merge develop

.gitignore

@@ -25,5 +25,6 @@ third_party/
 bazel-*
 third_party/
 
+build_*
 # clion workspace.
 cmake-build-*

CMakeLists.txt

@@ -72,7 +72,7 @@ option(WITH_INFERENCE    "Compile fluid inference library"              ON)
 option(WITH_INFERENCE_API_TEST   "Test fluid inference high-level api interface"  OFF)
 option(WITH_SYSTEM_BLAS   "Use system blas library"           OFF)
 option(PY_VERSION       "Compile PaddlePaddle with python3 support"     ${PY_VERSION})
-option(WITH_FAST_MATH   "Make use of fast math library"                 OFF)
+option(WITH_FAST_MATH   "Make use of fast math library, might affect the precision to some extent" ON)
 
 # PY_VERSION
 if(NOT PY_VERSION)

paddle/fluid/framework/op_desc.cc

@@ -50,6 +50,27 @@ class CompileTimeInferShapeContext : public InferShapeContext {
   const std::vector<std::string> &Outputs(
       const std::string &name) const override;
 
+  void ShareDim(const std::string &in, const std::string &out, size_t i = 0,
+                size_t j = 0) override {
+    PADDLE_ENFORCE_LT(i, Inputs(in).size());
+    PADDLE_ENFORCE_LT(j, Outputs(out).size());
+    const std::string &input_n = Inputs(in)[i];
+    const std::string &output_n = Outputs(out)[j];
+
+    PADDLE_ENFORCE(input_n != framework::kEmptyVarName, "The %s[%d] is @EMPTY@",
+                   in, i);
+    PADDLE_ENFORCE(output_n != framework::kEmptyVarName,
+                   "The %s[%d] is @EMPTY@", out, j);
+
+    auto *in_var = block_.FindVarRecursive(input_n);
+    auto *out_var = block_.FindVarRecursive(output_n);
+
+    PADDLE_ENFORCE(in_var->GetType() == out_var->GetType(),
+                   "The type of %s and %s is not the same.", input_n, output_n);
+
+    SetDim(output_n, GetDim(input_n));
+  }
+
   void ShareLoD(const std::string &in, const std::string &out, size_t i = 0,
                 size_t j = 0) const override {
     PADDLE_ENFORCE_LT(i, Inputs(in).size());

paddle/fluid/framework/operator.cc

@@ -542,6 +542,36 @@ class RuntimeInferShapeContext : public InferShapeContext {
     return op_.Outputs(name);
   }
 
+  void ShareDim(const std::string& in, const std::string& out, size_t i = 0,
+                size_t j = 0) override {
+    PADDLE_ENFORCE_LT(i, Inputs(in).size());
+    PADDLE_ENFORCE_LT(j, Outputs(out).size());
+    const std::string& input_n = Inputs(in)[i];
+    const std::string& output_n = Outputs(out)[j];
+
+    Variable* in_var = scope_.FindVar(input_n);
+    Variable* out_var = scope_.FindVar(output_n);
+    PADDLE_ENFORCE(in_var->Type() == out_var->Type(),
+                   "The type of %s and %s is not the same.", output_n,
+                   GetDim(input_n));
+
+    if (in_var->IsType<framework::SelectedRows>()) {
+      auto& in_sele_rows = in_var->Get<framework::SelectedRows>();
+      auto out_sele_rows = out_var->GetMutable<framework::SelectedRows>();
+      out_sele_rows->mutable_value()->Resize(in_sele_rows.value().dims());
+      out_sele_rows->set_rows(in_sele_rows.rows());
+      out_sele_rows->set_height(in_sele_rows.height());
+    } else if (in_var->IsType<framework::LoDTensor>()) {
+      auto& in_lod_tensor = in_var->Get<framework::LoDTensor>();
+      auto* out_lod_tensor = out_var->GetMutable<framework::LoDTensor>();
+      out_lod_tensor->Resize(in_lod_tensor.dims());
+    } else {
+      PADDLE_THROW(
+          "Currently, the input type of ShareDim only can be LoDTensor "
+          "or SelectedRows.");
+    }
+  }
+
   void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
                 size_t j = 0) const override {
     const std::vector<std::string>& inputs = Inputs(in);

paddle/fluid/framework/shape_inference.h

@@ -56,6 +56,9 @@ class InferShapeContext {
   virtual const std::vector<std::string> &Outputs(
       const std::string &name) const = 0;
 
