Modify Pybind LoDTensor API according to length-based LoD (#11106)

* add lod_tensor util and modify pybind

* refind pybind LoDTensor API and modify LoDTensor and DataFeeder test

* fix test error

* fix detection map op test

* fix reorder_lod_tensor test

* fix seq_concat_op

* fix chunk evel op test

* fix target assign op

* fix warp ctc op

* address comments step 1: reverse reset_lod op

* step 2: modify op test

* add warning message

* remove has_valid_lod

* add back has_valid_lod

* address comments

* add exception catching trial

benchmark/fluid/models/machine_translation.py

@@ -173,21 +173,6 @@ def seq_to_seq_net(embedding_dim, encoder_size, decoder_size, source_dict_dim,
         return avg_cost, feeding_list
 
 
-def to_lodtensor(data, place):
-    seq_lens = [len(seq) for seq in data]
-    cur_len = 0
-    lod = [cur_len]
-    for l in seq_lens:
-        cur_len += l
-        lod.append(cur_len)
-    flattened_data = np.concatenate(data, axis=0).astype("int64")
-    flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    lod_t = core.LoDTensor()
-    lod_t.set(flattened_data, place)
-    lod_t.set_lod([lod])
-    return lod_t, lod[-1]
-
-
 def lodtensor_to_ndarray(lod_tensor):
     dims = lod_tensor.get_dims()
     ndarray = np.zeros(shape=dims).astype('float32')

benchmark/fluid/models/stacked_dynamic_lstm.py

@@ -125,18 +125,3 @@ def get_model(args):
         batch_size=args.batch_size)
 
     return loss, inference_program, adam, train_reader, test_reader, batch_acc
-
-
-def to_lodtensor(data, place):
-    seq_lens = [len(seq) for seq in data]
-    cur_len = 0
-    lod = [cur_len]
-    for l in seq_lens:
-        cur_len += l
-        lod.append(cur_len)
-    flattened_data = numpy.concatenate(data, axis=0).astype("int64")
-    flattened_data = flattened_data.reshape([len(flattened_data), 1])
-    res = fluid.LoDTensor()
-    res.set(flattened_data, place)
-    res.set_lod([lod])
-    return res

paddle/fluid/framework/lod_tensor.cc

@@ -410,5 +410,38 @@ void LoDTensor::MergeLoDTensor(
   }
 }
 
+LoD ConvertToLengthBasedLoD(const LoD &offset_lod) {
+  LoD length_lod;
+  length_lod.reserve(offset_lod.size());
+  for (size_t lvl = 0; lvl < offset_lod.size(); ++lvl) {
+    std::vector<size_t> level;
+    if (offset_lod[lvl].size() > 0) {
+      level.reserve(offset_lod[lvl].size() - 1);
+    }
+    for (size_t idx = 0; idx < offset_lod[lvl].size() - 1; ++idx) {
+      level.push_back(offset_lod[lvl][idx + 1] - offset_lod[lvl][idx]);
+    }
+    length_lod.push_back(level);
+  }
+  return length_lod;
+}
+
+LoD ConvertToOffsetBasedLoD(const LoD &length_lod) {
+  LoD offset_lod;
+  offset_lod.reserve(length_lod.size());
+  for (size_t lvl = 0; lvl < length_lod.size(); ++lvl) {
+    std::vector<size_t> level;
+    level.reserve(length_lod[lvl].size() + 1);
+    size_t tmp = 0;
+    level.push_back(tmp);
+    for (size_t idx = 0; idx < length_lod[lvl].size(); ++idx) {
+      tmp += length_lod[lvl][idx];
+      level.push_back(tmp);
+    }
+    offset_lod.push_back(level);
+  }
+  return offset_lod;
+}
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/lod_tensor.h

@@ -226,5 +226,19 @@ extern void WriteToRecordIO(recordio::Writer* writer,
 extern std::vector<LoDTensor> ReadFromRecordIO(
     recordio::Scanner* scanner, const platform::DeviceContext& dev_ctx);
 
+/*
+ * Convert between length-based LoD and offset-based LoD.
+ * The implementation of LoDTensor class use offset-based LoD.
+ * However, we want to expose the more user-friendly length-based
+ * LoD to the Python side instead.
+ *
+ * Example:
+ * If offset_lod = [[0, 2, 3],[0, 3, 5, 9]]
+ * then length_lod = [[2, 1], [3, 2, 4]]
+ */
+LoD ConvertToLengthBasedLoD(const LoD& offset_lod);
+
+LoD ConvertToOffsetBasedLoD(const LoD& length_lod);
+
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/lod_tensor_test.cc

@@ -228,6 +228,38 @@ TEST(LoD, CheckAbsLoD) {
   ASSERT_FALSE(CheckAbsLoD(abs_lod0));
 }
 
+TEST(LoD, ConvertToLengthBasedLoD) {
+  LoD offset_lod;
+  offset_lod.push_back(std::vector<size_t>({0, 2}));
+  offset_lod.push_back(std::vector<size_t>({0, 1, 3}));
+  offset_lod.push_back(std::vector<size_t>({0, 2, 4, 5}));
+
+  LoD length_lod = ConvertToLengthBasedLoD(offset_lod);
+
+  LoD expected;
+  expected.push_back(std::vector<size_t>({2}));
+  expected.push_back(std::vector<size_t>({1, 2}));
+  expected.push_back(std::vector<size_t>({2, 2, 1}));
+
+  EXPECT_EQ(length_lod, expected);
+}
+
+TEST(LoD, ConvertToOffsetBasedLoD) {
+  LoD length_lod;
+  length_lod.push_back(std::vector<size_t>({2}));
+  length_lod.push_back(std::vector<size_t>({1, 2}));
+  length_lod.push_back(std::vector<size_t>({2, 2, 1}));
+
+  LoD offset_lod = ConvertToOffsetBasedLoD(length_lod);
+
+  LoD expected;
+  expected.push_back(std::vector<size_t>({0, 2}));
+  expected.push_back(std::vector<size_t>({0, 1, 3}));
+  expected.push_back(std::vector<size_t>({0, 2, 4, 5}));
+
+  EXPECT_EQ(offset_lod, expected);
+}
+
 template <typename T>
 static void TestRecordIO() {
   LoDTensor tensor;

