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

doc/fluid/design/concepts/lod_tensor.md

@@ -173,6 +173,7 @@ are transformed into offsets of elements/words as follows:
 
 ## Slicing of LoD Tensors
 
+
 When we use the above 2-level LoD Tensor as the input to a nested-RNN, we need to retrieve certain sequences.  Here we define the sequence identified by branch <i,j,...> as the **<i,j,...>-slice**.
 
 For example, the <2>-slice of above example is
@@ -189,3 +190,22 @@ and the <2,0>-slice of above slice is
 10  12
   ||
 ```
+
+## Length Representation vs Offset Representation
+
+The offset representation is an implementation-oriented decision and it makes understanding the idea behind LoDTensor difficult.
+Hence, we encapsulate this implementation detail in C++ and expose the original length representation in our Python API. 
+Specifically, we call this length representation `recursive_sequence_lengths` and users can use the following code to set or get the `recursive_sequence_lengths` of a LoDTensor in Python:
+```Python
+# length representation of lod called recursive_sequence_lengths
+recursive_seq_lens = [[3, 1, 2], [2, 2, 1, 3, 1, 2]]
+# Create a LoDTensor that has the above recursive_sequence_lengths info.
+# This recursive_sequence_lengths will be converted to an offset representation of LoD in the C++ implementation under the hood.
+tensor = fluid.LoDTensor(lod)
+
+# Set/Change the recursive_sequence_lengths info of LoDTensor
+tensor.set_recursive_sequence_lengths([[3, 1, 2]])
+# Get the recursive_sequence_lengths info of a LoDTensor (the offset-based LoD representation stored in C++ will be converted 
+# back to length-based recursive_sequence_lengths), new_recursive_seq_lens = [[3, 1, 2]]
+new_recursive_seq_lens = tensor.recursive_sequence_lengths()
+```

doc/fluid/howto/optimization/timeline_cn.md

+# 如何使用timeline工具做性能分析
+
+1. 在训练的主循环外加上`with profiler.profiler(...)`。运行之后，代码会在`/tmp/profile`目录下生成一个profile的记录文件。
+
+	**提示：**
+	请不要在timeline记录信息时运行太多次迭代，因为timeline中的记录数量和迭代次数是成正比的。
+
+	```python
+	with profiler.profiler('All', 'total', '/tmp/profile') as prof:
+	    for pass_id in range(pass_num):
+	        for batch_id, data in enumerate(train_reader()):
+	            exe.run(fluid.default_main_program(),
+	                    feed=feeder.feed(data),
+	                    fetch_list=[])
+	            ...
+	```
+
+1. 运行`python paddle/tools/timeline.py`来处理`/tmp/profile`，这个程序默认会生成一个`/tmp/timeline`文件，你也可以用命令行参数来修改这个路径，请参考[timeline.py](https://github.com/PaddlePaddle/Paddle/blob/develop/tools/timeline.py)。
+
+1. 打开chrome浏览器，访问<chrome://tracing/>，用`load`按钮来加载生成的`timeline`文件。
+
+	![chrome tracing](./tracing.jpeg)
+
+1. 结果如下图所示，可以放到来查看timetime的细节信息。
+
+	![chrome timeline](./timeline.jpeg)

paddle/contrib/inference/CMakeLists.txt

@@ -61,7 +61,7 @@ cc_library(paddle_inference_tensorrt_subgraph_engine
 inference_api_test(test_paddle_inference_api_tensorrt_subgraph_engine ARGS test_word2vec)
 endif()
 
-if (WITH_ANAKIN AND WITH_TESTING) # only needed in CI
+if (WITH_ANAKIN) # only needed in CI
     # Due to Anakin do not have official library releases and the versions of protobuf and cuda do not match Paddle's,
     # so anakin library will not be merged to our official inference library. To use anakin prediction API, one need to
     # compile the libinference_anakin_api.a and compile with anakin.so.
@@ -71,10 +71,12 @@ if (WITH_ANAKIN AND WITH_TESTING) # only needed in CI
     target_compile_options(inference_anakin_api_shared BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
     target_link_libraries(inference_anakin_api anakin anakin_saber_common)
     target_link_libraries(inference_anakin_api_shared anakin anakin_saber_common)
-    cc_test(inference_anakin_test SRCS paddle_inference_api_anakin_engine_tester.cc
+    if (WITH_TESTING)
+        cc_test(inference_anakin_test SRCS paddle_inference_api_anakin_engine_tester.cc
                                   ARGS --model=${ANAKIN_INSTALL_DIR}/mobilenet_v2.anakin.bin
                                   DEPS inference_anakin_api)
-    target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+        target_compile_options(inference_anakin_test BEFORE PUBLIC ${ANAKIN_COMPILE_EXTRA_FLAGS})
+     endif(WITH_TESTING)
 endif()
 
 if(WITH_TESTING)

paddle/fluid/framework/parallel_executor.cc

@@ -253,6 +253,9 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
       t->set_lod(lod_tensors[j].lod());
     }
   }
+  for (auto &p : member_->places_) {
+    platform::DeviceContextPool::Instance().Get(p)->Wait();
+  }
 }
 
 ParallelExecutor::~ParallelExecutor() {

paddle/scripts/paddle_build.sh

@@ -106,6 +106,7 @@ function cmake_gen() {
         -DWITH_FLUID_ONLY=${WITH_FLUID_ONLY:-OFF}
         -DCMAKE_EXPORT_COMPILE_COMMANDS=ON
         -DWITH_CONTRIB=${WITH_CONTRIB:-ON}
+        -DWITH_ANAKIN=${WITH_ANAKIN:-ON}
         -DWITH_INFERENCE_DEMO=${WITH_INFERENCE_DEMO:-ON}
     ========================================
 EOF

python/paddle/fluid/framework.py

@@ -27,6 +27,7 @@ __all__ = [
     'Variable',
     'Program',
     'Operator',
+    'Parameter',
     'default_startup_program',
     'default_main_program',
     'program_guard',
@@ -1922,7 +1923,7 @@ def program_guard(main_program, startup_program=None):
 def get_var(name, program=None):
     """
     Get a variable by name from the global block of a program.
