update api to 1.8 and update readme (#4609)

* update api to 1.8 for transformer and similarity_net, test=develop

* update readme, test=develop

PaddleNLP/machine_translation/transformer/README.md

@@ -32,7 +32,7 @@
 
 1. paddle安装
 
-   本项目依赖于 PaddlePaddle 1.6及以上版本或适当的develop版本，请参考 [安装指南](http://www.paddlepaddle.org/#quick-start) 进行安装
+   本项目依赖于 PaddlePaddle 1.8及以上版本或适当的develop版本，请参考 [安装指南](https://www.paddlepaddle.org.cn/install/quick) 进行安装
 
 2. 下载代码
 
@@ -44,7 +44,7 @@
 
 3. 环境依赖
 
-   请参考PaddlePaddle[安装说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.5/beginners_guide/install/index_cn.html)部分的内容
+   请参考PaddlePaddle[安装说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/index_cn.html)部分的内容
 
 
 ### 数据准备

PaddleNLP/machine_translation/transformer/transformer.py

@@ -752,18 +752,17 @@ def fast_decode(model_input, src_vocab_size, trg_vocab_size, max_in_len,
         # caches contains states of history steps in decoder self-attention
         # and static encoder output projections in encoder-decoder attention
         # to reduce redundant computation.
+        batch_size = layers.shape(start_tokens)[0]
         caches = [
             {
                 "k":  # for self attention
-                layers.fill_constant_batch_size_like(
-                    input=start_tokens,
-                    shape=[-1, n_head, 0, d_key],
+                layers.fill_constant(
+                    shape=[batch_size, n_head, 0, d_key],
                     dtype=enc_output.dtype,
                     value=0),
                 "v":  # for self attention
-                layers.fill_constant_batch_size_like(
-                    input=start_tokens,
-                    shape=[-1, n_head, 0, d_value],
+                layers.fill_constant(
+                    shape=[batch_size, n_head, 0, d_value],
                     dtype=enc_output.dtype,
                     value=0),
                 "static_k":  # for encoder-decoder attention
@@ -792,12 +791,10 @@ def fast_decode(model_input, src_vocab_size, trg_vocab_size, max_in_len,
                 lambda x: layers.gather(x, index=gather_idx), caches)
             pre_src_attn_bias = layers.gather(
                 trg_src_attn_bias, index=gather_idx)
+            bias_batch_size = layers.shape(pre_src_attn_bias)[0]
             pre_pos = layers.elementwise_mul(
-                x=layers.fill_constant_batch_size_like(
-                    input=pre_src_attn_bias,  # cann't use lod tensor here
-                    value=1,
-                    shape=[-1, 1],
-                    dtype=pre_ids.dtype),
+                x=layers.fill_constant(
+                    value=1, shape=[bias_batch_size, 1], dtype=pre_ids.dtype),
                 y=step_idx,
                 axis=0)
             logits = wrap_decoder(

PaddleNLP/shared_modules/models/matching/paddle_layers.py

@@ -210,7 +210,7 @@ class DataLayer(object):
         """
         operation
         """
-        data = fluid.layers.data(
+        data = fluid.data(
             name=name, shape=shape, dtype=dtype, lod_level=lod_level)
         return data
 
@@ -383,8 +383,10 @@ class ConstantLayer(object):
         """
         operation
         """
-        constant = fluid.layers.fill_constant_batch_size_like(input, shape,
-                                                              dtype, value)
+        shape = list(shape)
+        input_shape = fluid.layers.shape(input)
+        shape[0] = input_shape[0]
+        constant = fluid.layers.fill_constant(shape, dtype, value)
         return constant
 
 

PaddleNLP/similarity_net/README.md

@@ -22,7 +22,7 @@
 |UNICOM|联通客服|客服|
 ## 快速开始
 #### 版本依赖
-本项目依赖于 Paddlepaddle Fluid 1.6，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装。
+本项目依赖于 Paddlepaddle Fluid 1.8，请参考[安装指南](https://www.paddlepaddle.org.cn/install/quick)进行安装。
 
 python版本依赖python 2.7
 #### 安装代码

PaddleNLP/similarity_net/run_classifier.py

@@ -47,46 +47,51 @@ from models.model_check import check_version
 from models.model_check import check_cuda
 