paddle/fluid/pybind/pybind.cc

@@ -144,28 +144,74 @@ PYBIND11_PLUGIN(core) {
   py::class_<LoDTensor, Tensor>(m, "LoDTensor")
       .def_buffer(
           [](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
-      .def(
+      .def("__init__",
-          "__init__",
+           [](LoDTensor &instance, const std::vector<std::vector<size_t>>
-          [](LoDTensor &instance, const std::vector<std::vector<size_t>> &lod) {
+                                       &recursive_sequence_lengths) {
              LoD new_lod;
-            new_lod.reserve(lod.size());
+             new_lod.reserve(recursive_sequence_lengths.size());
-            std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+             std::copy(recursive_sequence_lengths.begin(),
-            new (&instance) LoDTensor(new_lod);
+                       recursive_sequence_lengths.end(),
+                       std::back_inserter(new_lod));
+             LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod);
+             PADDLE_ENFORCE(
+                 CheckLoD(new_offset_lod, -1),
+                 "the provided recursive_sequence_lengths info is invalid");
+             new (&instance) LoDTensor(new_offset_lod);
            })
       .def("__init__", [](LoDTensor &instance) { new (&instance) LoDTensor(); })
       .def("set_lod",
            [](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
+             // the input lod is offset-based level-of-detail info
+             LOG(WARNING)
+                 << "set_lod is deprecated and will be removed by 9.2018, "
+                    "please switch to set_recursive_sequence_lengths.";
              LoD new_lod;
              new_lod.reserve(lod.size());
              std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+             PADDLE_ENFORCE(CheckLoD(new_lod, vectorize(self.dims()).front()),
+                            "the provided lod info is invalid");
              self.set_lod(new_lod);
            })
-      .def("lod", [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
+      .def("set_recursive_sequence_lengths",
-        auto lod = self.lod();
+           [](LoDTensor &self, const std::vector<std::vector<size_t>>
+                                   &recursive_sequence_lengths) {
+             // the input recursive_sequence_lengths is length-based
+             // level-of-detail info
+             LoD new_lod;
+             new_lod.reserve(recursive_sequence_lengths.size());
+             std::copy(recursive_sequence_lengths.begin(),
+                       recursive_sequence_lengths.end(),
+                       std::back_inserter(new_lod));
+             LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod);
+             PADDLE_ENFORCE(
+                 CheckLoD(new_offset_lod, vectorize(self.dims()).front()),
+                 "the provided recursive_sequence_lengths info is invalid");
+             self.set_lod(new_offset_lod);
+           })
+      .def("lod",
+           [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
+             // output the offset-based lod info
+             LOG(WARNING) << "lod is deprecated and will be removed by 9.2018, "
+                             "please switch to recursive_sequence_lengths.";
+             LoD lod = self.lod();
              std::vector<std::vector<size_t>> new_lod;
              new_lod.reserve(lod.size());
              std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
              return new_lod;
+           })
+      .def("recursive_sequence_lengths",
+           [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
+             // output the length-based lod info
+             LoD lod = ConvertToLengthBasedLoD(self.lod());
+             std::vector<std::vector<size_t>> new_lod;
+             new_lod.reserve(lod.size());
+             std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
+             return new_lod;
+           })
+      .def("has_valid_recursive_sequence_lengths", [](LoDTensor &self) -> bool {
+        // Check that the lod info is valid and match the outermost
+        // dimension of the LoDTensor data
+        return CheckLoD(self.lod(), vectorize(self.dims()).front());
       });
 
   py::class_<SelectedRows>(m, "SelectedRows")

python/paddle/fluid/data_feeder.py

@@ -47,7 +47,7 @@ class DataToLoDTensorConverter(object):
         self.lod = []
 
         for i in six.range(lod_level):
-            self.lod.append([0])
+            self.lod.append([])
 
     def feed(self, data):
         self._feed_impl_(data, self.lod, self.lod_level)
@@ -56,8 +56,7 @@ class DataToLoDTensorConverter(object):
         if lod_level == 0:
             self.data.append(data)
         else:
-            cur_lod_len = len(data)
+            lod[0].append(len(data))
-            lod[0].append(lod[0][-1] + cur_lod_len)
             for each_data in data:
                 self._feed_impl_(each_data, lod[1:], lod_level - 1)
 
@@ -66,7 +65,7 @@ class DataToLoDTensorConverter(object):
         t = core.LoDTensor()
         t.set(arr, self.place)
         if self.lod_level > 0:
-            t.set_lod(self.lod)
+            t.set_recursive_sequence_lengths(self.lod)
         return t
 
 

python/paddle/fluid/lod_tensor.py

@@ -18,80 +18,6 @@ import numpy as np
 __all__ = ['create_lod_tensor', 'create_random_int_lodtensor']
 
 
-def _validate_lod(lod, tensor_height=-1):
-    """Check whether the input length-based lod info is valid.
-
-    There are several things to check:
-    1. lod should be a list of lists. Empty list is fine.
-    2. The length of each sublist (a lod level) should be at least one.
-    3. Each element in each lod level should be an integer greater than 0.
-    4. The sum of one lod level should be equal to the length of the next lod level.
-    5. The sum of the last lod level should be equal to the tensor height. 
-       Bypass this check if user does not provide tensor_height as input.
-
-    Args:
-        lod: the length-based lod info, e.g., [[2, 3], [2, 1, 2, 3, 4]].
-        tensor_height: the outermost dimension of the tensor with which the input 
-            lod is associated with. 
-
-    Returns:
-        A boolean indicating whether the input lod is valid or not.
-    """
-    assert isinstance(lod, list), "lod should be a list"
-    # Empty lod is fine
-    if len(lod) == 0:
-        return True
-
-    lod_sum = []
-    for level in lod:
-        assert isinstance(level, list), "each item in lod should be a list"
-        # Each level of lod should have at least one length info
-        if len(level) < 1:
-            return False
-        level_sum = 0
-        for lod_len in level:
-            # Each length in a level should be > 0
-            if lod_len <= 0:
-                return False
-            level_sum += lod_len
-        lod_sum.append(level_sum)
-
-    for idx, val in enumerate(lod_sum[:-1]):
-        # Each level's sum should be equal to 
-        # the number of items in the next level
-        if val != len(lod[idx + 1]):
-            return False
-
-    if tensor_height == -1:
-        return True
-    else:
-        # Last level's sum should be equal to the tensor height
-        return lod_sum[-1] == tensor_height
-
-
-def _convert_lod(lod):
-    """Convert a length-based lod to a offset-based lod.
-
-    If the length-based lod is [[2, 3], [2, 1, 2, 3, 4]],
-    then the offset-based lod is [[0, 2, 5], [0, 2, 3, 5, 8, 12]].
-
-    Args:
-        lod: a length-based lod info. 
-
-    Returns:
-        A list of lists as the offset-based lod converted to from the input lod.
-    """
-    new_lod = []
-    for level in lod:
-        cur_len = 0
-        new_level = [cur_len]
-        for lod_len in level:
-            cur_len += lod_len
-            new_level.append(cur_len)
-        new_lod.append(new_level)
-    return new_lod
-
-
 def create_lod_tensor(data, lod, place):
     """Create a lod tensor from a numpy array, a list, or an existing lod tensor.
 