+  virtual void ShareDim(const std::string &in, const std::string &out,
+                        size_t i = 0, size_t j = 0) = 0;
+
   virtual void ShareLoD(const std::string &in, const std::string &out,
                         size_t i = 0, size_t j = 0) const = 0;
 

paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc

@@ -27,9 +27,6 @@ void SetConfig(AnalysisConfig *cfg) {
   cfg->device = 0;
   cfg->enable_ir_optim = true;
   cfg->specify_input_name = true;
-#ifdef PADDLE_WITH_MKLDNN
-  cfg->_use_mkldnn = true;
-#endif
 }
 
 void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {

paddle/fluid/operators/activation_op.cc

@@ -80,7 +80,7 @@ class ActivationOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }
 
@@ -91,12 +91,26 @@ class ActivationOp : public framework::OperatorWithKernel {
   }
 };
 
+class ActivationOpInferVarType : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc& op_desc,
+                  framework::BlockDesc* block) const override {
+    auto x_name = op_desc.Input("X")[0];
+    auto out_name = op_desc.Output("Out")[0];
+    auto& x = block->FindRecursiveOrCreateVar(x_name);
+    auto& out = block->FindRecursiveOrCreateVar(out_name);
+    out.SetType(x.GetType());
+    out.SetDataType(x.GetDataType());
+  }
+};
+
 class ActivationOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
+    ctx->ShareDim("Out", framework::GradVarName("X"));
+    ctx->ShareLoD("Out", framework::GradVarName("X"));
   }
 
  protected:
@@ -525,12 +539,14 @@ namespace ops = paddle::operators;
 #define REGISTER_INPLACE_ACTIVATION_OP(OP_NAME, KERNEL_TYPE)        \
   REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp, \
                     ::paddle::operators::OP_NAME##OpMaker,          \
+                    ::paddle::operators::ActivationOpInferVarType,  \
                     ::paddle::operators::OP_NAME##GradMaker);       \
   REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad)
 
 #define REGISTER_ACTIVATION_OP(OP_NAME, KERNEL_TYPE)                    \
   REGISTER_OPERATOR(KERNEL_TYPE, ::paddle::operators::ActivationOp,     \
                     ::paddle::operators::OP_NAME##OpMaker,              \
+                    ::paddle::operators::ActivationOpInferVarType,      \
                     ::paddle::framework::DefaultGradOpDescMaker<true>); \
   REGISTER_OPERATOR(KERNEL_TYPE##_grad, ::paddle::operators::ActivationOpGrad)
 

paddle/fluid/operators/argsort_op.cc

@@ -42,8 +42,8 @@ class ArgsortOp : public framework::OperatorWithKernel {
                    "-rank(Input(X)) (%d).",
                    axis, num_dims);
 
-    ctx->SetOutputDim("Out", in_dims);
-    ctx->SetOutputDim("Indices", in_dims);
+    ctx->ShareDim("X", "Out");
+    ctx->ShareDim("X", "Indices");
     ctx->ShareLoD("X", "Out");
     ctx->ShareLoD("X", "Indices");
   }

paddle/fluid/operators/conv_shift_op.cc

@@ -44,7 +44,7 @@ class ConvShiftOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_LE(y_dims[1], x_dims[1],
                       "The 2nd dimension of Input(Y) should be less than or "
                       "equal to the 2nd dimension of Input(X).");
-    ctx->SetOutputDim("Out", x_dims);
+    ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }
 };