 
-def create_model(args, pyreader_name, is_inference=False, is_pointwise=False):
+def create_model(args, is_inference=False, is_pointwise=False):
     """
     Create Model for simnet
     """
     if is_inference:
-        inf_pyreader = fluid.layers.py_reader(
+        left = fluid.data(name='left', shape=[None], dtype='int64', lod_level=1)
+        pos_right = fluid.data(
+            name='pos_right', shape=[None], dtype='int64', lod_level=1)
+        inf_loader = fluid.io.DataLoader.from_generator(
             capacity=16,
-        shapes=([-1], [-1]),
-        dtypes=('int64', 'int64'),
-        lod_levels=(1, 1),
-        name=pyreader_name,
+            feed_list=[left, pos_right],
+            iterable=False,
             use_double_buffer=False)
 
-        left, pos_right = fluid.layers.read_file(inf_pyreader)
-        return inf_pyreader, left, pos_right
+        return inf_loader, left, pos_right
 
     else:
         if is_pointwise:
-            pointwise_pyreader = fluid.layers.py_reader(
+            left = fluid.data(
+                name='left', shape=[None], dtype='int64', lod_level=1)
+            right = fluid.data(
+                name='right', shape=[None], dtype='int64', lod_level=1)
+            label = fluid.data(name='label', shape=[None], dtype='int64')
+            pointwise_loader = fluid.io.DataLoader.from_generator(
                 capacity=16,
-            shapes=([-1], [-1], [-1]),
-            dtypes=('int64', 'int64', 'int64'),
-            lod_levels=(1, 1, 0),
-            name=pyreader_name,
+                feed_list=[left, right, label],
+                iterable=False,
                 use_double_buffer=False)
 
-            left, right, label = fluid.layers.read_file(pointwise_pyreader)
-            return pointwise_pyreader, left, right, label
+            return pointwise_loader, left, right, label
 
         else:
-            pairwise_pyreader = fluid.layers.py_reader(
+            left = fluid.data(
+                name='left', shape=[None], dtype='int64', lod_level=1)
+            pos_right = fluid.data(
+                name='pos_right', shape=[None], dtype='int64', lod_level=1)
+            neg_right = fluid.data(
+                name='neg_right', shape=[None], dtype='int64', lod_level=1)
+            pairwise_loader = fluid.io.DataLoader.from_generator(
                 capacity=16,
-            shapes=([-1], [-1], [-1]),
-            dtypes=('int64', 'int64', 'int64'),
-            lod_levels=(1, 1, 1),
-            name=pyreader_name,
+                feed_list=[left, pos_right, neg_right],
+                iterable=False,
                 use_double_buffer=False)
 
-            left, pos_right, neg_right = fluid.layers.read_file(pairwise_pyreader)
-            return pairwise_pyreader, left, pos_right, neg_right
+            return pairwise_loader, left, pos_right, neg_right
 
 
 def train(conf_dict, args):
@@ -131,8 +136,7 @@ def train(conf_dict, args):
         # Build network
         with fluid.program_guard(train_program, startup_prog):
             with fluid.unique_name.guard():
-                train_pyreader, left, pos_right, neg_right = create_model(
-                    args, pyreader_name='train_reader')
+                train_loader, left, pos_right, neg_right = create_model(args)
                 left_feat, pos_score = net.predict(left, pos_right)
                 pred = pos_score
                 _, neg_score = net.predict(left, neg_right)
@@ -147,8 +151,8 @@ def train(conf_dict, args):
             test_prog = fluid.Program()
             with fluid.program_guard(test_prog, startup_prog):
                 with fluid.unique_name.guard():
-                    test_pyreader, left, pos_right = create_model(
-                        args, pyreader_name='test_reader', is_inference=True)
+                    test_loader, left, pos_right = create_model(
+                        args, is_inference=True)
                     left_feat, pos_score = net.predict(left, pos_right)
                     pred = pos_score
             test_prog = test_prog.clone(for_test=True)
@@ -157,8 +161,8 @@ def train(conf_dict, args):
         # Build network
         with fluid.program_guard(train_program, startup_prog):
             with fluid.unique_name.guard():
-                train_pyreader, left, right, label = create_model(
-                    args, pyreader_name='train_reader', is_pointwise=True)
+                train_loader, left, right, label = create_model(
+                    args, is_pointwise=True)
                 left_feat, pred = net.predict(left, right)
                 avg_cost = loss.compute(pred, label)
                 avg_cost.persistable = True
@@ -171,15 +175,15 @@ def train(conf_dict, args):
             test_prog = fluid.Program()
             with fluid.program_guard(test_prog, startup_prog):
                 with fluid.unique_name.guard():
-                    test_pyreader, left, right = create_model(
-                        args, pyreader_name='test_reader', is_inference=True)
+                    test_loader, left, right = create_model(
+                        args, is_inference=True)
                     left_feat, pred = net.predict(left, right)
             test_prog = test_prog.clone(for_test=True)
 