@@ -139,11 +65,11 @@ def create_lod_tensor(data, lod, place):
         flattened_data = flattened_data.reshape([len(flattened_data), 1])
         return create_lod_tensor(flattened_data, lod, place)
     elif isinstance(data, np.ndarray):
-        assert _validate_lod(lod,
-                             data.shape[0]), "the provided lod info is invalid"
         tensor = core.LoDTensor()
         tensor.set(data, place)
-        tensor.set_lod(_convert_lod(lod))
+        tensor.set_recursive_sequence_lengths(lod)
+        assert tensor.has_valid_recursive_sequence_lengths(
+        ), "the provided lod info is invalid"
         return tensor
     else:
         raise TypeError(
@@ -181,9 +107,8 @@ def create_random_int_lodtensor(lod, base_shape, place, low, high):
         A fluid LoDTensor object with tensor data and lod info. 
     """
     assert isinstance(base_shape, list), "base_shape should be a list"
-    converted_lod = _convert_lod(lod)
     # append the total number of basic elements to the front of its shape
-    overall_shape = [converted_lod[-1][-1]] + base_shape
+    overall_shape = [sum(lod[-1])] + base_shape
     # the range of integer data elements is [low, high]    
     data = np.random.random_integers(low, high, overall_shape).astype("int64")
     return create_lod_tensor(data, lod, place)

python/paddle/fluid/tests/test_data_feeder.py

@@ -22,12 +22,11 @@ class TestDataFeeder(unittest.TestCase):
         label = fluid.layers.data(name='label', shape=[1], dtype='int64')
         feeder = fluid.DataFeeder([img, label], fluid.CPUPlace())
         result = feeder.feed([([0] * 784, [9]), ([1] * 784, [1])])
-        print(result)
 
         self.assertEqual(result['image'].shape(), [2, 1, 28, 28])
         self.assertEqual(result['label'].shape(), [2, 1])
-        self.assertEqual(result['image'].lod(), [])
+        self.assertEqual(result['image'].recursive_sequence_lengths(), [])
-        self.assertEqual(result['label'].lod(), [])
+        self.assertEqual(result['label'].recursive_sequence_lengths(), [])
 
     def test_lod_level_1_converter(self):
         # lod_level = 1
@@ -42,12 +41,12 @@ class TestDataFeeder(unittest.TestCase):
         # label = [1] * len(data)
         result = feeder.feed(
             [([1, 2, 3], [1]), ([4, 5], [1]), ([6, 7, 8, 9], [1])])
-        print(result)
 
         self.assertEqual(result['sentences'].shape(), [9, 1])
         self.assertEqual(result['label'].shape(), [3, 1])
-        self.assertEqual(result['sentences'].lod(), [[0, 3, 5, 9]])
+        self.assertEqual(result['sentences'].recursive_sequence_lengths(),
-        self.assertEqual(result['label'].lod(), [])
+                         [[3, 2, 4]])
+        self.assertEqual(result['label'].recursive_sequence_lengths(), [])
 
     def test_lod_level_2_converter(self):
         # lod_level = 2
@@ -62,12 +61,12 @@ class TestDataFeeder(unittest.TestCase):
         # label = [1] * len(data)
         result = feeder.feed(
             [([[1, 2, 3], [4, 5]], [1]), ([[6, 7, 8, 9]], [1])])
-        print(result)
 
         self.assertEqual(result['paragraphs'].shape(), [9, 1])
         self.assertEqual(result['label'].shape(), [2, 1])
-        self.assertEqual(result['paragraphs'].lod(), [[0, 2, 3], [0, 3, 5, 9]])
+        self.assertEqual(result['paragraphs'].recursive_sequence_lengths(),
-        self.assertEqual(result['label'].lod(), [])
+                         [[2, 1], [3, 2, 4]])
+        self.assertEqual(result['label'].recursive_sequence_lengths(), [])
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/test_lod_tensor.py

@@ -13,44 +13,41 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
-from paddle.fluid.lod_tensor import create_lod_tensor, create_random_int_lodtensor, _validate_lod, _convert_lod
+from paddle.fluid.lod_tensor import create_lod_tensor, create_random_int_lodtensor
-import numpy
+import numpy as np
 import unittest
 
 
 class TestLoDTensor(unittest.TestCase):
-    def test_validate_lod(self):
+    def test_pybind_lod(self):
-        lod = (1, 2, 1)
+        tensor = fluid.LoDTensor()
-        self.assertRaises(AssertionError, _validate_lod, lod, -1)
-        lod = [[1, 2], (2, 3)]
-        self.assertRaises(AssertionError, _validate_lod, lod, -1)
-        lod = [1, 2, 3]
-        self.assertRaises(AssertionError, _validate_lod, lod, -1)
-
         lod = []
-        self.assertTrue(_validate_lod(lod, -1))
+        tensor.set_recursive_sequence_lengths(lod)
         lod = [[], [1], [3]]
-        self.assertFalse(_validate_lod(lod, -1))
+        self.assertRaises(Exception, tensor.set_recursive_sequence_lengths, lod)
-        lod = [[0], [-1], [3]]
+        lod = [[0], [2], [3]]
-        self.assertFalse(_validate_lod(lod, -1))
+        self.assertRaises(Exception, tensor.set_recursive_sequence_lengths, lod)
 
-        # Each level's sum should be equal to the number of items in the next level
-        # Moreover, last level's sum should be equal to the tensor height
-        lod = [[2, 3], [1, 3, 1, 2, 1]]
-        self.assertTrue(_validate_lod(lod, tensor_height=8))
-        lod = [[1, 3], [2, 1, 3]]
-        self.assertFalse(_validate_lod(lod, tensor_height=6))
-        lod = [[1, 3], [2, 1, 3, 4]]
-        self.assertFalse(_validate_lod(lod, tensor_height=5))
-
-    def test_convert_lod(self):
         lod = [[1, 2, 3]]
-        converted_lod = [[0, 1, 3, 6]]
+        tensor.set_recursive_sequence_lengths(lod)
-        self.assertEqual(_convert_lod(lod), converted_lod)
+        self.assertEqual(tensor.recursive_sequence_lengths(), lod)
+        tensor.set(np.random.random([6, 1]), fluid.CPUPlace())
+        self.assertTrue(tensor.has_valid_recursive_sequence_lengths())
+        tensor.set(np.random.random([9, 1]), fluid.CPUPlace())
+        self.assertFalse(tensor.has_valid_recursive_sequence_lengths())
 
+        # Each level's sum should be equal to the number of items in the next level
+        # Moreover, last level's sum should be equal to the tensor height
+        lod = [[2, 3], [1, 3, 1, 2, 2]]
+        tensor.set_recursive_sequence_lengths(lod)
+        self.assertEqual(tensor.recursive_sequence_lengths(), lod)
+        tensor.set(np.random.random([8, 1]), fluid.CPUPlace())
+        self.assertFalse(tensor.has_valid_recursive_sequence_lengths())
         lod = [[2, 3], [1, 3, 1, 2, 1]]
-        converted_lod = [[0, 2, 5], [0, 1, 4, 5, 7, 8]]
+        tensor.set_recursive_sequence_lengths(lod)
-        self.assertEqual(_convert_lod(lod), converted_lod)
+        self.assertTrue(tensor.has_valid_recursive_sequence_lengths())
+        tensor.set(np.random.random([9, 1]), fluid.CPUPlace())
+        self.assertFalse(tensor.has_valid_recursive_sequence_lengths())
 
     def test_create_lod_tensor(self):
         # Create LoDTensor from a list
@@ -60,19 +57,19 @@ class TestLoDTensor(unittest.TestCase):
         self.assertRaises(AssertionError, create_lod_tensor, data, wrong_lod,
                           fluid.CPUPlace())
         tensor = create_lod_tensor(data, correct_lod, fluid.CPUPlace())
-        self.assertEqual(tensor.lod(), [[0, 3, 5]])
+        self.assertEqual(tensor.recursive_sequence_lengths(), correct_lod)
 