paddle/fluid/operators/cub_reduce.h

@@ -22,6 +22,7 @@
 
 #include <cub/cub.cuh>  // NOLINT
 #include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/framework/tensor_util.h"
 
 namespace paddle {
 namespace operators {
@@ -293,7 +294,12 @@ void TensorReduce(const framework::Tensor& x, framework::Tensor* y,
   }
   auto x_data = x.data<Tx>();
   auto y_data = y->mutable_data<Ty>(x.place());
-  if (reduce_num == 1) return;
+  if (reduce_num == 1) {
+    auto out_dims = y->dims();
+    framework::TensorCopy(x, y->place(), y);
+    y->Resize(out_dims);
+    return;
+  }
 
 #define CUB_BLOCK_DIM_CASE(block_dim)                                    \
   case block_dim: {                                                      \

paddle/fluid/operators/elementwise_op.h

@@ -41,7 +41,8 @@ class ElementwiseOp : public framework::OperatorWithKernel {
     auto y_dim = ctx->GetInputDim("Y");
     PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
                       "Rank of first input must >= rank of second input.");
-    ctx->SetOutputDim("Out", x_dim);
+
+    ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }
 
@@ -70,6 +71,7 @@ class ElementwiseOpInferVarType : public framework::VarTypeInference {
     auto& x = block->FindRecursiveOrCreateVar(x_name);
     auto& out = block->FindRecursiveOrCreateVar(out_name);
     out.SetType(x.GetType());
+    out.SetDataType(x.GetDataType());
   }
 };
 
@@ -157,10 +159,12 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel {
     auto x_grad_name = framework::GradVarName("X");
     auto y_grad_name = framework::GradVarName("Y");
     if (ctx->HasOutput(x_grad_name)) {
-      ctx->SetOutputDim(x_grad_name, x_dims);
+      ctx->ShareDim("X", /*->*/ x_grad_name);
+      ctx->ShareLoD("X", /*->*/ x_grad_name);
     }
     if (ctx->HasOutput(y_grad_name)) {
-      ctx->SetOutputDim(y_grad_name, y_dims);
+      ctx->ShareDim("Y", /*->*/ y_grad_name);
+      ctx->ShareLoD("Y", /*->*/ y_grad_name);
     }
   }
 
@@ -193,14 +197,15 @@ class ElementwiseOpExplicitGrad : public ElementwiseOpGrad {
 
     auto x_grad_name = framework::GradVarName("X");
     if (ctx->HasOutput(x_grad_name)) {
-      auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
-      ctx->SetOutputDim(x_grad_name, out_dims);
+      ctx->ShareDim(framework::GradVarName("Out"), /*->*/ x_grad_name);
+      ctx->ShareLoD(framework::GradVarName("Out"), /*->*/ x_grad_name);
     }
     auto y_grad_name = framework::GradVarName("Y");
     if (ctx->HasOutput(y_grad_name)) {
       PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
-      auto y_dims = ctx->GetInputDim("Y");
-      ctx->SetOutputDim(y_grad_name, y_dims);
+
+      ctx->ShareDim("Y", /*->*/ y_grad_name);
+      ctx->ShareLoD("Y", /*->*/ y_grad_name);
     }
   }
 };

paddle/fluid/operators/fake_dequantize_op.cc

@@ -48,7 +48,8 @@ class FakeDequantizeMaxAbsOp : public framework::OperatorWithKernel {
                    "Input(X) of FakeDequantizeMaxAbsOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of FakeDequantizeMaxAbsOp should not be null.");
-    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+
+    ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }
 };

paddle/fluid/operators/lookup_table_op.cc

@@ -137,6 +137,7 @@ class LookupTableOpGradVarTypeInference : public framework::VarTypeInference {
               << " is set to LoDTensor";
       block->Var(out_var_name)->SetType(framework::proto::VarType::LOD_TENSOR);
     }
+    block->Var(out_var_name)->SetDataType(block->Var("W")->GetDataType());
   }
 };
 