     if args.init_checkpoint is not "":
         utils.init_checkpoint(exe, args.init_checkpoint, startup_prog)
 
-    def valid_and_test(test_program, test_pyreader, get_valid_examples, process,
+    def valid_and_test(test_program, test_loader, get_valid_examples, process,
                        mode, exe, fetch_list):
         """
         return auc and acc
@@ -187,15 +191,15 @@ def train(conf_dict, args):
         # Get Batch Data
         batch_data = fluid.io.batch(
             get_valid_examples, args.batch_size, drop_last=False)
-        test_pyreader.decorate_paddle_reader(batch_data)
-        test_pyreader.start()
+        test_loader.set_sample_list_generator(batch_data)
+        test_loader.start()
         pred_list = []
         while True:
             try:
                 _pred = exe.run(program=test_program, fetch_list=[pred.name])
                 pred_list += list(_pred)
             except fluid.core.EOFException:
-                test_pyreader.reset()
+                test_loader.reset()
                 break
         pred_list = np.vstack(pred_list)
         if mode == "test":
@@ -233,8 +237,8 @@ def train(conf_dict, args):
                 get_train_examples, buf_size=10000),
             args.batch_size,
             drop_last=False)
-    train_pyreader.decorate_paddle_reader(train_batch_data)
-    train_pyreader.start()
+    train_loader.set_sample_list_generator(train_batch_data)
+    train_loader.start()
     exe.run(startup_prog)
     losses = []
     start_time = time.time()
@@ -248,8 +252,8 @@ def train(conf_dict, args):
             if args.do_valid and global_step % args.validation_steps == 0:
                 get_valid_examples = simnet_process.get_reader("valid")
                 valid_result = valid_and_test(
-                    test_prog, test_pyreader, get_valid_examples,
-                    simnet_process, "valid", exe, [pred.name])
+                    test_prog, test_loader, get_valid_examples, simnet_process,
+                    "valid", exe, [pred.name])
                 if args.compute_accuracy:
                     valid_auc, valid_acc = valid_result
                     logging.info(
@@ -281,7 +285,7 @@ def train(conf_dict, args):
                 logging.info("saving infer model in %s" % model_path)
 
         except fluid.core.EOFException:
-            train_pyreader.reset()
+            train_loader.reset()
             break
     end_time = time.time()
     #logging.info("epoch: %d, loss: %f, used time: %d sec" %
@@ -327,9 +331,8 @@ def train(conf_dict, args):
         else:
             # Get Feeder and Reader
             get_test_examples = simnet_process.get_reader("test")
-        test_result = valid_and_test(test_prog, test_pyreader,
-                                     get_test_examples, simnet_process, "test",
-                                     exe, [pred.name])
+        test_result = valid_and_test(test_prog, test_loader, get_test_examples,
+                                     simnet_process, "test", exe, [pred.name])
         if args.compute_accuracy:
             test_auc, test_acc = test_result
             logging.info("AUC of test is %f, Accuracy of test is %f" %
@@ -371,8 +374,8 @@ def test(conf_dict, args):
         if args.task_mode == "pairwise":
             with fluid.program_guard(test_prog, startup_prog):
                 with fluid.unique_name.guard():
-                    test_pyreader, left, pos_right = create_model(
-                        args, pyreader_name='test_reader', is_inference=True)
+                    test_loader, left, pos_right = create_model(
+                        args, is_inference=True)
                     left_feat, pos_score = net.predict(left, pos_right)
                     pred = pos_score
             test_prog = test_prog.clone(for_test=True)
@@ -380,8 +383,8 @@ def test(conf_dict, args):
         else:
             with fluid.program_guard(test_prog, startup_prog):
                 with fluid.unique_name.guard():
-                    test_pyreader, left, right = create_model(
-                        args, pyreader_name='test_reader', is_inference=True)
+                    test_loader, left, right = create_model(
+                        args, is_inference=True)
                     left_feat, pred = net.predict(left, right)
             test_prog = test_prog.clone(for_test=True)
 