         # Create LoDTensor from numpy array
-        data = numpy.random.random([10, 1])
+        data = np.random.random([10, 1])
         lod = [[2, 1], [3, 3, 4]]
         tensor = create_lod_tensor(data, lod, fluid.CPUPlace())
-        self.assertEqual(tensor.lod(), [[0, 2, 3], [0, 3, 6, 10]])
+        self.assertEqual(tensor.recursive_sequence_lengths(), lod)
 
         # Create LoDTensor from another LoDTensor, they are differnt instances
         new_lod = [[2, 2, 1], [1, 2, 2, 3, 2]]
         new_tensor = create_lod_tensor(tensor, new_lod, fluid.CPUPlace())
-        self.assertEqual(tensor.lod(), [[0, 2, 3], [0, 3, 6, 10]])
+        self.assertEqual(tensor.recursive_sequence_lengths(), lod)
-        self.assertEqual(new_tensor.lod(), [[0, 2, 4, 5], [0, 1, 3, 5, 8, 10]])
+        self.assertEqual(new_tensor.recursive_sequence_lengths(), new_lod)
 
     def test_create_random_int_lodtensor(self):
         # The shape of a word, commonly used in speech and NLP problem, is [1]
@@ -83,7 +80,7 @@ class TestLoDTensor(unittest.TestCase):
         high = dict_size - 1
         tensor = create_random_int_lodtensor(lod, shape,
                                              fluid.CPUPlace(), low, high)
-        self.assertEqual(tensor.lod(), [[0, 2, 5, 10]])
+        self.assertEqual(tensor.recursive_sequence_lengths(), lod)
         self.assertEqual(tensor.shape(), [10, 1])
 
 

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

@@ -162,7 +162,7 @@ class OpTest(unittest.TestCase):
                     tensor = core.LoDTensor()
                     if isinstance(np_value, tuple):
                         tensor.set(np_value[0], place)
-                        tensor.set_lod(np_value[1])
+                        tensor.set_recursive_sequence_lengths(np_value[1])
                     else:
                         tensor.set(np_value, place)
                     feed_map[name] = tensor
@@ -170,7 +170,8 @@ class OpTest(unittest.TestCase):
                 tensor = core.LoDTensor()
                 if isinstance(self.inputs[var_name], tuple):
                     tensor.set(self.inputs[var_name][0], place)
-                    tensor.set_lod(self.inputs[var_name][1])
+                    tensor.set_recursive_sequence_lengths(self.inputs[var_name][
+                        1])
                 else:
                     tensor.set(self.inputs[var_name], place)
                 feed_map[var_name] = tensor
@@ -293,7 +294,8 @@ class OpTest(unittest.TestCase):
                         str(place))
                     if isinstance(expect, tuple):
                         self.assertListEqual(
-                            actual.lod(), expect[1], "Output (" + sub_out_name +
+                            actual.recursive_sequence_lengths(), expect[1],
+                            "Output (" + sub_out_name +
                             ") has different lod at " + str(place))
             else:
                 idx = find_actual(out_name, fetch_list)
@@ -307,8 +309,8 @@ class OpTest(unittest.TestCase):
                     "Output (" + out_name + ") has diff at " + str(place) +
                     str(actual_t) + "\n" + str(expect_t))
                 if isinstance(expect, tuple):
-                    self.assertListEqual(actual.lod(), expect[1],
+                    self.assertListEqual(actual.recursive_sequence_lengths(),
-                                         "Output (" + out_name +
+                                         expect[1], "Output (" + out_name +
                                          ") has different lod at " + str(place))
 
     def _get_places(self):
@@ -408,7 +410,7 @@ class OpTest(unittest.TestCase):
         tensor = core.LoDTensor()
         tensor.set(np_value, place)
         if lod is not None:
-            tensor.set_lod(lod)
+            tensor.set_recursive_sequence_lengths(lod)
         return tensor
 
     @staticmethod

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

@@ -128,7 +128,7 @@ def create_or_get_tensor(scope, var_name, var, place):
     tensor = scope.var(var_name).get_tensor()
     if var is not None:
         assert isinstance(var, np.ndarray)
-        tensor.set_lod([[]])
+        tensor.set_recursive_sequence_lengths([])
         tensor.set_dims(var.shape)
         tensor.set(var, place)
     return tensor

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

@@ -26,36 +26,36 @@ class TestBeamSearchDecodeOp(unittest.TestCase):
 
     def append_lod_tensor(self, tensor_array, lod, data):
         lod_tensor = core.LoDTensor()
-        lod_tensor.set_lod(lod)
+        lod_tensor.set_recursive_sequence_lengths(lod)
         lod_tensor.set(data, self.place)
         tensor_array.append(lod_tensor)
 
     def test_get_set(self):
         ids = self.scope.var("ids").get_lod_tensor_array()
         self.append_lod_tensor(
-            ids, [[0, 3, 6], [0, 1, 2, 3, 4, 5, 6]],
+            ids, [[3, 3], [1, 1, 1, 1, 1, 1]],
             np.array(
                 [1, 2, 3, 4, 5, 6], dtype="int64"))
         self.append_lod_tensor(
-            ids, [[0, 3, 6], [0, 1, 1, 3, 5, 5, 6]],
+            ids, [[3, 3], [1, 0, 2, 2, 0, 1]],
             np.array(
                 [0, 1, 2, 3, 4, 5], dtype="int64"))
         self.append_lod_tensor(
-            ids, [[0, 3, 6], [0, 0, 1, 2, 3, 4, 5]],
+            ids, [[3, 3], [0, 1, 1, 1, 1, 1]],
             np.array(
                 [0, 1, 2, 3, 4], dtype="int64"))
 
         scores = self.scope.var("scores").get_lod_tensor_array()
         self.append_lod_tensor(
-            scores, [[0, 3, 6], [0, 1, 2, 3, 4, 5, 6]],
+            scores, [[3, 3], [1, 1, 1, 1, 1, 1]],
             np.array(
                 [1, 2, 3, 4, 5, 6], dtype="float64"))
         self.append_lod_tensor(
-            scores, [[0, 3, 6], [0, 1, 1, 3, 5, 5, 6]],
+            scores, [[3, 3], [1, 0, 2, 2, 0, 1]],
             np.array(
                 [0, 1, 2, 3, 4, 5], dtype="float64"))
         self.append_lod_tensor(
-            scores, [[0, 3, 6], [0, 0, 1, 2, 3, 4, 5]],
+            scores, [[3, 3], [0, 1, 1, 1, 1, 1]],
             np.array(
                 [0, 1, 2, 3, 4], dtype="float64"))
 
@@ -73,9 +73,11 @@ class TestBeamSearchDecodeOp(unittest.TestCase):
 
         beam_search_decode_op.run(self.scope, self.place)
 
