Enhance error message of cross_entropy_op,...

Enhance error message of cross_entropy_op, sigmoid_cross_entropy_with_logits_op, test=release/1.8 (#24542)

paddle/fluid/operators/cross_entropy_op.cc

@@ -25,12 +25,9 @@ class CrossEntropyOpBase : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, "Input(X) should be not null.");
-    PADDLE_ENFORCE_EQ(ctx->HasInput("Label"), true,
-                      "Input(Label) should be not null.");
-
-    PADDLE_ENFORCE_EQ(ctx->HasOutput("Y"), true,
-                      "Output(Y) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CrossEntropy");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "CrossEntropy");
+    OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "CrossEntropy");
 
     auto x_dims = ctx->GetInputDim("X");
     auto label_dims = ctx->GetInputDim("Label");
@@ -44,53 +41,61 @@ class CrossEntropyOpBase : public framework::OperatorWithKernel {
       PADDLE_ENFORCE_EQ(
           framework::slice_ddim(x_dims, 0, rank - 1),
           framework::slice_ddim(label_dims, 0, rank - 1),
-          "ShapeError: Input(X) and Input(Label) shall have the same shape "
-          "except the last dimension. But received: the shape of Input(X) is "
-          "[%s],"
-          "the shape of Input(Label) is [%s].",
-          x_dims, label_dims);
+          platform::errors::InvalidArgument(
+              "Input(X) and Input(Label) shall have the same shape "
+              "except the last dimension. But received: the shape of Input(X) "
+              "is "
+              "[%s], the shape of Input(Label) is [%s].",
+              x_dims, label_dims));
     }
 
     if (IsSoftLabel(ctx)) {
       PADDLE_ENFORCE_EQ(
           rank, label_dims.size(),
-          "ShapeError: If Attr(soft_label) == true, Input(X) and Input(Label) "
-          "shall have the same dimensions. But received: the dimensions of "
-          "Input(X) is [%d],"
-          "the shape of Input(X) is [%s], the dimensions of Input(Label) is "
-          "[%d], the shape of"
-          "Input(Label) is [%s]",
-          rank, x_dims, label_dims.size(), label_dims);
+          platform::errors::InvalidArgument(
+              "If Attr(soft_label) == true, Input(X) and Input(Label) "
+              "shall have the same dimensions. But received: the dimensions of "
+              "Input(X) is [%d],"
+              "the shape of Input(X) is [%s], the dimensions of Input(Label) "
+              "is "
+              "[%d], the shape of"
+              "Input(Label) is [%s]",
+              rank, x_dims, label_dims.size(), label_dims));
 
       if (check) {
         PADDLE_ENFORCE_EQ(
             x_dims[rank - 1], label_dims[rank - 1],
-            "ShapeError: If Attr(soft_label) == true, the last dimension of "
-            "Input(X) and Input(Label) should be equal. But received: the"
-            "last dimension of Input(X) is [%d], the shape of Input(X) is [%s],"
-            "the last dimension of Input(Label) is [%d], the shape of "
-            "Input(Label)"
-            "is [%s], the last dimension is [%d].",
-            x_dims[rank - 1], x_dims, label_dims[rank - 1], label_dims,
-            rank - 1);
+            platform::errors::InvalidArgument(
+                "If Attr(soft_label) == true, the last dimension of "
+                "Input(X) and Input(Label) should be equal. But received: the"
+                "last dimension of Input(X) is [%d], the shape of Input(X) is "
+                "[%s],"
+                "the last dimension of Input(Label) is [%d], the shape of "
+                "Input(Label)"
+                "is [%s], the last dimension is [%d].",
+                x_dims[rank - 1], x_dims, label_dims[rank - 1], label_dims,
+                rank - 1));
       }
     } else {
       if (rank == label_dims.size()) {
         PADDLE_ENFORCE_EQ(
             label_dims[rank - 1], 1UL,
-            "ShapeError: the last dimension of Input(Label) should be 1."
-            "But received: the last dimension of Input(Label) is [%d],"
-            "the last dimension is [%d]",
-            label_dims[rank - 1], rank - 1);
+            platform::errors::InvalidArgument(
+                "the last dimension of Input(Label) should be 1."
+                "But received: the last dimension of Input(Label) is [%d],"
+                "the last dimension is [%d]",
+                label_dims[rank - 1], rank - 1));
       } else {
-        PADDLE_ENFORCE_EQ(rank, label_dims.size() + 1,
-                          "ShapeError: The rank of Input(X) should be equal to "
-                          "Input(Label) plus 1."
-                          "But received: The dimension of Input(X) is [%d], "
-                          "the shape of Input(X) is [%s],"
-                          "the dimension of Input(Label) is [%d], the shape of "
-                          "Input(Label) is [%s]",
-                          rank, x_dims, label_dims.size(), label_dims);
+        PADDLE_ENFORCE_EQ(
+            rank, label_dims.size() + 1,
+            platform::errors::InvalidArgument(
+                "ShapeError: The rank of Input(X) should be equal to "
+                "Input(Label) plus 1."
+                "But received: The dimension of Input(X) is [%d], "
+                "the shape of Input(X) is [%s],"
+                "the dimension of Input(Label) is [%d], the shape of "
+                "Input(Label) is [%s]",
+                rank, x_dims, label_dims.size(), label_dims));
       }
     }
 
