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

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

paddle/fluid/API.spec

@@ -286,7 +286,11 @@ paddle.fluid.layers.increment (ArgSpec(args=['x', 'value', 'in_place'], varargs=
 paddle.fluid.layers.array_write (ArgSpec(args=['x', 'i', 'array'], varargs=None, keywords=None, defaults=(None,)), ('document', '40b6d15f4c86b2b09df340d7778ad713'))
 paddle.fluid.layers.create_array (ArgSpec(args=['dtype'], varargs=None, keywords=None, defaults=None), ('document', '2d4f20087080ba5105b55205ad5c5b6a'))
 paddle.fluid.layers.less_than (ArgSpec(args=['x', 'y', 'force_cpu', 'cond'], varargs=None, keywords=None, defaults=(None, None)), ('document', '067bbc799c66289ca8b8924c26b6673f'))
+paddle.fluid.layers.less_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd6b173ae1a149e0bdfe7b8bf69285957'))
+paddle.fluid.layers.greater_than (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '2c9bd414caa6c615539018d27001b44c'))
+paddle.fluid.layers.greater_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '62c667d24e7b07e166b47a53b61b2ff4'))
 paddle.fluid.layers.equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '80c29b1dc64718f0116de90d1ac88a77'))
+paddle.fluid.layers.not_equal (ArgSpec(args=['x', 'y', 'cond'], varargs=None, keywords=None, defaults=(None,)), ('document', '56148fb1024687a08e96af79bdc5c929'))
 paddle.fluid.layers.array_read (ArgSpec(args=['array', 'i'], varargs=None, keywords=None, defaults=None), ('document', 'dd68bead34dfbaf6b0a163fc1cc3c385'))
 paddle.fluid.layers.array_length (ArgSpec(args=['array'], varargs=None, keywords=None, defaults=None), ('document', 'ffb8b9578ec66db565b223d313aa82a2'))
 paddle.fluid.layers.IfElse.__init__ (ArgSpec(args=['self', 'cond', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

paddle/fluid/operators/affine_channel_op.cc

@@ -79,9 +79,13 @@ class AffineChannelOp : public framework::OperatorWithKernel {
                            : x_dims[x_dims.size() - 1]);
 
     PADDLE_ENFORCE_EQ(scale_dims.size(), 1UL);
-    PADDLE_ENFORCE_EQ(scale_dims[0], C);
     PADDLE_ENFORCE_EQ(b_dims.size(), 1UL);
+    if (ctx->IsRuntime() || scale_dims[0] > 0) {
+      PADDLE_ENFORCE_EQ(scale_dims[0], C);
+    }
+    if (ctx->IsRuntime() || b_dims[0] > 0) {
       PADDLE_ENFORCE_EQ(b_dims[0], C);
+    }
 
     ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
     ctx->ShareLoD("X", "Out");

paddle/fluid/operators/batch_norm_op.cc

@@ -65,11 +65,22 @@ void BatchNormOp::InferShape(framework::InferShapeContext *ctx) const {
       (data_layout == DataLayout::kNCHW ? x_dims[1]
                                         : x_dims[x_dims.size() - 1]);
 
-  PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
-  PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], C);
-  PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
-  PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], C);
+  auto scale_dim = ctx->GetInputDim("Scale");
+  auto bias_dim = ctx->GetInputDim("Bias");
 
+  PADDLE_ENFORCE_EQ(scale_dim.size(), 1UL);
+  PADDLE_ENFORCE_EQ(scale_dim.size(), 1UL);
+
+  bool check = true;
+  if ((!ctx->IsRuntime()) && (framework::product(scale_dim) <= 0 ||
+                              framework::product(bias_dim) <= 0)) {
+    check = false;
+  }
+
+  if (check) {
+    PADDLE_ENFORCE_EQ(scale_dim[0], C);
+    PADDLE_ENFORCE_EQ(scale_dim[0], C);
+  }
   ctx->SetOutputDim("Y", x_dims);
   ctx->SetOutputDim("MeanOut", {C});
   ctx->SetOutputDim("VarianceOut", {C});

paddle/fluid/operators/conv_op.cc

@@ -68,10 +68,15 @@ void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
 
   std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
   for (size_t i = 0; i < strides.size(); ++i) {
+    if ((!ctx->IsRuntime()) &&
+        (in_dims[i + 2] <= 0 || filter_dims[i + 2] <= 0)) {
+      output_shape.push_back(-1);
+    } else {
       output_shape.push_back(ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
                                             dilations[i], paddings[i],
                                             strides[i]));
     }
+  }
   ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
   ctx->ShareLoD("Input", "Output");
 }

paddle/fluid/operators/cos_sim_op.cc

@@ -40,17 +40,27 @@ class CosSimOp : public framework::OperatorWithKernel {
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
 
