<Tensor>("X");      // float tensor
     auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
     auto* input = input_t->data<T>();
-    auto* output = output_t->mutable_data<T>(ctx.GetPlace());
 
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+    auto out_dims = output_t->dims();
+    auto out_size_t = ctx.Input<Tensor>("OutSize");
+    if (out_size_t != nullptr) {
+      Tensor sizes;
+      framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
+      auto size_data = sizes.data<int>();
+      out_h = size_data[0];
+      out_w = size_data[1];
+    }
+    auto* output = output_t->mutable_data<T>(
+        {out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
+
     int batch_size = input_t->dims()[0];
     int channels = input_t->dims()[1];
     int in_h = input_t->dims()[2];
@@ -139,8 +150,8 @@ class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
     auto* d_output = d_output_t->data<T>();
+    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
 
     auto& device_ctx =
         ctx.template device_context<platform::CUDADeviceContext>();
@@ -149,6 +160,16 @@ class BilinearInterpGradOpCUDAKernel : public framework::OpKernel<T> {
 
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+
+    auto out_size_t = ctx.Input<Tensor>("OutSize");
+    if (out_size_t != nullptr) {
+      Tensor sizes;
+      framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
+      auto size_data = sizes.data<int>();
+      out_h = size_data[0];
+      out_w = size_data[1];
+    }
+
     int batch_size = d_input_t->dims()[0];
     int channels = d_input_t->dims()[1];
     int in_h = d_input_t->dims()[2];

paddle/fluid/operators/bilinear_interp_op.h

@@ -24,11 +24,18 @@ class BilinearInterpKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* input_t = ctx.Input<Tensor>("X");      // float tensor
     auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
+    auto out_dims = output_t->dims();
     auto* input = input_t->data<T>();
-    auto* output = output_t->mutable_data<T>(ctx.GetPlace());
-
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+    auto out_size_t = ctx.Input<Tensor>("OutSize");
+    if (out_size_t != nullptr) {
+      auto out_size_data = out_size_t->data<int>();
+      out_h = out_size_data[0];
+      out_w = out_size_data[1];
+    }
+    auto* output = output_t->mutable_data<T>(
+        {out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
     int batch_size = input_t->dims()[0];
     int channels = input_t->dims()[1];
     int in_h = input_t->dims()[2];
@@ -83,9 +90,8 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
     auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
     auto* d_output = d_output_t->data<T>();
-
+    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
     auto& device_ctx =
         ctx.template device_context<platform::CPUDeviceContext>();
     math::SetConstant<platform::CPUDeviceContext, T> zero;
@@ -93,6 +99,14 @@ class BilinearInterpGradKernel : public framework::OpKernel<T> {
 
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
+
+    auto out_size_t = ctx.Input<Tensor>("OutSize");
+    if (out_size_t != nullptr) {
+      auto out_size_data = out_size_t->data<int>();
+      out_h = out_size_data[0];
+      out_w = out_size_data[1];
+    }
+
     int batch_size = d_input_t->dims()[0];
     int channels = d_input_t->dims()[1];
     int in_h = d_input_t->dims()[2];

python/paddle/fluid/layers/nn.py

@@ -3944,7 +3944,7 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
         input (Variable): The input tensor of bilinear interpolation,
                           This is a 4-D tensor of the shape
                           (num_batches, channels, in_h, in_w).
-        out_shape(list|tuple|None): Output shape of bilinear interpolation
+        out_shape(list|tuple|Variable|None): Output shape of bilinear interpolation
                                     layer, the shape is (out_h, out_w).
                                     Default: None
         scale(int|None): The multiplier for the input height or width.
@@ -3971,13 +3971,20 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     def _is_list_or_turple_(data):
         return (isinstance(data, list) or isinstance(data, tuple))
 
+    out_h = 0
+    out_w = 0
+    inputs = {"X": input}
     if out_shape is not None:
-        if not (_is_list_or_turple_(out_shape) and len(out_shape) == 2):
+        if not (_is_list_or_turple_(out_shape) and len(out_shape) == 2) and (
+                out_shape is not Variable):
             raise ValueError('out_shape should be a list or tuple ',
                              'with length 2, (out_h, out_w).')
-        out_shape = list(map(int, out_shape))
-        out_h = out_shape[0]
-        out_w = out_shape[1]
+        if _is_list_or_turple_(out_shape):
+            out_shape = list(map(int, out_shape))
+            out_h = out_shape[0]
+            out_w = out_shape[1]
+        else:
+            inputs['OutSize'] = out_shape
     else:
         out_h = int(input.shape[2] * scale)
         out_w = int(input.shape[3] * scale)
@@ -3985,7 +3992,7 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
     out = helper.create_tmp_variable(dtype)
     helper.append_op(
         type="bilinear_interp",
-        inputs={"X": input},
+        inputs=inputs,
         outputs={"Out": out},
         attrs={"out_h": out_h,
                "out_w": out_w})

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

@@ -17,7 +17,10 @@ import numpy as np
 from op_test import OpTest
 
 
-def bilinear_interp_np(input, out_h, out_w):
+def bilinear_interp_np(input, out_h, out_w, out_size):
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
     batch_size, channel, in_h, in_w = input.shape
     if out_h > 1:
         ratio_h = (in_h - 1.0) / (out_h - 1.0)
@@ -49,12 +52,15 @@ def bilinear_interp_np(input, out_h, out_w):
 
 class TestBilinearInterpOp(OpTest):
     def setUp(self):
+        self.out_size = None
         self.init_test_case()
         self.op_type = "bilinear_interp"
         input_np = np.random.random(self.input_shape).astype("float32")
-        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w)
-
+        output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
+                                       self.out_size)
         self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
         self.attrs = {'out_h': self.out_h, 'out_w': self.out_w}
         self.outputs = {'Out': output_np}
 
@@ -68,6 +74,7 @@ class TestBilinearInterpOp(OpTest):
         self.input_shape = [2, 3, 4, 4]
         self.out_h = 2
         self.out_w = 2
+        self.out_size = np.array([3, 3]).astype("int32")
 
 
 class TestCase1(TestBilinearInterpOp):
@@ -91,5 +98,29 @@ class TestCase3(TestBilinearInterpOp):
         self.out_w = 128
 
 
+class TestCase4(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.out_size = np.array([2, 2]).astype("int32")
+
+
+class TestCase5(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.out_size = np.array([11, 11]).astype("int32")
+
+
+class TestCase6(TestBilinearInterpOp):
+    def init_test_case(self):
+        self.input_shape = [1, 1, 128, 64]
+        self.out_h = 64
+        self.out_w = 128
+        self.out_size = np.array([65, 129]).astype("int32")
+
+
 if __name__ == "__main__":
     unittest.main()