@@ -122,19 +127,23 @@ class CrossEntropyGradientOpBase : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const {
-    PADDLE_ENFORCE_EQ(ctx->HasInput("Label"), true,
-                      "Input(Label) should be not null.");
-    PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Y")), true,
-                      "Input(Y@GRAD) shoudl be not null.");
-    PADDLE_ENFORCE_EQ(ctx->HasOutput(framework::GradVarName("X")), true,
-                      "Output(X@GRAD) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label",
+                   "CrossEntropyGradientOpBase");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")), "Input",
+                   framework::GradVarName("Y"), "CrossEntropyGradientOpBase");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"), "CrossEntropyGradientOpBase");
 
     auto x_dims = GetXDim(ctx);
     auto label_dims = ctx->GetInputDim("Label");
     auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
     int rank = x_dims.size();
-    PADDLE_ENFORCE_EQ(dy_dims.size(), label_dims.size(),
-                      "Input(Y@Grad) and Input(Y) should have the same rank.");
+    PADDLE_ENFORCE_EQ(
+        dy_dims.size(), label_dims.size(),
+        platform::errors::InvalidArgument(
+            "Input(Y@Grad) and Input(Y) should have the same rank."
+            "But received: Y@Grad's rank is [%d], Y's rank is [%d]",
+            dy_dims.size(), label_dims.size()));
 
     bool check = true;
     if ((!ctx->IsRuntime()) &&
@@ -143,10 +152,15 @@ class CrossEntropyGradientOpBase : public framework::OperatorWithKernel {
     }
 
     if (check) {
-      PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
-                        framework::slice_ddim(dy_dims, 0, rank - 1),
-                        "The Input(X) and Input(Y@Grad) should have the same "
-                        "shape except the last dimension.");
+      PADDLE_ENFORCE_EQ(
+          framework::slice_ddim(x_dims, 0, rank - 1),
+          framework::slice_ddim(dy_dims, 0, rank - 1),
+          platform::errors::InvalidArgument(
+              "The Input(X) and Input(Y@Grad) should have the same "
+              "shape except the last dimension. but received: "
+              "the shape of Input(X) is [%s], "
+              "the shape of Input(Y@Grad) is [%s].",
+              x_dims, dy_dims));
     }
 
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
@@ -253,7 +267,7 @@ class CrossEntropyGradientOp : public CrossEntropyGradientOpBase {
   using CrossEntropyGradientOpBase::CrossEntropyGradientOpBase;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, "Input(X) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CrossEntropyGradientOp");
     CrossEntropyGradientOpBase::InferShape(ctx);
   }
 };
@@ -281,11 +295,10 @@ class CrossEntropyOp2 : public CrossEntropyOpBase {
   void InferShape(framework::InferShapeContext* ctx) const override {
     CrossEntropyOpBase::InferShape(ctx);
 