-    
+
     Args:
         name(str): name of the variable
         program(Program|None): program object.

python/paddle/fluid/layers/nn.py

@@ -5078,12 +5078,12 @@ def mean_iou(input, label, num_classes):
     out_correct = helper.create_tmp_variable(dtype='int32')
     helper.append_op(
         type="mean_iou",
-        inputs={"predictions": input,
-                "labels": label},
+        inputs={"Predictions": input,
+                "Labels": label},
         outputs={
-            "out_mean_iou": out_mean_iou,
-            "out_wrong": out_wrong,
-            "out_correct": out_correct
+            "OutMeanIou": out_mean_iou,
+            "OutWrong": out_wrong,
+            "OutCorrect": out_correct
         },
         attrs={"num_classes": num_classes})
     return out_mean_iou, out_wrong, out_correct

python/paddle/fluid/lod_tensor.py

@@ -18,15 +18,16 @@ import numpy as np
 __all__ = ['create_lod_tensor', 'create_random_int_lodtensor']
 
 
-def create_lod_tensor(data, lod, place):
+def create_lod_tensor(data, recursive_seq_lens, place):
     """
     Create a lod tensor from a numpy array, a list, or an existing lod tensor.
 
     Create a lod tensor by doing the following:
 
-    1. Check that the length-based input lod is valid.
+    1. Check that the length-based level of detail (LoD) also known as 
+       recursive_sequence_lengths of the input is valid.
 
-    2. Convert the length-based lod to a offset-based LoD.
+    2. Convert recursive_sequence_lengths to a offset-based LoD.
 
     3. Copy the data from a numpy array, a list or a existing lod tensor to
        CPU or GPU device (based on input place).
@@ -37,45 +38,47 @@ def create_lod_tensor(data, lod, place):
 
         Suppose we want LoDTensor to hold data for sequences of word, where each
         word is represented by an integer. If we want to create a LoDTensor to
-        represent two  sentences, one of 2 words, and one of 3 words.
+        represent two sentences, one of 2 words, and one of 3 words.
 
         Then :code:`data` can be a numpy array of integers with shape (5, 1).
-        :code:`lod` will be [[2, 3]], indicating the length(# of words) in each
-        sentence. This length-based input lod [[2, 3]] will be converted to
-        offset-based lod [[0, 2, 5]] inside the function call.
+        :code:`recursive_seq_lens` will be [[2, 3]], indicating the length(# of words) in each
+        sentence. This length-based :code:`recursive_seq_lens` [[2, 3]] will be converted to
+        offset-based LoD [[0, 2, 5]] inside the function call.
 
     Please reference :ref:`api_guide_low_level_lod_tensor` for more details
     regarding LoD.
 
     Args:
         data(numpy.ndarray|list|LoDTensor): a numpy array or a LoDTensor or a
-            list holding the data to be  copied.
-        lod(list): a list of lists indicating the length-based LoD info
-            specified by the user.
+            list holding the data to be copied.
+        recursive_seq_lens(list): a list of lists indicating the length-based level of detail 
+            info specified by the user.
         place(Place): CPU or GPU place indicating where the data in the new
             LoDTensor will be stored.
 
     Returns:
-        A fluid LoDTensor object with tensor data and lod info.
+        A fluid LoDTensor object with tensor data and recursive_seq_lens info.
     """
     if isinstance(data, core.LoDTensor):
-        return create_lod_tensor(np.array(data), lod, place)
+        return create_lod_tensor(np.array(data), recursive_seq_lens, place)
     elif isinstance(data, list):