@@ -390,10 +393,10 @@ def test(conf_dict, args):
         utils.init_checkpoint(exe, args.init_checkpoint, main_program=test_prog)
 
         test_exe = exe
-        test_pyreader.decorate_paddle_reader(batch_data)
+        test_loader.set_sample_list_generator(batch_data)
 
         logging.info("start test process ...")
-        test_pyreader.start()
+        test_loader.start()
         pred_list = []
         fetch_list = [pred.name]
         output = []
@@ -412,7 +415,7 @@ def test(conf_dict, args):
                         map(lambda item: str(np.argmax(item)), output[0])) +
                                            "\n")
             except fluid.core.EOFException:
-                test_pyreader.reset()
+                test_loader.reset()
                 break
         if args.task_mode == "pairwise":
             pred_list = np.array(pred_list).reshape((-1, 1))
@@ -468,16 +471,16 @@ def infer(conf_dict, args):
     if args.task_mode == "pairwise":
         with fluid.program_guard(test_prog, startup_prog):
             with fluid.unique_name.guard():
-                infer_pyreader, left, pos_right = create_model(
-                    args, pyreader_name='infer_reader', is_inference=True)
+                infer_loader, left, pos_right = create_model(
+                    args, is_inference=True)
                 left_feat, pos_score = net.predict(left, pos_right)
                 pred = pos_score
         test_prog = test_prog.clone(for_test=True)
     else:
         with fluid.program_guard(test_prog, startup_prog):
             with fluid.unique_name.guard():
-                infer_pyreader, left, right = create_model(
-                    args, pyreader_name='infer_reader', is_inference=True)
+                infer_loader, left, right = create_model(
+                    args, is_inference=True)
                 left_feat, pred = net.predict(left, right)
         test_prog = test_prog.clone(for_test=True)
 
@@ -486,13 +489,13 @@ def infer(conf_dict, args):
     utils.init_checkpoint(exe, args.init_checkpoint, main_program=test_prog)
 
     test_exe = exe
-    infer_pyreader.decorate_sample_list_generator(batch_data)
+    infer_loader.set_sample_list_generator(batch_data)
 
     logging.info("start test process ...")
     preds_list = []
     fetch_list = [pred.name]
     output = []
-    infer_pyreader.start()
+    infer_loader.start()
     while True:
         try:
             output = test_exe.run(program=test_prog, fetch_list=fetch_list)
@@ -502,7 +505,7 @@ def infer(conf_dict, args):
             else:
                 preds_list += map(lambda item: str(np.argmax(item)), output[0])
         except fluid.core.EOFException:
-            infer_pyreader.reset()
+            infer_loader.reset()
             break
     with io.open(args.infer_result_path, "w", encoding="utf8") as infer_file:
         for _data, _pred in zip(simnet_process.get_infer_data(), preds_list):

dygraph/similarity_net/README.md

@@ -23,7 +23,7 @@
 ## 快速开始
 #### 版本依赖
 
-本项目依赖于 Paddlepaddle Fluid 1.7，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装。
+本项目依赖于 Paddlepaddle Fluid 1.8，请参考[安装指南](https://www.paddlepaddle.org.cn/install/quick)进行安装。
 
 
 #### 安装代码

dygraph/similarity_net/model_check.py

@@ -33,6 +33,7 @@ def check_cuda(use_cuda, err = \
     except Exception as e:
         pass
 