-    PADDLE_ENFORCE_EQ(ctx->HasOutput("XShape"), true,
-                      "Output(XShape) should be not null.");
-
-    PADDLE_ENFORCE_EQ(ctx->HasOutput("MatchX"), true,
-                      "Output(MatchX) should be not null.");
+    OP_INOUT_CHECK(ctx->HasOutput("XShape"), "Output", "XShape",
+                   "CrossEntropyOp2");
+    OP_INOUT_CHECK(ctx->HasOutput("MatchX"), "Output", "MatchX",
+                   "CrossEntropyOp2");
     auto x_dims = ctx->GetInputDim("X");
     auto x_dims_vec = framework::vectorize(x_dims);
     x_dims_vec.push_back(0);
@@ -305,8 +318,8 @@ class CrossEntropyGradientOp2 : public CrossEntropyGradientOpBase {
  public:
   using CrossEntropyGradientOpBase::CrossEntropyGradientOpBase;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx->HasInput("MatchX"), true,
-                      "Input(MatchX) must exist");
+    OP_INOUT_CHECK(ctx->HasInput("MatchX"), "Input", "MatchX",
+                   "CrossEntropyGradientOp2");
     CrossEntropyGradientOpBase::InferShape(ctx);
   }
 

paddle/fluid/operators/cross_entropy_op.h

@@ -166,11 +166,14 @@ struct HardLabelCrossEntropyForwardFunctor {
   HOSTDEVICE void operator()(int64_t idx) const {
     auto label = label_[idx];
     if (label != ignore_index_) {
+      // don't update to PADDLE_ENFORCE_GE and PADDLE_ENFORCE_LT cause
+      // can't use platform::errors::InvalidArgument in HOSTDEVICE
       PADDLE_ENFORCE(label >= 0 && label < feature_size_,
                      "Variable value (label) of "
                      "OP(fluid.layers.cross_entropy) expected >= 0 "
                      "and < %ld, but got %ld. Please check label value.",
                      feature_size_, label);
+
       auto match_x = x_[idx * feature_size_ + label];
       y_[idx] = -math::TolerableValue<T>()(real_log(match_x));
       match_x_[idx] = match_x;

paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc

@@ -28,16 +28,24 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X",
+                   "SigmoidCrossEntropyWithLogitsOp");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label",
+                   "SigmoidCrossEntropyWithLogitsOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
+                   "SigmoidCrossEntropyWithLogitsOp");
 
     auto x_dims = ctx->GetInputDim("X");
     auto labels_dims = ctx->GetInputDim("Label");
 
     int rank = x_dims.size();
     PADDLE_ENFORCE_EQ(rank, labels_dims.size(),
-                      "Input(X) and Input(Label) shall have the same rank.");
+                      platform::errors::InvalidArgument(
+                          "Input(X) and Input(Label) shall have the same rank."
+                          "But received: the rank of Input(X) is [%d], "
+                          "the rank of Input(Label) is [%d].",
+                          rank, labels_dims.size()));
+
     bool check = true;
     if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 ||
                                 framework::product(labels_dims) <= 0)) {
@@ -45,10 +53,14 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
     }
 
     if (check) {
-      PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank),
-                        framework::slice_ddim(labels_dims, 0, rank),
-                        "Input(X) and Input(Label) shall have the same shape "
-                        "except the last dimension.");
+      PADDLE_ENFORCE_EQ(
+          framework::slice_ddim(x_dims, 0, rank),
+          framework::slice_ddim(labels_dims, 0, rank),
+          platform::errors::InvalidArgument(
+              "Input(X) and Input(Label) shall have the same shape "
+              "except the last dimension. But received: the shape of "
+              "Input(X) is [%s], the shape of Input(Label) is [%s].",
+              x_dims, labels_dims));
     }
 
     ctx->ShareDim("X", /*->*/ "Out");
@@ -62,12 +74,16 @@ class SigmoidCrossEntropyWithLogitsGradOp
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) shoudl be not null.");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
-                   "Output(X@GRAD) should be not null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X",
+                   "SigmoidCrossEntropyWithLogitsGradOp");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label",
+                   "SigmoidCrossEntropyWithLogitsGradOp");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"),
+                   "SigmoidCrossEntropyWithLogitsGradOp");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"),
+                   "SigmoidCrossEntropyWithLogitsGradOp");
 