         # When input data is a list, it only deal with the case where the base element 
         # is an index of shape [1] and dtype int64 (e.g., word id). Hence, the generated 
         # LoDTensor will be of shape [n, 1] and dtype int64, where `n` is the total number 
         # of words or other indexes in the sequence. 
-        new_lod = []
+        new_recursive_seq_lens = []
         for seq in data:
-            new_lod.append(len(seq))
-        assert [new_lod] == lod, "data and lod do not match"
+            new_recursive_seq_lens.append(len(seq))
+        assert [
+            new_recursive_seq_lens
+        ] == recursive_seq_lens, "data and recursive_seq_lens do not match"
         flattened_data = np.concatenate(data, axis=0).astype("int64")
         flattened_data = flattened_data.reshape([len(flattened_data), 1])
-        return create_lod_tensor(flattened_data, lod, place)
+        return create_lod_tensor(flattened_data, recursive_seq_lens, place)
     elif isinstance(data, np.ndarray):
         tensor = core.LoDTensor()
         tensor.set(data, place)
-        tensor.set_recursive_sequence_lengths(lod)
+        tensor.set_recursive_sequence_lengths(recursive_seq_lens)
         assert tensor.has_valid_recursive_sequence_lengths(
         ), "the provided lod info is invalid"
         return tensor
@@ -84,7 +87,8 @@ def create_lod_tensor(data, lod, place):
             "data should be either a LoDTensor, a Numpy array or a list")
 
 
-def create_random_int_lodtensor(lod, base_shape, place, low, high):
+def create_random_int_lodtensor(recursive_seq_lens, base_shape, place, low,
+                                high):
     """
     Create a LoDTensor containing random integers.
 
@@ -95,7 +99,7 @@ def create_random_int_lodtensor(lod, base_shape, place, low, high):
     The function does the following:
 
     1. Calculate the overall shape of the LoDTensor based on the length-based
-       :code:`lod` input and the shape of the basic element in
+       :code:`recursive_seq_lens` input and the shape of the basic element in
        :code:`base_shape`.
 
     2. Create a numpy array of this shape.
@@ -105,12 +109,13 @@ def create_random_int_lodtensor(lod, base_shape, place, low, high):
     Suppose we want LoDTensor to hold data for sequences of word, where each
     word is represented by an integer. If we want to create a LoDTensor to
     represent two sentences, one of 2 words, and one of 3 words. Then
-    'base_shape' is [1], input length-based 'lod' is [[2, 3]]. Then the overall
-    shape of the LoDTensor would be [5, 1], holding 5 words for two sentences.
+    'base_shape' is [1], input length-based 'recursive_seq_lens' is [[2, 3]]. 
+    Then the overall shape of the LoDTensor would be [5, 1], holding 5 words 
+    for two sentences.
 
     Args:
-        lod(list): a list of lists indicating the length-based LoD info
-            specified by the user.
+        recursive_seq_lens(list): a list of lists indicating the length-based 
+            level of detail info specified by the user.
         base_shape(list): the shape of the basic element to be held by the
             LoDTensor.
         place(Place): CPU or GPU place indicating where the data in the new
@@ -119,11 +124,11 @@ def create_random_int_lodtensor(lod, base_shape, place, low, high):
         high(int): the upper bound of the random integers.
 
     Returns:
-        A fluid LoDTensor object with tensor data and lod info. 
+        A fluid LoDTensor object with tensor data and recursive_seq_lens info. 
     """
     assert isinstance(base_shape, list), "base_shape should be a list"
     # append the total number of basic elements to the front of its shape
-    overall_shape = [sum(lod[-1])] + base_shape
+    overall_shape = [sum(recursive_seq_lens[-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)
+    return create_lod_tensor(data, recursive_seq_lens, place)

python/paddle/fluid/optimizer.py

@@ -1113,7 +1113,6 @@ class ModelAverage(Optimizer):
 
     Args:
         average_window_rate: The rate of average window.
-        params_grads: A list of parameter-grad variable pairs.
         min_average_window: The minimum size of average window.
         max_average_window: The maximum size of average window.
 
@@ -1122,8 +1121,8 @@ class ModelAverage(Optimizer):
       .. code-block:: python
 
         optimizer = fluid.optimizer.Momentum()
-        _, params_grads = optimizer.minimize(cost)
-        model_average = fluid.optimizer.ModelAverage(params_grads, 0.15,
+        optimizer.minimize(cost)
+        model_average = fluid.optimizer.ModelAverage(0.15,
                                                 min_average_window=10000,
                                                 max_average_window=20000)
         for pass_id in range(args.pass_num):
@@ -1137,7 +1136,6 @@ class ModelAverage(Optimizer):
 
     def __init__(self,
                  average_window_rate,
-                 params_grads=None,
                  min_average_window=10000,
                  max_average_window=10000,
                  **kwargs):
@@ -1146,21 +1144,16 @@ class ModelAverage(Optimizer):
         self.min_average_window = min_average_window
         self.max_average_window = max_average_window
 
-        self.params_grads = [] if params_grads is None else params_grads
-        params = {}
-        for param, grad in self.params_grads:
-            if param.do_model_average != False:
-                params[param.name] = (param, grad)
+        self.params_grads = []
         for param in framework.default_main_program().global_block(
         ).all_parameters():
-            if param.name not in params and param.do_model_average != False:
+            if param.do_model_average != False:
                 grad = param.block.create_var(
                     name=unique_name.generate(".".join([param.name, 'tmp'])),
                     dtype=param.dtype,
                     persistable=False,
                     stop_gradient=True)
-                params[param.name] = (param, grad)
-        self.params_grads = params.values()
+                self.params_grads.append((param, grad))
 
         for param, grad in self.params_grads:
             self._append_average_accumulate_op(param)

python/paddle/fluid/tests/book/high-level-api/label_semantic_roles/test_label_semantic_roles_newapi.py

@@ -206,35 +206,35 @@ def infer(use_cuda, inference_program, params_dirname):
     inferencer = fluid.Inferencer(
         inference_program, param_path=params_dirname, place=place)
 
-    # Setup inputs by creating LoDTensors to represent sequences of words.