+    bool check = true;
+    if ((!ctx->IsRuntime()) &&
+        (framework::product(x_dims) <= 0 || framework::product(y_dims) <= 0)) {
+      check = false;
+    }
+
+    if (check) {
       PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
                         "Ranks of Input(X) and Input(Y) must be equal.");
       PADDLE_ENFORCE_GE(x_dims.size(), 2,
                         "Rank of Input(X) must not be less than 2.");
-    PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 1, x_dims.size()),
+      PADDLE_ENFORCE_EQ(
+          framework::slice_ddim(x_dims, 1, x_dims.size()),
           framework::slice_ddim(y_dims, 1, y_dims.size()),
           "All dimensions except the 1st of Input(X) and Input(Y) "
           "must be equal.");
-    PADDLE_ENFORCE(x_dims[0] == y_dims[0] || y_dims[0] == 1,
+      PADDLE_ENFORCE(
+          x_dims[0] == y_dims[0] || y_dims[0] == 1,
           "The 1st dimension of Input(Y) must be equal to Input(X) or"
           " just 1 (which will be broadcasted to match Input(X)).");
+    }
 
     // resize tensor
     ctx->SetOutputDim("Out", {x_dims[0], 1});

paddle/fluid/operators/detection_map_op.cc

@@ -51,8 +51,10 @@ class DetectionMAPOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(label_dims.size(), 2,
                       "The rank of Input(Label) must be 2, "
                       "the shape is [N, 6].");
+    if (ctx->IsRuntime() || label_dims[1] > 0) {
       PADDLE_ENFORCE(label_dims[1] == 6 || label_dims[1] == 5,
                      "The shape of Input(Label) is [N, 6] or [N, 5].");
+    }
 
     if (ctx->HasInput("PosCount")) {
       PADDLE_ENFORCE(ctx->HasInput("TruePos"),

paddle/fluid/operators/row_conv_op.cc

@@ -45,9 +45,12 @@ class RowConvOp : public framework::OperatorWithKernel {
     auto filter_dims = ctx->GetInputDim("Filter");
     PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
     PADDLE_ENFORCE_EQ(filter_dims.size(), 2, "Input(Y)'s rank should be 2.");
+    if (ctx->IsRuntime() || (x_dims[1] > 0 && filter_dims[1] > 0)) {
       PADDLE_ENFORCE_EQ(
           x_dims[1], filter_dims[1],
           "The 2nd dimension of Input(X) and Input(Filter) should be same.");
+    }
+
     ctx->SetOutputDim("Out", x_dims);
     ctx->ShareLoD("X", "Out");
   }

paddle/fluid/operators/scatter_op.cc

@@ -42,10 +42,6 @@ class ScatterOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Updates")[0],
                       ctx->GetInputDim("Ids")[0],
                       "Updates and Ids should have same batch-size.");
-    framework::DDim data_dim(updates_dims);
-    for (int i = 1; i < data_dim.size(); ++i) {
-      PADDLE_ENFORCE_EQ(data_dim[i], updates_dims[i]);
-    }
     ctx->SetOutputDim("Out", ref_dims);
   }
 

paddle/fluid/operators/unpool_op.cc

@@ -99,11 +99,16 @@ class UnpoolOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(in_x_dims.size() == 4,
                    "Unpooling intput must be of 4-dimensional.");
     PADDLE_ENFORCE_EQ(in_x_dims, in_y_dims);
+
     std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
     for (size_t i = 0; i < ksize.size(); ++i) {
+      if (!ctx->IsRuntime() && in_x_dims[i + 2] <= 0) {
+        output_shape.push_back(-1);
+      } else {
         output_shape.push_back(UnpoolOutputSize(in_x_dims[i + 2], ksize[i],
                                                 paddings[i], strides[i]));
       }
+    }
     ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
   }
 };

paddle/scripts/paddle_build.sh

@@ -446,7 +446,8 @@ function assert_api_spec_approvals() {
         BRANCH="develop"
     fi
 