-        expected_lod = [[0, 4, 8], [0, 1, 3, 6, 9, 10, 13, 16, 19]]
+        expected_lod = [[4, 4], [1, 2, 3, 3, 1, 3, 3, 3]]
-        self.assertEqual(sentence_ids.lod(), expected_lod)
+        self.assertEqual(sentence_ids.recursive_sequence_lengths(),
-        self.assertEqual(sentence_scores.lod(), expected_lod)
+                         expected_lod)
+        self.assertEqual(sentence_scores.recursive_sequence_lengths(),
+                         expected_lod)
 
         expected_data = np.array(
             [2, 1, 0, 3, 1, 0, 3, 2, 1, 5, 4, 3, 2, 4, 4, 3, 6, 5, 4], "int64")

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

@@ -48,18 +48,18 @@ class BeamSearchOpTester(unittest.TestCase):
         op.run(self.scope, core.CPUPlace())
         selected_ids = self.scope.find_var("selected_ids").get_tensor()
         print 'selected_ids', np.array(selected_ids)
-        print 'lod', selected_ids.lod()
+        print 'lod', selected_ids.recursive_sequence_lengths()
 
     def _create_pre_ids(self):
         np_data = np.array([[1, 2, 3, 4]], dtype='int64')
         tensor = create_tensor(self.scope, "pre_ids", np_data)
 
     def _create_ids(self):
-        self.lod = [[0, 1, 4], [0, 1, 2, 3, 4]]
+        self.lod = [[1, 3], [1, 1, 1, 1]]
         np_data = np.array(
             [[4, 2, 5], [2, 1, 3], [3, 5, 2], [8, 2, 1]], dtype='int64')
         tensor = create_tensor(self.scope, "ids", np_data)
-        tensor.set_lod(self.lod)
+        tensor.set_recursive_sequence_lengths(self.lod)
 
     def _create_scores(self):
         np_data = np.array(
@@ -71,7 +71,7 @@ class BeamSearchOpTester(unittest.TestCase):
             ],
             dtype='float32')
         tensor = create_tensor(self.scope, "scores", np_data)
-        tensor.set_lod(self.lod)
+        tensor.set_recursive_sequence_lengths(self.lod)
 
 
 if __name__ == '__main__':

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

@@ -65,23 +65,25 @@ def batch_bipartite_match(distance, lod, match_type=None, dist_threshold=None):
         distance (numpy.array) : The distance of two entries with shape [M, N].
         lod (list of int): The offsets of each input in this batch.
     """
-    n = len(lod) - 1
+    n = len(lod)
     m = distance.shape[1]
     match_indices = -1 * np.ones((n, m), dtype=np.int)
     match_dist = np.zeros((n, m), dtype=np.float32)
-    for i in range(len(lod) - 1):
+    cur_offset = 0
-        bipartite_match(distance[lod[i]:lod[i + 1], :], match_indices[i, :],
+    for i in range(n):
-                        match_dist[i, :])
+        bipartite_match(distance[cur_offset:(cur_offset + lod[i]), :],
+                        match_indices[i, :], match_dist[i, :])
         if match_type == 'per_prediction':
-            argmax_match(distance[lod[i]:lod[i + 1], :], match_indices[i, :],
+            argmax_match(distance[cur_offset:(cur_offset + lod[i]), :],
-                         match_dist[i, :], dist_threshold)
+                         match_indices[i, :], match_dist[i, :], dist_threshold)
+        cur_offset += lod[i]
     return match_indices, match_dist
 
 
 class TestBipartiteMatchOpWithLoD(OpTest):
     def setUp(self):
         self.op_type = 'bipartite_match'
-        lod = [[0, 5, 11, 23]]
+        lod = [[5, 6, 12]]
         dist = np.random.random((23, 217)).astype('float32')
         match_indices, match_dist = batch_bipartite_match(dist, lod[0])
 
@@ -98,7 +100,7 @@ class TestBipartiteMatchOpWithLoD(OpTest):
 class TestBipartiteMatchOpWithoutLoD(OpTest):
     def setUp(self):
         self.op_type = 'bipartite_match'
-        lod = [[0, 8]]
+        lod = [[8]]
         dist = np.random.random((8, 17)).astype('float32')
         match_indices, match_dist = batch_bipartite_match(dist, lod[0])
 
@@ -115,7 +117,7 @@ class TestBipartiteMatchOpWithoutLoD(OpTest):
 class TestBipartiteMatchOpWithPerPredictionType(OpTest):
     def setUp(self):
         self.op_type = 'bipartite_match'
-        lod = [[0, 5, 11, 23]]
+        lod = [[5, 6, 12]]
         dist = np.random.random((23, 237)).astype('float32')
         match_indices, match_dist = batch_bipartite_match(dist, lod[0],
                                                           'per_prediction', 0.5)

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

@@ -81,15 +81,19 @@ def batch_box_coder(prior_box, prior_box_var, target_box, lod, code_type,
     n = target_box.shape[0]
     m = prior_box.shape[0]
     output_box = np.zeros((n, m, 4), dtype=np.float32)
-    for i in range(len(lod) - 1):
+    cur_offset = 0
+    for i in range(len(lod)):
         if (code_type == "EncodeCenterSize"):
-            box_coder(target_box[lod[i]:lod[i + 1], :], prior_box,
+            box_coder(target_box[cur_offset:(cur_offset + lod[i]), :],
-                      prior_box_var, output_box[lod[i]:lod[i + 1], :, :],
+                      prior_box, prior_box_var,
+                      output_box[cur_offset:(cur_offset + lod[i]), :, :],
                       code_type, box_normalized)
         elif (code_type == "DecodeCenterSize"):
-            box_coder(target_box[lod[i]:lod[i + 1], :, :], prior_box,
+            box_coder(target_box[cur_offset:(cur_offset + lod[i]), :, :],
-                      prior_box_var, output_box[lod[i]:lod[i + 1], :, :],
+                      prior_box, prior_box_var,
+                      output_box[cur_offset:(cur_offset + lod[i]), :, :],
                       code_type, box_normalized)
+        cur_offset += lod[i]
     return output_box
 
 
@@ -99,7 +103,7 @@ class TestBoxCoderOp(OpTest):
 
     def setUp(self):
         self.op_type = "box_coder"
-        lod = [[0, 1, 2, 3, 4, 5]]
+        lod = [[1, 1, 1, 1, 1]]
         prior_box = np.random.random((10, 4)).astype('float32')
         prior_box_var = np.random.random((10, 4)).astype('float32')
         target_box = np.random.random((5, 10, 4)).astype('float32')
@@ -152,7 +156,7 @@ class TestBoxCoderOpWithLoD(OpTest):
 
     def setUp(self):
         self.op_type = "box_coder"
-        lod = [[0, 4, 12, 20]]
+        lod = [[4, 8, 8]]
         prior_box = np.random.random((10, 4)).astype('float32')
         prior_box_var = np.random.random((10, 4)).astype('float32')
         target_box = np.random.random((20, 4)).astype('float32')

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

@@ -144,10 +144,10 @@ class TestChunkEvalOp(OpTest):
         starts = sorted(starts)
         self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = self.gen_chunks(
             infer, label, starts)
-        self.inputs = {
+        lod = []
-            'Inference': (infer, [starts]),
+        for i in range(len(starts) - 1):
-            'Label': (label, [starts])
+            lod.append(starts[i + 1] - starts[i])
-        }
+        self.inputs = {'Inference': (infer, [lod]), 'Label': (label, [lod])}
         precision = float(
             self.num_correct_chunks
         ) / self.num_infer_chunks if self.num_infer_chunks else 0