-    # Here each word is the basic element of these LoDTensors and the shape of 
+    # Setup input by creating LoDTensor to represent sequence of words.
+    # Here each word is the basic element of the LoDTensor and the shape of 
     # each word (base_shape) should be [1] since it is simply an index to 
     # look up for the corresponding word vector.
-    # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
-    # which has only one lod level. Then the created LoDTensors will have only 
+    # Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
+    # which has only one level of detail. Then the created LoDTensor will have only 
     # one higher level structure (sequence of words, or sentence) than the basic 
     # element (word). Hence the LoDTensor will hold data for three sentences of 
     # length 3, 4 and 2, respectively. 
-    # Note that lod info should be a list of lists.
-    lod = [[3, 4, 2]]
+    # Note that recursive_sequence_lengths should be a list of lists.
+    recursive_seq_lens = [[3, 4, 2]]
     base_shape = [1]
     # The range of random integers is [low, high]
     word = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
     ctx_n2 = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
     ctx_n1 = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
     ctx_0 = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
     ctx_p1 = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
     ctx_p2 = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=WORD_DICT_LEN - 1)
     pred = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=PRED_DICT_LEN - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=PRED_DICT_LEN - 1)
     mark = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=MARK_DICT_LEN - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=MARK_DICT_LEN - 1)
 
     results = inferencer.infer(
         {

python/paddle/fluid/tests/book/high-level-api/machine_translation/test_machine_translation.py

@@ -229,11 +229,13 @@ def decode_main(use_cuda, is_sparse):
         [1. for _ in range(batch_size)], dtype='float32')
     init_ids_data = init_ids_data.reshape((batch_size, 1))
     init_scores_data = init_scores_data.reshape((batch_size, 1))
-    init_lod = [1] * batch_size
-    init_lod = [init_lod, init_lod]
+    init_recursive_seq_lens = [1] * batch_size
+    init_recursive_seq_lens = [init_recursive_seq_lens, init_recursive_seq_lens]
 
-    init_ids = fluid.create_lod_tensor(init_ids_data, init_lod, place)
-    init_scores = fluid.create_lod_tensor(init_scores_data, init_lod, place)
+    init_ids = fluid.create_lod_tensor(init_ids_data, init_recursive_seq_lens,
+                                       place)
+    init_scores = fluid.create_lod_tensor(init_scores_data,
+                                          init_recursive_seq_lens, place)
 
     train_data = paddle.batch(
         paddle.reader.shuffle(
@@ -257,7 +259,7 @@ def decode_main(use_cuda, is_sparse):
             feed=feed_dict,
             fetch_list=[translation_ids, translation_scores],
             return_numpy=False)
-        print result_ids.lod()
+        print result_ids.recursive_sequence_lengths()
         break
 
 

python/paddle/fluid/tests/book/high-level-api/recommender_system/test_recommender_system_newapi.py

@@ -209,13 +209,15 @@ def infer(use_cuda, inference_program, params_dirname):
         inference_program, param_path=params_dirname, place=place)
 
     # Use the first data from paddle.dataset.movielens.test() as input.
-    # Use create_lod_tensor(data, lod, place) API to generate LoD Tensor,
-    # where `data` is a list of sequences of index numbers, `lod` is 
-    # the level of detail (lod) info associated with `data`.
+    # Use create_lod_tensor(data, recursive_sequence_lengths, place) API 
+    # to generate LoD Tensor where `data` is a list of sequences of index 
+    # numbers, `recursive_sequence_lengths` is the length-based level of detail 
+    # (lod) info associated with `data`.
     # For example, data = [[10, 2, 3], [2, 3]] means that it contains
     # two sequences of indexes, of length 3 and 2, respectively.
-    # Correspondingly, lod = [[3, 2]] contains one level of detail info,
-    # indicating that `data` consists of two sequences of length 3 and 2. 
+    # Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one 
+    # level of detail info, indicating that `data` consists of two sequences 
+    # of length 3 and 2, respectively. 
     user_id = fluid.create_lod_tensor([[1]], [[1]], place)
     gender_id = fluid.create_lod_tensor([[1]], [[1]], place)
     age_id = fluid.create_lod_tensor([[0]], [[1]], place)

python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_conv.py

@@ -128,17 +128,17 @@ def infer(use_cuda, inference_program, params_dirname=None):
     # Here each word is the basic element of the LoDTensor and the shape of 
     # each word (base_shape) should be [1] since it is simply an index to 
     # look up for the corresponding word vector.
-    # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
-    # which has only one lod level. Then the created LoDTensor will have only 
+    # Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
+    # which has only one level of detail. Then the created LoDTensor will have only 
     # one higher level structure (sequence of words, or sentence) than the basic 
     # element (word). Hence the LoDTensor will hold data for three sentences of 
     # length 3, 4 and 2, respectively. 
-    # Note that lod info should be a list of lists.
-    lod = [[3, 4, 2]]
+    # Note that recursive_sequence_lengths should be a list of lists.