paddle/fluid/operators/prelu_op.cc

@@ -49,7 +49,7 @@ class PReluOp : public framework::OperatorWithKernel {
     } else {
       PADDLE_THROW("Unkown mode %s", mode);
     }
-    ctx->SetOutputDim("Out", x_dim);
+    ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }
 

paddle/fluid/operators/rnn_memory_helper_op.cc

@@ -54,7 +54,7 @@ class RNNMemoryHelperOpShapeInference : public framework::InferShapeBase {
                    "Input(X) of rnn_memory_helper op should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output of rnn_memory_helper op should not be null.");
-    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }
 };

paddle/fluid/operators/sequence_conv_op.cc

@@ -90,8 +90,8 @@ class SequenceConvGradOp : public framework::OperatorWithKernel {
                         ctx->GetInputDim("PaddingData"));
     }
     if (ctx->HasOutput(framework::GradVarName("X"))) {
-      ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
-      ctx->ShareLoD("X", framework::GradVarName("X"));
+      ctx->ShareDim("X", /*->*/ framework::GradVarName("X"));
+      ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
     }
     if (ctx->HasOutput(framework::GradVarName("Filter"))) {
       ctx->SetOutputDim(framework::GradVarName("Filter"),

paddle/fluid/operators/sequence_pool_op.cc

@@ -102,8 +102,9 @@ class SequencePoolGradOp : public framework::OperatorWithKernel {
     for (int64_t i = 1; i < og_dims.size(); ++i) {
       PADDLE_ENFORCE_EQ(og_dims[i], x_dims[i], "The dimension mismatch.");
     }
-    ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
-    ctx->ShareLoD("X", framework::GradVarName("X"));
+
+    ctx->ShareDim("X", /*->*/ framework::GradVarName("X"));
+    ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
   }
 
  protected:

paddle/fluid/operators/sequence_reshape_op.cc

@@ -92,7 +92,7 @@ class SequenceReshapeGradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput("X"),
                    "Input(X) of SequenceReshapeGradOp should  not be null.");
 
-    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+    ctx->ShareDim("X", /*->*/ framework::GradVarName("X"));
     ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
   }
 };

paddle/fluid/operators/sequence_softmax_op.cc

@@ -27,7 +27,8 @@ class SequenceSoftmaxOp : public framework::OperatorWithKernel {
                    "Input(X) of SequenceSoftmaxOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of SequenceSoftmaxOp should not be null.");
-    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+
+    ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }
 

paddle/fluid/operators/shrink_rnn_memory_op.cc

@@ -151,9 +151,9 @@ class ShrinkRNNMemoryGradInferShape : public framework::InferShapeBase {
   void operator()(framework::InferShapeContext *context) const override {
     PADDLE_ENFORCE(context->HasInput("X"));
     PADDLE_ENFORCE(context->HasOutput(framework::GradVarName("X")));
-    context->SetOutputDim(framework::GradVarName("X"),
-                          context->GetInputDim("X"));
-    context->ShareLoD("X", framework::GradVarName("X"));
+
+    context->ShareDim("X", /*->*/ framework::GradVarName("X"));
+    context->ShareLoD("X", /*->*/ framework::GradVarName("X"));
   }
 };
 

paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc

@@ -40,7 +40,7 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
                       "The 2nd dimension of Input(X) and Input(Label) should "
                       "be equal.");
 
-    ctx->SetOutputDim("Out", x_dims);
+    ctx->ShareDim("X", /*->*/ "Out");
     ctx->ShareLoD("X", /*->*/ "Out");
   }
 };

python/paddle/fluid/lod_tensor.py

@@ -74,7 +74,7 @@ def create_lod_tensor(data, recursive_seq_lens, place):
         assert [
             new_recursive_seq_lens
         ] == recursive_seq_lens, "data and recursive_seq_lens do not match"
-        flattened_data = np.concatenate(data, axis=0).astype("int64")
+        flattened_data = np.concatenate(data, axis=0)
         flattened_data = flattened_data.reshape([len(flattened_data), 1])
         return create_lod_tensor(flattened_data, recursive_seq_lens, place)
     elif isinstance(data, np.ndarray):