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

@@ -22,9 +22,9 @@ from op_test import OpTest
 class CRFDecoding(object):
     def __init__(self, emission_weights, transition_weights,
                  seq_start_positions):
-        assert (emission_weights.shape[0] == seq_start_positions[-1])
+        assert (emission_weights.shape[0] == sum(seq_start_positions))
         self.tag_num = emission_weights.shape[1]
-        self.seq_num = len(seq_start_positions) - 1
+        self.seq_num = len(seq_start_positions)
 
         self.seq_start_positions = seq_start_positions
         self.x = emission_weights
@@ -34,9 +34,9 @@ class CRFDecoding(object):
         self.w = transition_weights[2:, :]
 
         self.track = np.zeros(
-            (seq_start_positions[-1], self.tag_num), dtype="int64")
+            (sum(seq_start_positions), self.tag_num), dtype="int64")
         self.decoded_path = np.zeros(
-            (seq_start_positions[-1], 1), dtype="int64")
+            (sum(seq_start_positions), 1), dtype="int64")
 
     def _decode_one_sequence(self, decoded_path, x):
         seq_len, tag_num = x.shape
@@ -71,9 +71,11 @@ class CRFDecoding(object):
             decoded_path[i - 1] = max_idx = track[i, max_idx]
 
     def decode(self):
+        cur_pos = 0
         for i in range(self.seq_num):
-            start = self.seq_start_positions[i]
+            start = cur_pos
-            end = self.seq_start_positions[i + 1]
+            cur_pos += self.seq_start_positions[i]
+            end = cur_pos
             self._decode_one_sequence(self.decoded_path[start:end, :],
                                       self.x[start:end, :])
         return self.decoded_path
@@ -90,11 +92,13 @@ class TestCRFDecodingOp1(OpTest):
         TAG_NUM = 17
         MAX_SEQ_LEN = 10
 
-        lod = [[0]]
+        lod = [[]]
+        total_len = 0
         for i in range(SEQ_NUM):
-            lod[-1].append(lod[-1][-1] + random.randint(1, MAX_SEQ_LEN))
+            lod[-1].append(random.randint(1, MAX_SEQ_LEN))
+            total_len += lod[-1][-1]
         emission = np.random.uniform(-1, 1,
-                                     [lod[-1][-1], TAG_NUM]).astype("float64")
+                                     [total_len, TAG_NUM]).astype("float64")
         transition = np.random.uniform(-0.5, 0.5,
                                        [TAG_NUM + 2, TAG_NUM]).astype("float64")
 
@@ -126,7 +130,8 @@ class TestCRFDecodingOp2(OpTest):
         self.op_type = "crf_decoding"
         TAG_NUM = 5
 
-        lod = [[0, 1, 3, 6, 10]]
+        lod = [[1, 2, 3, 4]]
+        total_len = sum(lod[-1])
         transition = np.repeat(
             np.arange(
                 TAG_NUM, dtype="float64").reshape(1, TAG_NUM),
@@ -135,13 +140,13 @@ class TestCRFDecodingOp2(OpTest):
         emission = np.repeat(
             np.arange(
                 TAG_NUM, dtype="float64").reshape(1, TAG_NUM),
-            lod[-1][-1],
+            total_len,
             axis=0)
 
         labels = np.random.randint(
-            low=0, high=TAG_NUM, size=(lod[-1][-1], 1), dtype="int64")
+            low=0, high=TAG_NUM, size=(total_len, 1), dtype="int64")
         predicted_labels = np.ones(
-            (lod[-1][-1], 1), dtype="int64") * (TAG_NUM - 1)
+            (total_len, 1), dtype="int64") * (TAG_NUM - 1)
         expected_output = (labels == predicted_labels).astype("int64")
 
         self.inputs = {

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

@@ -22,14 +22,16 @@ from test_softmax_op import stable_softmax
 def CTCAlign(input, lod, blank, merge_repeated):
     lod0 = lod[0]
     result = []
-    for i in range(len(lod0) - 1):
+    cur_offset = 0
+    for i in range(len(lod0)):
         prev_token = -1
-        for j in range(lod0[i], lod0[i + 1]):
+        for j in range(cur_offset, cur_offset + lod0[i]):
             token = input[j][0]
             if (token != blank) and not (merge_repeated and
                                          token == prev_token):
                 result.append(token)
             prev_token = token
+        cur_offset += lod0[i]
     result = np.array(result).reshape([len(result), 1]).astype("int32")
     if len(result) == 0:
         result = np.array([-1])
@@ -39,7 +41,7 @@ def CTCAlign(input, lod, blank, merge_repeated):
 class TestCTCAlignOp(OpTest):
     def config(self):
         self.op_type = "ctc_align"
-        self.input_lod = [[0, 11, 18]]
+        self.input_lod = [[11, 7]]
         self.blank = 0
         self.merge_repeated = False
         self.input = np.array(
@@ -66,7 +68,7 @@ class TestCTCAlignOp(OpTest):
 class TestCTCAlignOpCase1(TestCTCAlignOp):
     def config(self):
         self.op_type = "ctc_align"
-        self.input_lod = [[0, 11, 19]]
+        self.input_lod = [[11, 8]]
         self.blank = 0
         self.merge_repeated = True
         self.input = np.array(
@@ -77,7 +79,7 @@ class TestCTCAlignOpCase1(TestCTCAlignOp):
 class TestCTCAlignOpCase2(TestCTCAlignOp):
     def config(self):
         self.op_type = "ctc_align"
-        self.input_lod = [[0, 4]]
+        self.input_lod = [[4]]
         self.blank = 0
         self.merge_repeated = True
         self.input = np.array([0, 0, 0, 0]).reshape([4, 1]).astype("int32")

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

@@ -74,13 +74,13 @@ class TestDetectionMAPOp(OpTest):
         self.evaluate_difficult = True
         self.ap_type = "integral"
 
-        self.label_lod = [[0, 2, 4]]
+        self.label_lod = [[2, 2]]
         # label difficult xmin ymin xmax ymax
         self.label = [[1, 0, 0.1, 0.1, 0.3, 0.3], [1, 1, 0.6, 0.6, 0.8, 0.8],
                       [2, 0, 0.3, 0.3, 0.6, 0.5], [1, 0, 0.7, 0.1, 0.9, 0.3]]
 
         # label score xmin ymin xmax ymax difficult
-        self.detect_lod = [[0, 3, 7]]
+        self.detect_lod = [[3, 4]]
         self.detect = [
             [1, 0.3, 0.1, 0.0, 0.4, 0.3], [1, 0.7, 0.0, 0.1, 0.2, 0.3],
             [1, 0.9, 0.7, 0.6, 0.8, 0.8], [2, 0.8, 0.2, 0.1, 0.4, 0.4],
@@ -89,7 +89,7 @@ class TestDetectionMAPOp(OpTest):
         ]
 