+    recursive_seq_lens = [[3, 4, 2]]
     base_shape = [1]
     # The range of random integers is [low, high]
     tensor_words = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=len(word_dict) - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=len(word_dict) - 1)
     results = inferencer.infer({'words': tensor_words})
     print("infer results: ", results)
 

python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_dynamic_rnn.py

@@ -143,17 +143,17 @@ def infer(use_cuda, inference_program, params_dirname=None):
     # Here each word is the basic element of the LoDTensor and the shape of 
     # each word (base_shape) should be [1] since it is simply an index to 
     # look up for the corresponding word vector.
-    # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
-    # which has only one lod level. Then the created LoDTensor will have only 
+    # Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
+    # which has only one level of detail. Then the created LoDTensor will have only 
     # one higher level structure (sequence of words, or sentence) than the basic 
     # element (word). Hence the LoDTensor will hold data for three sentences of 
     # length 3, 4 and 2, respectively. 
-    # Note that lod info should be a list of lists.
-    lod = [[3, 4, 2]]
+    # Note that recursive_sequence_lengths should be a list of lists.
+    recursive_seq_lens = [[3, 4, 2]]
     base_shape = [1]
     # The range of random integers is [low, high]
     tensor_words = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=len(word_dict) - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=len(word_dict) - 1)
     results = inferencer.infer({'words': tensor_words})
     print("infer results: ", results)
 

python/paddle/fluid/tests/book/high-level-api/understand_sentiment/test_understand_sentiment_stacked_lstm.py

@@ -138,17 +138,17 @@ def infer(use_cuda, inference_program, params_dirname=None):
     # Here each word is the basic element of the LoDTensor and the shape of 
     # each word (base_shape) should be [1] since it is simply an index to 
     # look up for the corresponding word vector.
-    # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
-    # which has only one lod level. Then the created LoDTensor will have only 
+    # Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
+    # which has only one level of detail. Then the created LoDTensor will have only 
     # one higher level structure (sequence of words, or sentence) than the basic 
     # element (word). Hence the LoDTensor will hold data for three sentences of 
     # length 3, 4 and 2, respectively. 
-    # Note that lod info should be a list of lists.
-    lod = [[3, 4, 2]]
+    # Note that recursive_sequence_lengths should be a list of lists.
+    recursive_seq_lens = [[3, 4, 2]]
     base_shape = [1]
     # The range of random integers is [low, high]
     tensor_words = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=len(word_dict) - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=len(word_dict) - 1)
     results = inferencer.infer({'words': tensor_words})
     print("infer results: ", results)
 

python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py

@@ -124,21 +124,22 @@ def infer(use_cuda, inference_program, params_dirname=None):
 
     # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word 
     # is simply an index to look up for the corresponding word vector and hence 
-    # the shape of word (base_shape) should be [1]. The length-based level of 
-    # detail (lod) info of each LoDtensor should be [[1]] meaning there is only 
-    # one lod_level and there is only one sequence of one word on this level.
-    # Note that lod info should be a list of lists.
-    lod = [[1]]
+    # the shape of word (base_shape) should be [1]. The recursive_sequence_lengths, 
+    # which is length-based level of detail (lod) of each LoDTensor, should be [[1]] 
+    # meaning there is only one level of detail and there is only one sequence of 
+    # one word on this level.
+    # Note that recursive_sequence_lengths should be a list of lists.
+    recursive_seq_lens = [[1]]
     base_shape = [1]
     # The range of random integers is [low, high]
     first_word = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=dict_size - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
     second_word = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=dict_size - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
     third_word = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=dict_size - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
     fourth_word = fluid.create_random_int_lodtensor(
-        lod, base_shape, place, low=0, high=dict_size - 1)
+        recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
 
     result = inferencer.infer(
         {

python/paddle/fluid/tests/book/notest_understand_sentiment.py

@@ -238,17 +238,21 @@ def infer(word_dict, use_cuda, save_dirname=None):
         # Here each word is the basic element of the LoDTensor and the shape of 
         # each word (base_shape) should be [1] since it is simply an index to 
         # look up for the corresponding word vector.
-        # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
-        # which has only one lod level. Then the created LoDTensor will have only 
+        # Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
+        # which has only one level of detail. Then the created LoDTensor will have only 
         # one higher level structure (sequence of words, or sentence) than the basic 
         # element (word). Hence the LoDTensor will hold data for three sentences of 
         # length 3, 4 and 2, respectively. 
-        # Note that lod info should be a list of lists.
-        lod = [[3, 4, 2]]
+        # Note that recursive_sequence_lengths should be a list of lists.
+        recursive_seq_lens = [[3, 4, 2]]
         base_shape = [1]
         # The range of random integers is [low, high]
         tensor_words = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=word_dict_len - 1)
+            recursive_seq_lens,
+            base_shape,
+            place,
+            low=0,
+            high=word_dict_len - 1)
 
         # Construct feed as a dictionary of {feed_target_name: feed_target_data}
         # and results will contain a list of data corresponding to fetch_targets.
@@ -257,7 +261,7 @@ def infer(word_dict, use_cuda, save_dirname=None):
                           feed={feed_target_names[0]: tensor_words},
                           fetch_list=fetch_targets,
                           return_numpy=False)
-        print(results[0].lod())
+        print(results[0].recursive_sequence_lengths())
         np_data = np.array(results[0])
         print("Inference Shape: ", np_data.shape)
         print("Inference results: ", np_data)

python/paddle/fluid/tests/book/test_label_semantic_roles.py

@@ -247,35 +247,67 @@ def infer(use_cuda, save_dirname=None):
         [inference_program, feed_target_names,
          fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
 
-        # Setup inputs by creating LoDTensors to represent sequences of words.