+
 def check_version():
     """
         Log error and exit when the installed version of paddlepaddle is
@@ -43,7 +44,7 @@ def check_version():
         "Please make sure the version is good with your code." \
 
     try:
-            fluid.require_version('1.6.0')
+        fluid.require_version('1.8.0')
     except Exception as e:
         print(err)
         sys.exit(1)
@@ -59,7 +60,7 @@ def check_version():
         "Please make sure the version is good with your code." \
 
     try:
-        fluid.require_version('1.6.0')
+        fluid.require_version('1.8.0')
     except Exception as e:
         print(err)
         sys.exit(1)

dygraph/similarity_net/nets/paddle_layers.py

@@ -30,6 +30,7 @@ import paddle.fluid.layers.utils as utils
 from paddle.fluid.dygraph import Embedding, Conv2D, GRUUnit, Layer, to_variable
 from paddle.fluid.layers.utils import map_structure, flatten, pack_sequence_as
 
+
 class EmbeddingLayer(object):
     """
     Embedding Layer class
@@ -52,12 +53,12 @@ class EmbeddingLayer(object):
             size=[self.dict_size, self.emb_dim],
             is_sparse=True,
             padding_idx=self.padding_idx,
-            param_attr=attr.ParamAttr(name=self.name, initializer=fluid.initializer.Xavier()))
+            param_attr=attr.ParamAttr(
+                name=self.name, initializer=fluid.initializer.Xavier()))
 
         return emb
 
 
-
 class FCLayer(object):
     """
     Fully Connect Layer class
@@ -117,11 +118,13 @@ class DynamicLSTMLayer(object):
         self.lstm_dim = lstm_dim
         self.name = name
         self.is_reverse = is_reverse
+
     def ops(self):
         """
         operation
         """
-        lstm_cell = BasicLSTMUnit(hidden_size=self.lstm_dim, input_size=self.lstm_dim*4)
+        lstm_cell = BasicLSTMUnit(
+            hidden_size=self.lstm_dim, input_size=self.lstm_dim * 4)
         lstm = RNN(cell=lstm_cell, time_major=True, is_reverse=self.is_reverse)
         return lstm
 
@@ -141,7 +144,7 @@ class DataLayer(object):
         """
         operation
         """
-        data = fluid.layers.data( 
+        data = fluid.data(
             name=name, shape=shape, dtype=dtype, lod_level=lod_level)
         return data
 
@@ -314,8 +317,10 @@ class ConstantLayer(object):
         """
         operation
         """
-        constant = fluid.layers.fill_constant_batch_size_like(input, shape,
-                                                              dtype, value)
+        shape = list(shape)
+        input_shape = fluid.layers.shape(input)
+        shape[0] = input_shape[0]
+        constant = fluid.layers.fill_constant(shape, dtype, value)
         return constant
 
 
@@ -358,14 +363,10 @@ class SoftsignLayer(object):
 
 
 class SimpleConvPool(Layer):
-    def __init__(self,
-                 num_channels,
-                 num_filters,
-                 filter_size,
-                 use_cudnn=False
-                 ):
+    def __init__(self, num_channels, num_filters, filter_size, use_cudnn=False):
         super(SimpleConvPool, self).__init__()
-        self._conv2d = Conv2D(num_channels = num_channels,
+        self._conv2d = Conv2D(
+            num_channels=num_channels,
             num_filters=num_filters,
             filter_size=filter_size,
             padding=[1, 1],
@@ -378,6 +379,7 @@ class SimpleConvPool(Layer):
         x = fluid.layers.reshape(x, shape=[x.shape[0], -1])
         return x
 
+
 class FC(Layer):
     """
     This interface is used to construct a callable object of the ``FC`` class.
@@ -580,7 +582,7 @@ class DynamicGRU(Layer):
                  gate_activation='sigmoid',
                  candidate_activation='tanh',
                  origin_mode=False,
-                 init_size = None):
+                 init_size=None):
         super(DynamicGRU, self).__init__()
         self.gru_unit = GRUUnit(
             size * 3,
@@ -591,16 +593,19 @@ class DynamicGRU(Layer):
             origin_mode=origin_mode)
         self.size = size
         self.is_reverse = is_reverse
+
     def forward(self, inputs, h_0):
         hidden = h_0
         res = []
         for i in range(inputs.shape[1]):
             if self.is_reverse:
                 i = inputs.shape[1] - 1 - i
-            input_ = inputs[ :, i:i+1, :]
-            input_ = fluid.layers.reshape(input_, [-1, input_.shape[2]], inplace=False)
+            input_ = inputs[:, i:i + 1, :]
+            input_ = fluid.layers.reshape(
+                input_, [-1, input_.shape[2]], inplace=False)
             hidden, reset, gate = self.gru_unit(input_, hidden)
-            hidden_ = fluid.layers.reshape(hidden, [-1, 1, hidden.shape[1]], inplace=False)
+            hidden_ = fluid.layers.reshape(
+                hidden, [-1, 1, hidden.shape[1]], inplace=False)
             res.append(hidden_)
         if self.is_reverse:
             res = res[::-1]
@@ -786,10 +791,13 @@ class BasicLSTMUnit(RNNUnit):
 