         # label score true_pos false_pos
-        self.tf_pos_lod = [[0, 3, 7]]
+        self.tf_pos_lod = [[3, 4]]
         self.tf_pos = [[1, 0.9, 1, 0], [1, 0.7, 1, 0], [1, 0.3, 0, 1],
                        [1, 0.2, 1, 0], [2, 0.8, 0, 1], [2, 0.1, 1, 0],
                        [3, 0.2, 0, 1]]
@@ -112,15 +112,19 @@ class TestDetectionMAPOp(OpTest):
             for i, count in enumerate(class_pos_count):
                 class_pos_count_dict[i] = count
 
-            for i in range(len(true_pos_lod[0]) - 1):
+            cur_pos = 0
-                start = true_pos_lod[0][i]
+            for i in range(len(true_pos_lod[0])):
-                end = true_pos_lod[0][i + 1]
+                start = cur_pos
+                cur_pos += true_pos_lod[0][i]
+                end = cur_pos
                 for j in range(start, end):
                     true_pos_dict[i].append(true_pos[j])
 
-            for i in range(len(false_pos_lod[0]) - 1):
+            cur_pos = 0
-                start = false_pos_lod[0][i]
+            for i in range(len(false_pos_lod[0])):
-                end = false_pos_lod[0][i + 1]
+                start = cur_pos
+                cur_pos += false_pos_lod[0][i]
+                end = cur_pos
                 for j in range(start, end):
                     false_pos_dict[i].append(false_pos[j])
 
@@ -130,19 +134,19 @@ class TestDetectionMAPOp(OpTest):
             label_number = self.class_num
 
             out_class_pos_count = []
-            out_true_pos_lod = [0]
+            out_true_pos_lod = []
             out_true_pos = []
-            out_false_pos_lod = [0]
+            out_false_pos_lod = []
             out_false_pos = []
 
             for i in range(label_number):
                 out_class_pos_count.append([label_count[i]])
                 true_pos_list = true_pos[i]
                 out_true_pos += true_pos_list
-                out_true_pos_lod.append(len(out_true_pos))
+                out_true_pos_lod.append(len(true_pos_list))
                 false_pos_list = false_pos[i]
                 out_false_pos += false_pos_list
-                out_false_pos_lod.append(len(out_false_pos))
+                out_false_pos_lod.append(len(false_pos_list))
 
             return out_class_pos_count, out_true_pos, [
                 out_true_pos_lod
@@ -241,7 +245,7 @@ class TestDetectionMAPOpSkipDiff(TestDetectionMAPOp):
 
         self.evaluate_difficult = False
 
-        self.tf_pos_lod = [[0, 2, 6]]
+        self.tf_pos_lod = [[2, 4]]
         # label score true_pos false_pos
         self.tf_pos = [[1, 0.7, 1, 0], [1, 0.3, 0, 1], [1, 0.2, 1, 0],
                        [2, 0.8, 0, 1], [2, 0.1, 1, 0], [3, 0.2, 0, 1]]
@@ -267,9 +271,9 @@ class TestDetectionMAPOpMultiBatch(TestDetectionMAPOp):
     def init_test_case(self):
         super(TestDetectionMAPOpMultiBatch, self).init_test_case()
         self.class_pos_count = [0, 2, 1]
-        self.true_pos_lod = [[0, 0, 3, 5]]
+        self.true_pos_lod = [[0, 3, 2]]
         self.true_pos = [[0.7, 1.], [0.3, 0.], [0.2, 1.], [0.8, 0.], [0.1, 1.]]
-        self.false_pos_lod = [[0, 0, 3, 5]]
+        self.false_pos_lod = [[0, 3, 2]]
         self.false_pos = [[0.7, 0.], [0.3, 1.], [0.2, 0.], [0.8, 1.], [0.1, 0.]]
 
 

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

@@ -136,16 +136,16 @@ class BaseRNN(object):
         feed_dict = dict()
 
         for iname in self.inputs:
-            lod = [0]
+            lod = []
             np_flatten = []
             for seq_id in xrange(len(self.inputs[iname])):
                 seq_len = len(self.inputs[iname][seq_id])
-                lod.append(lod[-1] + seq_len)
+                lod.append(seq_len)
                 np_flatten.extend(self.inputs[iname][seq_id])
 
             t = fluid.Tensor()
             t.set(numpy.array(np_flatten), place)
-            t.set_lod([lod])
+            t.set_recursive_sequence_lengths([lod])
             feed_dict[iname] = t
 
         for pname in self.params:

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

@@ -39,20 +39,20 @@ class TestDyRnnStaticInput(unittest.TestCase):
 
     def prepare_x_tensor(self):
         self.x_tensor_dim = 10
-        lod = [[0, 2, 3, 6]]
+        lod = [[2, 1, 3]]
-        shape = [lod[0][-1], self.x_tensor_dim]
+        shape = [sum(lod[0]), self.x_tensor_dim]
         self.x_tensor_data = np.random.random(shape).astype('float32')
         self.x_tensor = core.LoDTensor()
-        self.x_tensor.set_lod(lod)
+        self.x_tensor.set_recursive_sequence_lengths(lod)
         self.x_tensor.set(self.x_tensor_data, self.place)
 
     def prepare_static_input_tensor(self):
         self.static_input_tensor_dim = 4
-        lod = [[0, 1, 3, 6]]
+        lod = [[1, 2, 3]]
-        shape = [lod[0][-1], self.static_input_tensor_dim]
+        shape = [sum(lod[0]), self.static_input_tensor_dim]
         self.static_input_data = np.random.random(shape).astype('float32')
         self.static_input_tensor = core.LoDTensor()
-        self.static_input_tensor.set_lod(lod)
+        self.static_input_tensor.set_recursive_sequence_lengths(lod)
         self.static_input_tensor.set(self.static_input_data, self.place)
 
     def fetch_value(self, var):
@@ -69,7 +69,7 @@ class TestDyRnnStaticInput(unittest.TestCase):
         ndarray = np.zeros(shape=dims).astype('float32')
         for i in xrange(np.product(dims)):
             ndarray.ravel()[i] = lod_tensor.get_float_element(i)
-        return ndarray, lod_tensor.lod()
+        return ndarray, lod_tensor.recursive_sequence_lengths()
 
     def build_graph(self, only_forward=False):
         x_tensor = fluid.layers.data(
@@ -131,21 +131,20 @@ class TestDyRnnStaticInput(unittest.TestCase):
             framework.grad_var_name('static_input_tensor'))
         return static_input_grad, loss
 
-    def get_seq_len_from_lod(self, lod):
-        return [lod[0][i + 1] - lod[0][i] for i in xrange(len(lod[0]) - 1)]
-
     def get_expected_static_step_outs(self):
-        x_lod = self.x_tensor.lod()
+        x_lod = self.x_tensor.recursive_sequence_lengths()
-        x_seq_len = self.get_seq_len_from_lod(x_lod)
+        x_seq_len = x_lod[0]
         x_seq_len_sorted = sorted(x_seq_len)
         x_sorted_indices = np.argsort(x_seq_len)[::-1]
 