-        # Here each word is the basic element of these LoDTensors and the shape of 
+        # Setup input by creating LoDTensor to represent sequence of words.
+        # Here each word is the basic element of the LoDTensor and the shape of 
         # each word (base_shape) should be [1] since it is simply an index to 
         # look up for the corresponding word vector.
-        # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]],
-        # which has only one lod level. Then the created LoDTensors will have only 
+        # Suppose the recursive_sequence_lengths info is set to [[3, 4, 2]],
+        # which has only one level of detail. Then the created LoDTensor will have only 
         # one higher level structure (sequence of words, or sentence) than the basic 
         # element (word). Hence the LoDTensor will hold data for three sentences of 
         # length 3, 4 and 2, respectively. 
-        # Note that lod info should be a list of lists.
-        lod = [[3, 4, 2]]
+        # Note that recursive_sequence_lengths should be a list of lists.
+        recursive_seq_lens = [[3, 4, 2]]
         base_shape = [1]
         # The range of random integers is [low, high]
         word = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=word_dict_len - 1)
+            recursive_seq_lens,
+            base_shape,
+            place,
+            low=0,
+            high=word_dict_len - 1)
         pred = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=pred_dict_len - 1)
+            recursive_seq_lens,
+            base_shape,
+            place,
+            low=0,
+            high=pred_dict_len - 1)
         ctx_n2 = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=word_dict_len - 1)
+            recursive_seq_lens,
+            base_shape,
+            place,
+            low=0,
+            high=word_dict_len - 1)
         ctx_n1 = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=word_dict_len - 1)
+            recursive_seq_lens,
+            base_shape,
+            place,
+            low=0,
+            high=word_dict_len - 1)
         ctx_0 = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=word_dict_len - 1)
+            recursive_seq_lens,
+            base_shape,
+            place,
+            low=0,
+            high=word_dict_len - 1)
         ctx_p1 = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=word_dict_len - 1)
+            recursive_seq_lens,
+            base_shape,
+            place,
+            low=0,
+            high=word_dict_len - 1)
         ctx_p2 = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=word_dict_len - 1)
+            recursive_seq_lens,
+            base_shape,
+            place,
+            low=0,
+            high=word_dict_len - 1)
         mark = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=mark_dict_len - 1)
+            recursive_seq_lens,
+            base_shape,
+            place,
+            low=0,
+            high=mark_dict_len - 1)
 
         # Construct feed as a dictionary of {feed_target_name: feed_target_data}
         # and results will contain a list of data corresponding to fetch_targets.
@@ -301,7 +333,7 @@ def infer(use_cuda, save_dirname=None):
                           },
                           fetch_list=fetch_targets,
                           return_numpy=False)
-        print(results[0].lod())
+        print(results[0].recursive_sequence_lengths())
         np_data = np.array(results[0])
         print("Inference Shape: ", np_data.shape)
 

python/paddle/fluid/tests/book/test_machine_translation.py

@@ -108,7 +108,7 @@ def decoder_decode(context, is_sparse):
         pre_state = pd.array_read(array=state_array, i=counter)
         pre_score = pd.array_read(array=scores_array, i=counter)
 
-        # expand the lod of pre_state to be the same with pre_score
+        # expand the recursive_sequence_lengths of pre_state to be the same with pre_score
         pre_state_expanded = pd.sequence_expand(pre_state, pre_score)
 
         pre_ids_emb = pd.embedding(
@@ -252,11 +252,13 @@ def decode_main(use_cuda, is_sparse):
         [1. for _ in range(batch_size)], dtype='float32')
     init_ids_data = init_ids_data.reshape((batch_size, 1))
     init_scores_data = init_scores_data.reshape((batch_size, 1))
-    init_lod = [1] * batch_size
-    init_lod = [init_lod, init_lod]
+    init_recursive_seq_lens = [1] * batch_size
+    init_recursive_seq_lens = [init_recursive_seq_lens, init_recursive_seq_lens]
 
-    init_ids = fluid.create_lod_tensor(init_ids_data, init_lod, place)
-    init_scores = fluid.create_lod_tensor(init_scores_data, init_lod, place)
+    init_ids = fluid.create_lod_tensor(init_ids_data, init_recursive_seq_lens,
+                                       place)
+    init_scores = fluid.create_lod_tensor(init_scores_data,
+                                          init_recursive_seq_lens, place)
 
     train_data = paddle.batch(
         paddle.reader.shuffle(
@@ -280,7 +282,7 @@ def decode_main(use_cuda, is_sparse):
             feed=feed_dict,
             fetch_list=[translation_ids, translation_scores],
             return_numpy=False)
-        print result_ids.lod()
+        print result_ids.recursive_sequence_lengths()
         break
 
 

python/paddle/fluid/tests/book/test_recommender_system.py

@@ -260,13 +260,15 @@ def infer(use_cuda, save_dirname=None):
 
         # Use the first data from paddle.dataset.movielens.test() as input
         assert feed_target_names[0] == "user_id"
-        # Use create_lod_tensor(data, lod, place) API to generate LoD Tensor
-        # where `data` is a list of sequences of index numbers, `lod` is 
-        # the level of detail (lod) info associated with `data`.
