Merge pull request #10886 from kexinzhao/label_semantic_roles_lod

Simplify label_semantic_roles book example using new LoDTensor API

Author: kexinzhao

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

@@ -202,24 +202,35 @@ def infer(use_cuda, inference_program, save_path):
  inferencer = fluid.Inferencer(
  inference_program, param_path=save_path, place=place)
 
- def create_random_lodtensor(lod, place, low, high):
- data = np.random.random_integers(low, high,
- [lod[-1], 1]).astype("int64")
- res = fluid.LoDTensor()
- res.set(data, place)
- res.set_lod([lod])
- return res
-
- # Create an input example
- lod = [0, 4, 10]
- word = create_random_lodtensor(lod, place, low=0, high=WORD_DICT_LEN - 1)
- pred = create_random_lodtensor(lod, place, low=0, high=PRED_DICT_LEN - 1)
- ctx_n2 = create_random_lodtensor(lod, place, low=0, high=WORD_DICT_LEN - 1)
- ctx_n1 = create_random_lodtensor(lod, place, low=0, high=WORD_DICT_LEN - 1)
- ctx_0 = create_random_lodtensor(lod, place, low=0, high=WORD_DICT_LEN - 1)
- ctx_p1 = create_random_lodtensor(lod, place, low=0, high=WORD_DICT_LEN - 1)
- ctx_p2 = create_random_lodtensor(lod, place, low=0, high=WORD_DICT_LEN - 1)
- mark = create_random_lodtensor(lod, place, low=0, high=MARK_DICT_LEN - 1)
+ # Setup inputs by creating LoDTensors to represent sequences of words.
+ # Here each word is the basic element of these LoDTensors 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 
+ # 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]]
+ 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)
+ pred = fluid.create_random_int_lodtensor(
+ lod, 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)
+ ctx_n1 = fluid.create_random_int_lodtensor(
+ lod, 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)
+ ctx_p1 = fluid.create_random_int_lodtensor(
+ lod, 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)
+ mark = fluid.create_random_int_lodtensor(
+ lod, base_shape, place, low=0, high=MARK_DICT_LEN - 1)
 
  results = inferencer.infer(
  {

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

@@ -116,29 +116,6 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
  return feature_out
 
 
-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])
- res = fluid.LoDTensor()
- res.set(flattened_data, place)
- res.set_lod([lod])
- return res
-
-
-def create_random_lodtensor(lod, place, low, high):
- data = np.random.random_integers(low, high, [lod[-1], 1]).astype("int64")
- res = fluid.LoDTensor()
- res.set(data, place)
- res.set_lod([lod])
- return res
-
-
 def train(use_cuda, save_dirname=None, is_local=True):
  # define network topology
  word = fluid.layers.data(
@@ -271,23 +248,35 @@ def infer(use_cuda, save_dirname=None):
  [inference_program, feed_target_names,
  fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
 
- lod = [0, 4, 10]
- word = create_random_lodtensor(
- lod, place, low=0, high=word_dict_len - 1)
- pred = create_random_lodtensor(
- lod, place, low=0, high=pred_dict_len - 1)
- ctx_n2 = create_random_lodtensor(
- lod, place, low=0, high=word_dict_len - 1)
- ctx_n1 = create_random_lodtensor(
- lod, place, low=0, high=word_dict_len - 1)
- ctx_0 = create_random_lodtensor(
- lod, place, low=0, high=word_dict_len - 1)
- ctx_p1 = create_random_lodtensor(
- lod, place, low=0, high=word_dict_len - 1)
- ctx_p2 = create_random_lodtensor(
- lod, place, low=0, high=word_dict_len - 1)
- mark = create_random_lodtensor(
- lod, place, low=0, high=mark_dict_len - 1)
+ # Setup inputs by creating LoDTensors to represent sequences of words.
+ # Here each word is the basic element of these LoDTensors 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 
+ # 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]]
+ 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)
+ pred = fluid.create_random_int_lodtensor(
+ lod, 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)
+ ctx_n1 = fluid.create_random_int_lodtensor(
+ lod, 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)
+ ctx_p1 = fluid.create_random_int_lodtensor(
+ lod, 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)
+ mark = fluid.create_random_int_lodtensor(
+ lod, 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.