         self._weight = self.create_parameter(
             attr=self._param_attr,
-            shape=[self._input_size + self._hidden_size, 4 * self._hidden_size],
+            shape=[
+                self._input_size + self._hidden_size, 4 * self._hidden_size
+            ],
             dtype=self._dtype)
 
-        self._bias = self.create_parameter(attr=self._bias_attr,
+        self._bias = self.create_parameter(
+            attr=self._bias_attr,
             shape=[4 * self._hidden_size],
             dtype=self._dtype,
             is_bias=True)
@@ -817,11 +825,7 @@ class BasicLSTMUnit(RNNUnit):
 
 
 class RNN(Layer):
-    def __init__(self,
-                 cell,
-                 is_reverse=False,
-                 time_major=False,
-                 **kwargs):
+    def __init__(self, cell, is_reverse=False, time_major=False, **kwargs):
         super(RNN, self).__init__()
         self.cell = cell
         if not hasattr(self.cell, "call"):
@@ -831,12 +835,17 @@ class RNN(Layer):
         self.batch_index, self.time_step_index = (1, 0) if time_major else (0,
                                                                             1)
 
-    def forward(self, inputs, initial_states=None, sequence_length=None, **kwargs):
+    def forward(self,
+                inputs,
+                initial_states=None,
+                sequence_length=None,
+                **kwargs):
         if fluid.in_dygraph_mode():
 
             class OutputArray(object):
                 def __init__(self, x):
                     self.array = [x]
+
                 def append(self, x):
                     self.array.append(x)
 
@@ -844,9 +853,8 @@ class RNN(Layer):
                 # TODO: use where_op
                 new_state = fluid.layers.elementwise_mul(
                     new_state, step_mask,
-                    axis=0) - fluid.layers.elementwise_mul(state,
-                                                           (step_mask - 1),
-                                                           axis=0)
+                    axis=0) - fluid.layers.elementwise_mul(
+                        state, (step_mask - 1), axis=0)
                 return new_state
 
             flat_inputs = flatten(inputs)
@@ -872,16 +880,20 @@ class RNN(Layer):
 
             if self.is_reverse:
                 inputs = map_structure(lambda x: fluid.layers.reverse(x, axis=[0]), inputs)
-                mask = fluid.layers.reverse(mask, axis=[0]) if sequence_length is not None else None
+                mask = fluid.layers.reverse(
+                    mask, axis=[0]) if sequence_length is not None else None
 
             states = initial_states
             outputs = []
             for i in range(time_steps):
-                step_inputs = map_structure(lambda x:x[i], inputs)
-                step_outputs, new_states = self.cell(step_inputs, states, **kwargs)
+                step_inputs = map_structure(lambda x: x[i], inputs)
+                step_outputs, new_states = self.cell(step_inputs, states,
+                                                     **kwargs)
                 if sequence_length is not None:
                     new_states = map_structure(
-                        partial(_maybe_copy, step_mask=mask[i]), states,
+                        partial(
+                            _maybe_copy, step_mask=mask[i]),
+                        states,
                         new_states)
                 states = new_states
                 if i == 0:
@@ -922,10 +934,9 @@ class EncoderCell(RNNUnit):
         self.lstm_cells = list()
         for i in range(self.num_layers):
             self.lstm_cells.append(
-                self.add_sublayer(
-                    "layer_%d" % i,
-                    BasicLSTMUnit(input_size if i == 0 else hidden_size,
-                                  hidden_size)))
+                self.add_sublayer("layer_%d" % i,
+                                  BasicLSTMUnit(input_size if i == 0 else
+                                                hidden_size, hidden_size)))
 
     def forward(self, step_input, states):
         new_states = []
@@ -1040,4 +1051,3 @@ class BasicGRUUnit(Layer):
         new_hidden = u * pre_hidden + (1 - u) * c
 
         return new_hidden
-

dygraph/similarity_net/run_classifier.py

@@ -85,7 +85,6 @@ def train(conf_dict, args):
         else:
             return auc
 