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

@@ -16,6 +16,8 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle.fluid.core as core
+from paddle.fluid.op import Operator
 
 
 class ElementwiseMulOp(OpTest):
@@ -115,5 +117,56 @@ class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
         }
 
 
+class TestElementWiseMulSelectedRows(OpTest):
+    def setUp(self):
+        self.rows = [0, 1, 2, 3, 4, 5, 6]
+        self.feature = 12
+        self.height = 100
+        self.input_shape = (len(self.rows), self.feature)
+
+    def prepare_input(self, scope, place):
+        self.input = {
+            "X": np.random.random(self.input_shape).astype("float32"),
+            "Y": np.random.random(self.input_shape).astype("float32")
+        }
+
+        def init_input(in_name):
+            x_selected_rows = scope.var(in_name).get_selected_rows()
+            x_selected_rows.set_height(self.height)
+            x_selected_rows.set_rows(self.rows)
+            x_array = self.input[in_name]
+            x_tensor = x_selected_rows.get_tensor()
+            x_tensor.set(x_array, place)
+
+        init_input("X")
+        init_input("Y")
+
+    def create_out_selected_row(self, scope):
+        return scope.var('Out').get_selected_rows()
+
+    def check_result(self, out_selected_rows):
+        assert out_selected_rows.height() == self.height
+        assert out_selected_rows.rows() == self.rows
+        out_tensor = np.array(out_selected_rows.get_tensor())
+        assert out_tensor.shape == self.input_shape
+
+    def check_with_place(self, place):
+        scope = core.Scope()
+        self.prepare_input(scope, place)
+
+        out_selected_rows = self.create_out_selected_row(scope)
+        out_selected_rows.set_height(0)
+        out_selected_rows.set_rows([])
+
+        elementwise_mul = Operator("elementwise_mul", X='X', Y='Y', Out='Out')
+        elementwise_mul.run(scope, place)
+        self.check_result(out_selected_rows)
+
+    def test_elewisemul_with_selected_rows_input(self):
+        places = [core.CPUPlace()]
+        for place in places:
+            self.check_with_place(place)
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -243,5 +243,87 @@ class TestKeepDimReduceSumMultiAxises(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestReduceSumWithDimOne(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_sum"
+        self.inputs = {'X': np.random.random((10, 1, 1)).astype("float64")}
+        self.attrs = {'dim': [1, 2], 'keep_dim': True}
+        self.outputs = {
+            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
+                                        keepdims=True)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestReduceSumWithNumelOne(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_sum"
+        self.inputs = {'X': np.random.random((1, 1)).astype("float64")}
+        self.attrs = {'dim': [1], 'keep_dim': False}
+        self.outputs = {
+            'Out': self.inputs['X'].sum(axis=tuple(self.attrs['dim']),
+                                        keepdims=False)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestReduceMeanWithDimOne(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.inputs = {'X': np.random.random((10, 1, 1)).astype("float64")}
+        self.attrs = {'dim': [1], 'keep_dim': False}
+        self.outputs = {
+            'Out': self.inputs['X'].mean(
+                axis=tuple(self.attrs['dim']), keepdims=False)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestReduceMeanWithNumelOne(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_mean"
+        self.inputs = {'X': np.random.random((1, 1)).astype("float64")}
+        self.attrs = {'dim': [1], 'keep_dim': True}
+        self.outputs = {
+            'Out': self.inputs['X'].mean(
+                axis=tuple(self.attrs['dim']), keepdims=True)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+class TestReduceAll(OpTest):
+    def setUp(self):
+        self.op_type = "reduce_sum"
+        self.inputs = {'X': np.random.random((1, 1, 1)).astype("float64")}
+        self.attrs = {'reduce_all': True, 'keep_dim': False}
+        self.outputs = {'Out': self.inputs['X'].sum()}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
 if __name__ == '__main__':
     unittest.main()