Api (lod_append) error message enhancement (#23541)

paddle/fluid/operators/concat_op.h

@@ -47,13 +47,13 @@ static inline framework::DDim ComputeAndCheckShape(
             is_runtime || (out_dims[j] > 0 && inputs_dims[i][j] > 0);
         if (check_shape) {
           // check all shape in run time
-          PADDLE_ENFORCE_EQ(
-              inputs_dims[0][j], inputs_dims[i][j],
-              platform::errors::InvalidArgument(
-                  "The shape of input[%d] must be equal to input[0]. "
-                  "But received input[0]'s shape = "
-                  "[%s], input[%d]'s shape = [%s].",
-                  i, inputs_dims[0], i, inputs_dims[i]));
+          PADDLE_ENFORCE_EQ(inputs_dims[0][j], inputs_dims[i][j],
+                            platform::errors::InvalidArgument(
+                                "The %d-th dimension of input[0] and input[%d] "
+                                "is expected to be equal."
+                                "But received input[0]'s shape = "
+                                "[%s], input[%d]'s shape = [%s].",
+                                j, i, inputs_dims[0], i, inputs_dims[i]));
         }
       }
     }
@@ -79,9 +79,9 @@ class ConcatKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto ins = ctx.MultiInput<framework::LoDTensor>("X");
     framework::LoDTensor* out = ctx.Output<framework::LoDTensor>("Out");
-    PADDLE_ENFORCE_NOT_NULL(
-        ins[0], platform::errors::NotFound(
-                    " The first input of concat should not be null."));
+    PADDLE_ENFORCE_NOT_NULL(ins[0],
+                            platform::errors::NotFound(
+                                "The first input tensor is not initalized."));
     auto axis = ctx.Attr<int>("axis");
     bool need_resize_out_dims = false;
     if (ctx.HasInput("AxisTensor")) {
@@ -116,7 +116,9 @@ class ConcatKernel : public framework::OpKernel<T> {
               platform::errors::Unimplemented(
                   "The lod level of all input LoDTensors should be same. "
                   "Maybe different lod level of input LoDTensors can concat,"
-                  " it is not supported currently."));
+                  "it is not supported currently. The lod level of %dth input "
+                  "is %d and first input is %d.",
+                  i, ins[i]->lod().size(), lod_size_0));
         } else {
           lod_size = 0;
           break;
@@ -181,9 +183,9 @@ class ConcatGradKernel : public framework::OpKernel<T> {
         }
       }
     }
-    PADDLE_ENFORCE_NOT_NULL(
-        ins[0], platform::errors::NotFound(
-                    "The first input of concat should not be null."));
+    PADDLE_ENFORCE_NOT_NULL(ins[0],
+                            platform::errors::NotFound(
+                                "The first input tensor is not initalized."));
 
     auto axis = ctx.Attr<int>("axis");
     if (ctx.HasInput("AxisTensor")) {

paddle/fluid/operators/lod_reset_op.cc

@@ -32,9 +32,9 @@ class LoDResetOp : public framework::OperatorWithKernel {
       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."));
+              "If Input(Y) is not provided, the output's LoD should be "
+              "specified by attribute 'target_lod'. But the size of "
+              "'target_lod' is 0."));
     } else if (ctx->IsRuntime()) {
       ctx->ShareLoD("Y", "Out");
     }

paddle/fluid/operators/lod_reset_op.h

@@ -41,10 +41,10 @@ class LoDResetKernel : public framework::OpKernel<T> {
         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. ",
+                "The last value of Input(Y)'s last level LoD should be equal "
+                "to the first dimension of Input(X). But received the last "
+                "value of Input(Y)'s last level LoD is %d, the first dimension "
+                "of Input(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
@@ -75,19 +75,16 @@ class LoDResetKernel : public framework::OpKernel<T> {
     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]));
+            "The last value of 'Target LoD''s last level LoD should be equal "
+            "to the first dimension of Input(X). But received the 'Target LoD' "
+            "is %s, Input(X)'s shape is is %s.",
+            framework::make_ddim(level0), in->dims()));
     for (size_t i = 0; i < level0.size() - 1; ++i) {
-      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]));
+      PADDLE_ENFORCE_GE(level0[i + 1], level0[i],
+                        platform::errors::InvalidArgument(
+                            "'Target LoD' should be an ascending "
+                            "vector. But received the Target LoD is %s.",
+                            framework::make_ddim(level0)));
     }
 