-    API_FILES=("paddle/fluid/API.spec"
+    API_FILES=("CMakeLists.txt"
+               "paddle/fluid/API.spec"
                "paddle/fluid/op_use_default_grad_op_maker.spec"
                "python/paddle/fluid/parallel_executor.py"
                "paddle/fluid/framework/operator.h"
@@ -469,24 +470,29 @@ function assert_api_spec_approvals() {
       echo "checking ${API_FILE} change, PR: ${GIT_PR_ID}, changes: ${API_CHANGE}"
       if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then
           # NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable.
-          # approval_user_list: velconia 1979255,panyx0718 2887803,XiaoguangHu01 46782768,chengduoZH 30176695,Xreki 12538138,luotao1 6836917,sneaxiy 32832641,tensor-tang 21351065,jacquesqiao 3048612,typhoonzero 13348433,shanyi15 35982308. 
+          # approval_user_list: velconia 1979255,XiaoguangHu01 46782768,chengduoZH 30176695,Xreki 12538138,luotao1 6836917,sneaxiy 32832641,tensor-tang 21351065,jacquesqiao 3048612,typhoonzero 13348433,shanyi15 35982308. 
           if [ "$API_FILE" == "paddle/fluid/API.spec" ];then
             APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-            python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 2887803 35982308 46782768 30176695`
+            python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 35982308 46782768 30176695`
             if [ "${APPROVALS}" == "TRUE" ];then
               APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
               python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 35982308`
             fi
+          elif [ "$API_FILE" == "CMakeLists.txt" ];then
+            APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
+            python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 6836917 46782768 30176695`
           else
             APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-            python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
+            python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
           fi
           echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
           if [ "${APPROVALS}" == "FALSE" ]; then
             if [ "$API_FILE" == "paddle/fluid/API.spec" ];then
-              echo "You must have one RD (panyx0718 or chengduoZH or XiaoguangHu01) and one PM (shanyi15) approval for the api change! ${API_FILE}"
+              echo "You must have one RD (chengduoZH or XiaoguangHu01) and one PM (shanyi15) approval for the api change! ${API_FILE}"
+            elif [ "$API_FILE" == "CMakeLists.txt" ];then
+              echo "You must have one RD (luotao1 or chengduoZH or XiaoguangHu01) approval for the cmakelist change! ${API_FILE}"
             else
-              echo "You must have one RD (velconia,panyx0718,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
+              echo "You must have one RD (velconia,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
             fi
             exit 1
           fi
@@ -496,10 +502,10 @@ function assert_api_spec_approvals() {
     HAS_CONST_CAST=`git diff -U0 upstream/$BRANCH |grep -o -m 1 "const_cast" || true`
     if [ ${HAS_CONST_CAST} ] && [ "${GIT_PR_ID}" != "" ]; then
         APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-        python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
+        python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 1979255 21351065 3048612 13348433 46782768 30176695 12538138 6836917 32832641`
         echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
         if [ "${APPROVALS}" == "FALSE" ]; then
-            echo "You must have one RD (velconia,panyx0718,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
+            echo "You must have one RD (velconia,XiaoguangHu01,chengduoZH,Xreki,luotao1,sneaxiy,tensor-tang,jacquesqiao,typhoonzero) approval for the api change! ${API_FILE}"
             exit 1
         fi
     fi

python/paddle/fluid/layers/control_flow.py

@@ -29,7 +29,8 @@ from functools import reduce
 
 __all__ = [
     'While', 'Switch', 'increment', 'array_write', 'create_array', 'less_than',
-    'equal', 'array_read', 'array_length', 'IfElse', 'DynamicRNN', 'StaticRNN',
+    'less_equal', 'greater_than', 'greater_equal', 'equal', 'not_equal',
+    'array_read', 'array_length', 'IfElse', 'DynamicRNN', 'StaticRNN',
     'reorder_lod_tensor_by_rank', 'Print', 'is_empty'
 ]
 
@@ -189,6 +190,7 @@ def Print(input,
             'print_tensor_lod': print_tensor_lod,
             'print_phase': print_phase.upper()
         })
+    return input
 
 
 class BlockGuard(object):
@@ -971,6 +973,114 @@ def less_than(x, y, force_cpu=None, cond=None):
     return cond
 