+        # Use create_lod_tensor(data, recursive_sequence_lengths, place) API 
+        # to generate LoD Tensor where `data` is a list of sequences of index 
+        # numbers, `recursive_sequence_lengths` is the length-based level of detail 
+        # (lod) info associated with `data`.
         # For example, data = [[10, 2, 3], [2, 3]] means that it contains
         # two sequences of indexes, of length 3 and 2, respectively.
-        # Correspondingly, lod = [[3, 2]] contains one level of detail info,
-        # indicating that `data` consists of two sequences of length 3 and 2. 
+        # Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one 
+        # level of detail info, indicating that `data` consists of two sequences 
+        # of length 3 and 2, respectively. 
         user_id = fluid.create_lod_tensor([[1]], [[1]], place)
 
         assert feed_target_names[1] == "gender_id"

python/paddle/fluid/tests/book/test_rnn_encoder_decoder.py

@@ -216,19 +216,19 @@ def infer(use_cuda, save_dirname=None):
         # Here each word is the basic element of the LoDTensor and the shape of 
         # each word (base_shape) should be [1] since it is simply an index to 
         # look up for the corresponding word vector.
-        # Suppose the length_based level of detail (lod) info is set to [[4, 6]],
-        # which has only one lod level. Then the created LoDTensor will have only 
+        # Suppose the recursive_sequence_lengths info is set to [[4, 6]],
+        # which has only one level of detail. Then the created LoDTensor will have only 
         # one higher level structure (sequence of words, or sentence) than the basic 
         # element (word). Hence the LoDTensor will hold data for two sentences of 
         # length 4 and 6, respectively. 
-        # Note that lod info should be a list of lists.
-        lod = [[4, 6]]
+        # Note that recursive_sequence_lengths should be a list of lists.
+        recursive_seq_lens = [[4, 6]]
         base_shape = [1]
         # The range of random integers is [low, high]
         word_data = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=1)
+            recursive_seq_lens, base_shape, place, low=0, high=1)
         trg_word = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=1)
+            recursive_seq_lens, base_shape, place, low=0, high=1)
 
         # Construct feed as a dictionary of {feed_target_name: feed_target_data}
         # and results will contain a list of data corresponding to fetch_targets.
@@ -241,7 +241,7 @@ def infer(use_cuda, save_dirname=None):
                           },
                           fetch_list=fetch_targets,
                           return_numpy=False)
-        print(results[0].lod())
+        print(results[0].recursive_sequence_lengths())
         np_data = np.array(results[0])
         print("Inference shape: ", np_data.shape)
         print("Inference results: ", np_data)

python/paddle/fluid/tests/book/test_word2vec.py

@@ -168,21 +168,22 @@ def infer(use_cuda, save_dirname=None):
 
         # Setup inputs by creating 4 LoDTensors representing 4 words. Here each word 
         # is simply an index to look up for the corresponding word vector and hence 
-        # the shape of word (base_shape) should be [1]. The length-based level of 
-        # detail (lod) info of each LoDtensor should be [[1]] meaning there is only 
-        # one lod_level and there is only one sequence of one word on this level.
-        # Note that lod info should be a list of lists.
-        lod = [[1]]
+        # the shape of word (base_shape) should be [1]. The recursive_sequence_lengths, 
+        # which is length-based level of detail (lod) of each LoDTensor, should be [[1]] 
+        # meaning there is only one level of detail and there is only one sequence of 
+        # one word on this level.
+        # Note that recursive_sequence_lengths should be a list of lists.