     // cast level0 to size_t

python/paddle/fluid/layers/nn.py

@@ -6265,11 +6265,9 @@ def lod_reset(x, y=None, target_lod=None):
     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')
+        check_type(y, 'y', (Variable), 'lod_reset')
+        if y.lod_level == 0:
+            check_variable_and_dtype(y, 'y', ['int32'], 'lod_reset')
         helper.append_op(
             type="lod_reset", inputs={'X': x,
                                       'Y': y}, outputs={'Out': out})
@@ -6327,6 +6325,9 @@ def lod_append(x, level):
     if (not isinstance(level, Iterable)) and (not isinstance(level, Variable)):
         raise ValueError("Input(level) must be list, tuple or Variable.")
 
+    check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],
+                             'lod_append')
+
     helper = LayerHelper("lod_append", **locals())
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
 
@@ -6335,6 +6336,8 @@ def lod_append(x, level):
 
     if isinstance(level, Variable):
         inputs['Y'] = level
+        if level.lod_level == 0:
+            check_variable_and_dtype(level, 'level', ['int32'], 'lod_append')
     else:
         attrs['target_lod'] = level
     helper.append_op(

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

@@ -3033,7 +3033,7 @@ class TestBook(LayerTest):
             z = layers.lod_reset(x=x, y=y)
             self.assertTrue(z.lod_level == 2)
             # case 2
-            lod_tensor_in = layers.data(name='lod_in', shape=[1], dtype='int64')
+            lod_tensor_in = layers.data(name='lod_in', shape=[1], dtype='int32')
             z = layers.lod_reset(x=x, y=lod_tensor_in)
             self.assertTrue(z.lod_level == 1)
             # case 3

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

+#Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+import paddle.fluid.core as core
+from paddle.fluid import compiler, Program, program_guard
+from paddle.fluid.op import Operator
+from paddle.fluid.backward import append_backward
+
+
+class TestLoDAppendAPI(unittest.TestCase):
+    def test_api(self, use_cuda=False):
+        main_program = Program()
+        with fluid.program_guard(main_program):
+            x = fluid.layers.data(name='x', shape=[6], dtype='float32')
+            result = fluid.layers.lod_append(x, [0, 2, 6])
+
+            x_i = np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0]).astype("float32")
+
+            for use_cuda in [False, True]:
+                if use_cuda and not fluid.core.is_compiled_with_cuda():
+                    return
+                place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+                exe = fluid.Executor(place)
+                [out] = exe.run(fluid.default_main_program(),
+                                feed={'x': x_i},
+                                fetch_list=[result],
+                                return_numpy=False)
+                self.assertEqual(out.recursive_sequence_lengths(), [[2, 4]])
+
+
+class TestLodAppendOpError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program()):
+
+            def test_x_Variable():
+                # The input(x) must be Variable.
+                x1 = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float64")
+                level1 = [0, 2, 4]
+                fluid.layers.lod_append(x1, level1)
+                self.assertRaises(TypeError, fluid.layers.lod_append, x1,
+                                  level1)
+
+            def test_level_Variable():
+                # The input(level) must be Variable or list.
+                x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
+                level2 = 2
+                fluid.layers.lod_append(x2, level2)
+                self.assertRaises(TypeError, fluid.layers.lod_append, x2,
+                                  level2)
+
+            def test_x_dtype():
+                for dtype in ["bool", "float16"]:
+                    x3 = fluid.layers.data(
+                        name='x3_' + dtype, shape=[4], dtype=dtype)
+                    level3 = fluid.layers.data(
+                        name='level3', shape=[4], dtype='int32', lod_level=2)
+                    self.assertRaises(TypeError, fluid.layers.lod_append, x3,
+                                      level3)
+
+            def test_level_dtype():
+                for dtype in ["bool", "float16", "float32", "float64", "int64"]:
+                    x4 = fluid.layers.data(
+                        name='x4_' + dtype, shape=[4], dtype='float32')
+                    level4 = fluid.layers.data(
+                        name='level4', shape=[4], dtype=dtype, lod_level=0)
+                    self.assertRaises(TypeError, fluid.layers.lod_append, x4,
+                                      level4)
+
+
+if __name__ == "__main__":
+    unittest.main()