 
+@templatedoc()
+def less_equal(x, y, cond=None):
+    """
+    This layer returns the truth value of :math:`x <= y` elementwise, which is equivalent to the overloaded operator `<=`.
+
+    Args:
+        x(Variable): First operand of *less_equal*
+        y(Variable): Second operand of *less_equal*
+        cond(Variable|None): Optional output variable to store the result of *less_equal*
+
+    Returns:
+        Variable: The tensor variable storing the output of *less_equal*.
+
+    Examples:
+        .. code-block:: python
+
+          out = fluid.layers.less_equal(x=label, y=limit)
+    """
+    helper = LayerHelper("less_equal", **locals())
+    if cond is None:
+        cond = helper.create_variable_for_type_inference(dtype='bool')
+        cond.stop_gradient = True
+
+    attrs = dict()
+    if force_init_on_cpu():
+        attrs['force_cpu'] = force_init_on_cpu()
+
+    helper.append_op(
+        type='less_equal',
+        inputs={'X': [x],
+                'Y': [y]},
+        outputs={'Out': [cond]},
+        attrs=attrs)
+    return cond
+
+
+@templatedoc()
+def greater_than(x, y, cond=None):
+    """
+    This layer returns the truth value of :math:`x > y` elementwise, which is equivalent to the overloaded operator `>`.
+
+    Args:
+        x(Variable): First operand of *greater_than*
+        y(Variable): Second operand of *greater_than*
+        cond(Variable|None): Optional output variable to store the result of *greater_than*
+
+    Returns:
+        Variable: The tensor variable storing the output of *greater_than*.
+
+    Examples:
+        .. code-block:: python
+
+          out = fluid.layers.greater_than(x=label, y=limit)
+    """
+    helper = LayerHelper("greater_than", **locals())
+    if cond is None:
+        cond = helper.create_variable_for_type_inference(dtype='bool')
+        cond.stop_gradient = True
+
+    attrs = dict()
+    if force_init_on_cpu():
+        attrs['force_cpu'] = force_init_on_cpu()
+
+    helper.append_op(
+        type='greater_than',
+        inputs={'X': [x],
+                'Y': [y]},
+        outputs={'Out': [cond]},
+        attrs=attrs)
+    return cond
+
+
+@templatedoc()
+def greater_equal(x, y, cond=None):
+    """
+    This layer returns the truth value of :math:`x >= y` elementwise, which is equivalent to the overloaded operator `>=`.
+
+    Args:
+        x(Variable): First operand of *greater_equal*
+        y(Variable): Second operand of *greater_equal*
+        cond(Variable|None): Optional output variable to store the result of *greater_equal*
+
+    Returns:
+        Variable: The tensor variable storing the output of *greater_equal*.
+
+    Examples:
+        .. code-block:: python
+
+          out = fluid.layers.greater_equal(x=label, y=limit)
+    """
+    helper = LayerHelper("greater_equal", **locals())
+    if cond is None:
+        cond = helper.create_variable_for_type_inference(dtype='bool')
+        cond.stop_gradient = True
+
+    attrs = dict()
+    if force_init_on_cpu():
+        attrs['force_cpu'] = force_init_on_cpu()
+
+    helper.append_op(
+        type='greater_equal',
+        inputs={'X': [x],
+                'Y': [y]},
+        outputs={'Out': [cond]},
+        attrs=attrs)
+    return cond
+
+
 def equal(x, y, cond=None):
     """
     This layer returns the truth value of :math:`x == y` elementwise.
@@ -999,6 +1109,34 @@ def equal(x, y, cond=None):
     return cond
 
 
+def not_equal(x, y, cond=None):
+    """
+    This layer returns the truth value of :math:`x != y` elementwise, which is equivalent to the overloader operator `!=`.
+
+    Args:
+        x(Variable): First operand of *not_equal*
+        y(Variable): Second operand of *not_equal*
+        cond(Variable|None): Optional output variable to store the result of *not_equal*
+
+    Returns:
+        Variable: The tensor variable storing the output of *not_equal*.
+
+    Examples:
+        .. code-block:: python
+
+          out = fluid.layers.not_equal(x=label, y=limit)
+    """
+    helper = LayerHelper("not_equal", **locals())
+    if cond is None:
+        cond = helper.create_variable_for_type_inference(dtype='bool')
+        cond.stop_gradient = True
+
+    helper.append_op(
+        type='not_equal', inputs={'X': [x],
+                                  'Y': [y]}, outputs={'Out': [cond]})
+    return cond
+
+
 def array_read(array, i):
     """
     This function performs the operation to read the data in as an