-        static_lod = self.static_input_tensor.lod()
+        static_lod = self.static_input_tensor.recursive_sequence_lengths()
-        static_sliced = [
+        static_sliced = []
-            self.static_input_data[static_lod[0][i]:static_lod[0][i + 1]]
+        cur_offset = 0
-            for i in xrange(len(static_lod[0]) - 1)
+        for i in xrange(len(static_lod[0])):
-        ]
+            static_sliced.append(self.static_input_data[cur_offset:(
-        static_seq_len = self.get_seq_len_from_lod(static_lod)
+                cur_offset + static_lod[0][i])])
+            cur_offset += static_lod[0][i]
+        static_seq_len = static_lod[0]
         static_reordered = []
         for i in xrange(len(x_sorted_indices)):
             static_reordered.extend(static_sliced[x_sorted_indices[i]].tolist())
@@ -159,11 +158,13 @@ class TestDyRnnStaticInput(unittest.TestCase):
 
         for i in xrange(self._max_sequence_len):
             end = len(x_seq_len) - bisect.bisect_left(x_seq_len_sorted, i + 1)
-            lod = [0]
+            lod = []
+            total_len = 0
             for i in xrange(end):
-                lod.append(static_seq_len_reordered[i] + lod[-1])
+                lod.append(static_seq_len_reordered[i])
+                total_len += lod[-1]
             static_step_lods.append([lod])
-            end = lod[-1]
+            end = total_len
             static_step_outs.append(
                 np.array(static_reordered[:end]).astype('float32'))
 
@@ -199,7 +200,9 @@ class TestDyRnnStaticInput(unittest.TestCase):
             self.static_input_tensor.set_float_element(i, origin)
             numeric_gradients.ravel()[i] = (y_pos - y_neg) / self._delta / 2
         self.assertTrue(np.allclose(actual_gradients, numeric_gradients, 0.001))
-        self.assertTrue(np.allclose(actual_lod, self.static_input_tensor.lod()))
+        self.assertTrue(
+            np.allclose(actual_lod,
+                        self.static_input_tensor.recursive_sequence_lengths()))
 
 
 if __name__ == '__main__':

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

@@ -52,23 +52,29 @@ class TestEditDistanceOp(OpTest):
     def setUp(self):
         self.op_type = "edit_distance"
         normalized = False
-        x1 = np.array([[0, 12, 3, 5, 8, 2]]).astype("int64")
+        x1 = np.array([[12, 3, 5, 8, 2]]).astype("int64")
-        x2 = np.array([[0, 12, 4, 7, 8]]).astype("int64")
+        x2 = np.array([[12, 4, 7, 8]]).astype("int64")
         x1 = np.transpose(x1)
         x2 = np.transpose(x2)
-        x1_lod = [0, 1, 5]
+        x1_lod = [1, 4]
-        x2_lod = [0, 3, 4]
+        x2_lod = [3, 1]
 
-        num_strs = len(x1_lod) - 1
+        num_strs = len(x1_lod)
         distance = np.zeros((num_strs, 1)).astype("float32")
         sequence_num = np.array(2).astype("int64")
+
+        x1_offset = 0
+        x2_offset = 0
         for i in range(0, num_strs):
             distance[i] = Levenshtein(
-                hyp=x1[x1_lod[i]:x1_lod[i + 1]],
+                hyp=x1[x1_offset:(x1_offset + x1_lod[i])],
-                ref=x2[x2_lod[i]:x2_lod[i + 1]])
+                ref=x2[x2_offset:(x2_offset + x2_lod[i])])
+            x1_offset += x1_lod[i]
+            x2_offset += x2_lod[i]
             if normalized is True:
-                len_ref = x2_lod[i + 1] - x2_lod[i]
+                len_ref = x2_lod[i]
                 distance[i] = distance[i] / len_ref
+
         self.attrs = {'normalized': normalized}
         self.inputs = {'Hyps': (x1, [x1_lod]), 'Refs': (x2, [x2_lod])}
         self.outputs = {'Out': distance, 'SequenceNum': sequence_num}
@@ -81,23 +87,29 @@ class TestEditDistanceOpNormalized(OpTest):
     def setUp(self):
         self.op_type = "edit_distance"
         normalized = True
-        x1 = np.array([[0, 10, 3, 6, 5, 8, 2]]).astype("int64")
+        x1 = np.array([[10, 3, 6, 5, 8, 2]]).astype("int64")
-        x2 = np.array([[0, 10, 4, 6, 7, 8]]).astype("int64")
+        x2 = np.array([[10, 4, 6, 7, 8]]).astype("int64")
         x1 = np.transpose(x1)
         x2 = np.transpose(x2)
-        x1_lod = [0, 1, 3, 6]
+        x1_lod = [1, 2, 3]
-        x2_lod = [0, 2, 3, 5]
+        x2_lod = [2, 1, 2]
 
-        num_strs = len(x1_lod) - 1
+        num_strs = len(x1_lod)
         distance = np.zeros((num_strs, 1)).astype("float32")
         sequence_num = np.array(3).astype("int64")
+
+        x1_offset = 0
+        x2_offset = 0
         for i in range(0, num_strs):
             distance[i] = Levenshtein(
-                hyp=x1[x1_lod[i]:x1_lod[i + 1]],
+                hyp=x1[x1_offset:(x1_offset + x1_lod[i])],
-                ref=x2[x2_lod[i]:x2_lod[i + 1]])
+                ref=x2[x2_offset:(x2_offset + x2_lod[i])])
+            x1_offset += x1_lod[i]
+            x2_offset += x2_lod[i]
             if normalized is True:
-                len_ref = x2_lod[i + 1] - x2_lod[i]
+                len_ref = x2_lod[i]
                 distance[i] = distance[i] / len_ref
+
         self.attrs = {'normalized': normalized}
         self.inputs = {'Hyps': (x1, [x1_lod]), 'Refs': (x2, [x2_lod])}
         self.outputs = {'Out': distance, 'SequenceNum': sequence_num}

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

@@ -24,17 +24,16 @@ class TestFeedFetch(unittest.TestCase):
         input_array = np.ones((4, 4, 6)).astype("float32")
         input_array[0, 0, 0] = 3
         input_array[3, 3, 5] = 10
-        input_tensor = core.LoDTensor([[0, 2, 4]])
+        input_tensor = core.LoDTensor([[2, 2]])
         input_tensor.set(input_array, place)
 
         core.set_feed_variable(scope, input_tensor, "feed", 0)
 
         output_tensor = core.get_fetch_variable(scope, "feed", 0)
 
-        output_lod = output_tensor.lod()
+        output_lod = output_tensor.recursive_sequence_lengths()
-        self.assertEqual(0, output_lod[0][0])
+        self.assertEqual(2, output_lod[0][0])
         self.assertEqual(2, output_lod[0][1])
-        self.assertEqual(4, output_lod[0][2])
 
         output_array = np.array(output_tensor)
         self.assertEqual(3, output_array[0, 0, 0])

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

@@ -55,7 +55,7 @@ class TestFillConstantBatchSizeLikeWithLoDTensor(OpTest):
         self.op_type = "fill_constant_batch_size_like"
         self.inputs = {
             'Input': (np.random.random((31, 28)).astype("float32"),
-                      [[0, 9, 23, 31]])
+                      [[9, 14, 8]])
         }
         self.attrs = {
             'value': 3.5,