+        recursive_seq_lens = [[1]]
         base_shape = [1]
         # The range of random integers is [low, high]
         first_word = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=dict_size - 1)
+            recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
         second_word = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=dict_size - 1)
+            recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
         third_word = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=dict_size - 1)
+            recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
         fourth_word = fluid.create_random_int_lodtensor(
-            lod, base_shape, place, low=0, high=dict_size - 1)
+            recursive_seq_lens, base_shape, place, low=0, high=dict_size - 1)
 
         assert feed_target_names[0] == 'firstw'
         assert feed_target_names[1] == 'secondw'
@@ -200,7 +201,7 @@ def infer(use_cuda, save_dirname=None):
                           },
                           fetch_list=fetch_targets,
                           return_numpy=False)
-        print(results[0].lod())
+        print(results[0].recursive_sequence_lengths())
         np_data = np.array(results[0])
         print("Inference Shape: ", np_data.shape)
 

python/paddle/fluid/tests/test_lod_tensor.py

@@ -19,18 +19,21 @@ import unittest
 
 
 class TestLoDTensor(unittest.TestCase):
-    def test_pybind_lod(self):
+    def test_pybind_recursive_seq_lens(self):
         tensor = fluid.LoDTensor()
-        lod = []
-        tensor.set_recursive_sequence_lengths(lod)
-        lod = [[], [1], [3]]
-        self.assertRaises(Exception, tensor.set_recursive_sequence_lengths, lod)
-        lod = [[0], [2], [3]]
-        self.assertRaises(Exception, tensor.set_recursive_sequence_lengths, lod)
+        recursive_seq_lens = []
+        tensor.set_recursive_sequence_lengths(recursive_seq_lens)
+        recursive_seq_lens = [[], [1], [3]]
+        self.assertRaises(Exception, tensor.set_recursive_sequence_lengths,
+                          recursive_seq_lens)
+        recursive_seq_lens = [[0], [2], [3]]
+        self.assertRaises(Exception, tensor.set_recursive_sequence_lengths,
+                          recursive_seq_lens)
 
-        lod = [[1, 2, 3]]
-        tensor.set_recursive_sequence_lengths(lod)
-        self.assertEqual(tensor.recursive_sequence_lengths(), lod)
+        recursive_seq_lens = [[1, 2, 3]]
+        tensor.set_recursive_sequence_lengths(recursive_seq_lens)
+        self.assertEqual(tensor.recursive_sequence_lengths(),
+                         recursive_seq_lens)
         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())
@@ -38,13 +41,14 @@ class TestLoDTensor(unittest.TestCase):
 
         # 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)
+        recursive_seq_lens = [[2, 3], [1, 3, 1, 2, 2]]
+        tensor.set_recursive_sequence_lengths(recursive_seq_lens)
+        self.assertEqual(tensor.recursive_sequence_lengths(),
+                         recursive_seq_lens)
         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]]
-        tensor.set_recursive_sequence_lengths(lod)
+        recursive_seq_lens = [[2, 3], [1, 3, 1, 2, 1]]
+        tensor.set_recursive_sequence_lengths(recursive_seq_lens)
         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())
@@ -52,35 +56,42 @@ class TestLoDTensor(unittest.TestCase):
     def test_create_lod_tensor(self):
         # Create LoDTensor from a list
         data = [[1, 2, 3], [3, 4]]
-        wrong_lod = [[2, 2]]
-        correct_lod = [[3, 2]]
-        self.assertRaises(AssertionError, create_lod_tensor, data, wrong_lod,
-                          fluid.CPUPlace())
-        tensor = create_lod_tensor(data, correct_lod, fluid.CPUPlace())
-        self.assertEqual(tensor.recursive_sequence_lengths(), correct_lod)
+        wrong_recursive_seq_lens = [[2, 2]]
+        correct_recursive_seq_lens = [[3, 2]]
+        self.assertRaises(AssertionError, create_lod_tensor, data,
+                          wrong_recursive_seq_lens, fluid.CPUPlace())
+        tensor = create_lod_tensor(data, correct_recursive_seq_lens,
+                                   fluid.CPUPlace())
+        self.assertEqual(tensor.recursive_sequence_lengths(),
+                         correct_recursive_seq_lens)
 
         # Create LoDTensor from numpy array
         data = np.random.random([10, 1])
-        lod = [[2, 1], [3, 3, 4]]
-        tensor = create_lod_tensor(data, lod, fluid.CPUPlace())
-        self.assertEqual(tensor.recursive_sequence_lengths(), lod)
+        recursive_seq_lens = [[2, 1], [3, 3, 4]]
+        tensor = create_lod_tensor(data, recursive_seq_lens, fluid.CPUPlace())
+        self.assertEqual(tensor.recursive_sequence_lengths(),
+                         recursive_seq_lens)
 
         # 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.recursive_sequence_lengths(), lod)
-        self.assertEqual(new_tensor.recursive_sequence_lengths(), new_lod)
+        new_recursive_seq_lens = [[2, 2, 1], [1, 2, 2, 3, 2]]
+        new_tensor = create_lod_tensor(tensor, new_recursive_seq_lens,
+                                       fluid.CPUPlace())
+        self.assertEqual(tensor.recursive_sequence_lengths(),
+                         recursive_seq_lens)
+        self.assertEqual(new_tensor.recursive_sequence_lengths(),
+                         new_recursive_seq_lens)
 
     def test_create_random_int_lodtensor(self):
         # The shape of a word, commonly used in speech and NLP problem, is [1]
         shape = [1]
-        lod = [[2, 3, 5]]
+        recursive_seq_lens = [[2, 3, 5]]
         dict_size = 10000
         low = 0
         high = dict_size - 1
-        tensor = create_random_int_lodtensor(lod, shape,
+        tensor = create_random_int_lodtensor(recursive_seq_lens, shape,
                                              fluid.CPUPlace(), low, high)
-        self.assertEqual(tensor.recursive_sequence_lengths(), lod)
+        self.assertEqual(tensor.recursive_sequence_lengths(),
+                         recursive_seq_lens)
         self.assertEqual(tensor.shape(), [10, 1])
 
 

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

@@ -401,7 +401,7 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(output)
         print(str(program))
 
-    def test_maxout(self):
+    def test_crop(self):
         program = Program()
         with program_guard(program):
             x = layers.data(name='x', shape=[3, 5], dtype="float32")
@@ -410,6 +410,15 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(output)
         print(str(program))
 
+    def test_mean_iou(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[16], dtype='float32')
+            y = layers.data(name='label', shape=[1], dtype='int64')
+            iou = layers.mean_iou(x, y, 2)
+            self.assertIsNotNone(iou)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()