Op (lod_reset) error message enhancement (#23499)

paddle/fluid/operators/lod_reset_op.cc

@@ -24,16 +24,17 @@ class LoDResetOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of LoDResetOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of LoDResetOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "LoDReset");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "LoDReset");
 
     if (!ctx->HasInput("Y")) {
       auto level0 = ctx->Attrs().Get<std::vector<int>>("target_lod");
-      PADDLE_ENFORCE_GT(level0.size(), 0,
-                        "If Input(Y) not provided, the target lod should be "
-                        "specified by attribute `target_lod`.");
+      PADDLE_ENFORCE_GT(
+          static_cast<int64_t>(level0.size()), 0,
+          platform::errors::InvalidArgument(
+              "If Input(Y) not provided, the target lod should be "
+              "specified by attribute `target_lod`. But the size of "
+              "`target_lod` is 0."));
     } else if (ctx->IsRuntime()) {
       ctx->ShareLoD("Y", "Out");
     }
@@ -181,10 +182,9 @@ class LoDResetGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of LoDResetGradOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@Grad) of LoDResetGradOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "LoDResetGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Output",
+                   framework::GradVarName("Out"), "LoDResetGrad");
 
     auto x_grad_name = framework::GradVarName("X");
     if (ctx->HasOutput(x_grad_name)) {

paddle/fluid/operators/lod_reset_op.h

@@ -38,9 +38,14 @@ class LoDResetKernel : public framework::OpKernel<T> {
       if (lod_t->lod().size() > 0) {
         auto y_lod = lod_t->lod();
         auto last_level = y_lod[y_lod.size() - 1];
-        PADDLE_ENFORCE_EQ((int64_t)(last_level.back()), in->dims()[0],
-                          "Last value of `Y`'s last level LoD should be equal "
-                          "to the first dimension of `X`");
+        PADDLE_ENFORCE_EQ(
+            static_cast<int64_t>(last_level.back()), in->dims()[0],
+            platform::errors::InvalidArgument(
+                "The last value of `Y`'s last level LoD should be equal "
+                "to the first dimension of `X`. But received the last value of "
+                "`Y`'s last level LoD is %d, the first dimension of `X` is "
+                "%d. ",
+                static_cast<int64_t>(last_level.back()), in->dims()[0]));
         out->set_lod(y_lod);
         return;  // early return, since lod already set
       } else {
@@ -56,16 +61,33 @@ class LoDResetKernel : public framework::OpKernel<T> {
       level0 = ctx.Attr<std::vector<int>>("target_lod");
     }
 
-    PADDLE_ENFORCE_GT(level0.size(), 1UL,
-                      "Size of target LoD should be greater than 1.");
-    PADDLE_ENFORCE_EQ(level0[0], 0,
-                      "Target LoD should be a vector starting from 0.");
-    PADDLE_ENFORCE_EQ(level0.back(), in->dims()[0],
-                      "Target LoD should be a vector end with the "
-                      "first dimension of Input(X).");
+    PADDLE_ENFORCE_GT(
+        level0.size(), 1UL,
+        platform::errors::InvalidArgument(
+            "The size of target LoD should be greater than 1. But received the "
+            "size of target LoD is %d.",
+            level0.size()));
+    PADDLE_ENFORCE_EQ(static_cast<int64_t>(level0[0]), 0,
+                      platform::errors::InvalidArgument(
+                          "Target LoD should be a vector starting from 0. But "
+                          "target LoD starts from %d.",
+                          static_cast<int64_t>(level0[0])));
+    PADDLE_ENFORCE_EQ(
+        static_cast<int64_t>(level0.back()), in->dims()[0],
+        platform::errors::InvalidArgument(
+            "The last value of `Target LoD`'s last level LoD should be equal "
+            "to the first dimension of `X`. But received the last value of "
+            "`Target LoD`'s last level LoD is %d, the first dimension of `X` "
+            "is "
+            "%d. ",
+            static_cast<int64_t>(level0.back()), in->dims()[0]));
     for (size_t i = 0; i < level0.size() - 1; ++i) {
-      PADDLE_ENFORCE(level0[i + 1] >= level0[i],
-                     "Target LoD should be an ascending vector.");
+      PADDLE_ENFORCE_GE(
+          level0[i + 1], level0[i],
+          platform::errors::InvalidArgument(
+              "Target LoD should be an ascending vector. But the %s element is "
+              "%s and the %s element of Target LoD is %s.",
+              i + 1, level0[i + 1], i, level0[i]));
     }
 
     // cast level0 to size_t

python/paddle/fluid/layers/nn.py

@@ -6189,9 +6189,16 @@ def lod_reset(x, y=None, target_lod=None):
             y = fluid.layers.data(name='y', shape=[10, 20], lod_level=2)
             out = fluid.layers.lod_reset(x=x, y=y)
     """
+    check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
+                             'lod_reset')
     helper = LayerHelper("lod_reset", **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
     if y is not None:
+        if y.lod_level > 0:
+            check_variable_and_dtype(
+                y, 'y', ['float32', 'float64', 'int32', 'int64'], 'lod_reset')
+        else:
+            check_variable_and_dtype(y, 'y', ['int32', 'int64'], 'lod_reset')
         helper.append_op(
             type="lod_reset", inputs={'X': x,
                                       'Y': y}, outputs={'Out': out})

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

@@ -17,6 +17,7 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+from paddle.fluid import Program, program_guard
 
 
 class TestLodResetOpByAttr(OpTest):
@@ -132,5 +133,32 @@ class TestLodAppendOpByAttr(OpTest):
         self.check_grad(["X"], "Out", check_dygraph=False)
 
 
+class TestLodResetOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+
+            def test_Variable():
+                # The input must be Variable.
+                x1 = fluid.create_lod_tensor(
+                    np.ones([6]), [3, 3], fluid.CPUPlace())
+                y1 = fluid.create_lod_tensor(
+                    np.ones([6]), [2, 2, 2], fluid.CPUPlace())
+                self.assertRaises(TypeError, fluid.layers.lod_reset, [x1, y1])
+
+            def test_type():
+                # dtype must be float32 or float64 or int32 or int64
+                x2 = fluid.layers.data(shape=[4], dtype='uint8', name='x2')
+                y2 = fluid.layers.data(
+                    shape=[4], dtype='uint8', name='x2', lod_level=2)
+                self.assertRaises(TypeError, fluid.layers.lod_reset, [x2, y2])
+
+            def test_type2():
+                # dtype must be int32 or int64
+                x3 = fluid.layers.data(shape=[4], dtype='float32', name='x3')
+                y3 = fluid.layers.data(
+                    shape=[4], dtype='float32', name='x3', lod_level=0)
+                self.assertRaises(TypeError, fluid.layers.lod_reset, [x3, y3])
+
+
 if __name__ == '__main__':
     unittest.main()