     auto x_dims = ctx->GetInputDim("X");
     auto labels_dims = ctx->GetInputDim("Label");
@@ -81,14 +97,23 @@ class SigmoidCrossEntropyWithLogitsGradOp
     }
 
     if (check) {
-      PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank),
-                        framework::slice_ddim(labels_dims, 0, rank),
-                        "Input(X) and Input(Label) shall have the same shape.");
+      PADDLE_ENFORCE_EQ(
+          framework::slice_ddim(x_dims, 0, rank),
+          framework::slice_ddim(labels_dims, 0, rank),
+          platform::errors::InvalidArgument(
+              "Input(X) and Input(Label) shall have the same shape "
+              "except the last dimension. But received: the shape of "
+              "Input(X) is [%s], the shape of Input(Label) is [%s].",
+              x_dims, labels_dims));
 
       PADDLE_ENFORCE_EQ(
           framework::slice_ddim(x_dims, 0, rank),
           framework::slice_ddim(dout_dims, 0, rank),
-          "Input(X) and Input(Out@Grad) shall have the same shape.");
+          platform::errors::InvalidArgument(
+              "Input(X) and Input(Out@Grad) shall have the same shape "
+              "except the last dimension. But received: the shape of "
+              "Input(X) is [%s], the shape of Input(Out@Grad) is [%s].",
+              x_dims, dout_dims));
     }
 
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);

python/paddle/fluid/layers/loss.py

@@ -1404,9 +1404,14 @@ def sigmoid_cross_entropy_with_logits(x,
     ${comment}
 
     Args:
-        x(${x_type}): ${x_comment}
-        label(${label_type}): ${label_comment}
-        ignore_index(int): ${ignore_index_comment}
+        x(Variable): a 2-D tensor with shape N x D, where N is the batch size and
+                D is the number of classes. This input is a tensor of logits computed
+                by the previous operator. Logits are unscaled log probabilities given
+                as log(p/(1-p)) The data type should be float32 or float64.
+        label (Variable): a 2-D tensor of the same type and shape as X.
+                This input is a tensor of probabalistic labels for each logit.
+        ignore_index(int): Specifies a target value that is ignored and 
+                does not contribute to the input gradient.
         name(str|None): The default value is None.  Normally there is
             no need for user to set this property.  For more information,
             please refer to :ref:`api_guide_Name`
@@ -1431,6 +1436,8 @@ def sigmoid_cross_entropy_with_logits(x,
                 normalize=True) # or False
             # loss = fluid.layers.reduce_sum(loss) # summation of loss
     """
+    check_variable_and_dtype(x, 'input', ['float16', 'float32', 'float64'],
+                             'sigmoid_cross_entropy_with_logits')
 
     helper = LayerHelper("sigmoid_cross_entropy_with_logits", **locals())
 

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

@@ -20,6 +20,8 @@ from scipy.special import logit
 from scipy.special import expit
 import paddle.fluid.core as core
 import unittest
+from paddle.fluid import compiler, Program, program_guard
+import paddle.fluid as fluid
 
 
 class TestSigmoidCrossEntropyWithLogitsOp1(OpTest):
@@ -242,5 +244,31 @@ class TestSigmoidCrossEntropyWithLogitsOp6(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class TestSigmoidCrossEntropyWithLogitsOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+
+            def test_Variable():
+                # the input of sigmoid_cross_entropy_with_logits must be Variable.
+                x1 = fluid.create_lod_tensor(
+                    np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
+                lab1 = fluid.create_lod_tensor(
+                    np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
+                fluid.layers.sigmoid_cross_entropy_with_logits(x1, lab1)
+
+            self.assertRaises(TypeError, test_Variable)
+
+            def test_dtype():
+                # the input dtype of sigmoid_cross_entropy_with_logits must be float16 or float32 or float64
+                # float16 only can be set on GPU place
+                x2 = fluid.layers.data(
+                    name='x2', shape=[3, 4, 5, 6], dtype="int32")
+                lab2 = fluid.layers.data(
+                    name='lab2', shape=[3, 4, 5, 6], dtype="int32")
+                fluid.layers.sigmoid_cross_entropy_with_logits(x2, lab2)
+
+            self.assertRaises(TypeError, test_dtype)
+
+
 if __name__ == '__main__':
     unittest.main()