-
     with fluid.dygraph.guard(place):
         # used for continuous evaluation 
         if args.enable_ce:
@@ -100,8 +99,7 @@ def train(conf_dict, args):
         conf_dict['seq_len'] = args.seq_len
 
         # Load network structure dynamically
-        net = utils.import_class("./nets",
-                                conf_dict["net"]["module_name"],
+        net = utils.import_class("./nets", conf_dict["net"]["module_name"],
                                  conf_dict["net"]["class_name"])(conf_dict)
         if args.init_checkpoint is not "":
             model, _ = load_dygraph(args.init_checkpoint)
@@ -113,9 +111,10 @@ def train(conf_dict, args):
         # Load Optimization method
         learning_rate = conf_dict["optimizer"]["learning_rate"]
         optimizer_name = conf_dict["optimizer"]["class_name"]
-        if optimizer_name=='SGDOptimizer':
-            optimizer = fluid.optimizer.SGDOptimizer(learning_rate,parameter_list=net.parameters())
-        elif optimizer_name=='AdamOptimizer':
+        if optimizer_name == 'SGDOptimizer':
+            optimizer = fluid.optimizer.SGDOptimizer(
+                learning_rate, parameter_list=net.parameters())
+        elif optimizer_name == 'AdamOptimizer':
             beta1 = conf_dict["optimizer"]["beta1"]
             beta2 = conf_dict["optimizer"]["beta2"]
             epsilon = conf_dict["optimizer"]["epsilon"]
@@ -136,22 +135,32 @@ def train(conf_dict, args):
         losses = []
         start_time = time.time()
 
-        train_pyreader = fluid.io.PyReader(capacity=16, return_list=True, use_double_buffer=True)
-        get_train_examples = simnet_process.get_reader("train",epoch=args.epoch)
-        train_pyreader.decorate_sample_list_generator(
-                paddle.batch(get_train_examples, batch_size=args.batch_size),
-                place)
+        train_loader = fluid.io.DataLoader.from_generator(
+            capacity=16,
+            return_list=True,
+            iterable=True,
+            use_double_buffer=True)
+        get_train_examples = simnet_process.get_reader(
+            "train", epoch=args.epoch)
+        train_loader.set_sample_list_generator(
+            paddle.batch(
+                get_train_examples, batch_size=args.batch_size), place)
         if args.do_valid:
-            valid_pyreader = fluid.io.PyReader(capacity=16, return_list=True, use_double_buffer=True)
+            valid_loader = fluid.io.DataLoader.from_generator(
+                capacity=16,
+                return_list=True,
+                iterable=True,
+                use_double_buffer=True)
             get_valid_examples = simnet_process.get_reader("valid")
-            valid_pyreader.decorate_sample_list_generator(
-                paddle.batch(get_valid_examples, batch_size=args.batch_size),
+            valid_loader.set_sample_list_generator(
+                paddle.batch(
+                    get_valid_examples, batch_size=args.batch_size),
                 place)
             pred_list = []
 
         if args.task_mode == "pairwise":
 
-            for left, pos_right, neg_right in train_pyreader():
+            for left, pos_right, neg_right in train_loader():
 
                 left = fluid.layers.reshape(left, shape=[-1, 1])
                 pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
@@ -168,23 +177,27 @@ def train(conf_dict, args):
                 net.clear_gradients()
 
                 if args.do_valid and global_step % args.validation_steps == 0:
-                    for left, pos_right in valid_pyreader():
+                    for left, pos_right in valid_loader():
                         left = fluid.layers.reshape(left, shape=[-1, 1])
-                        pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
+                        pos_right = fluid.layers.reshape(
+                            pos_right, shape=[-1, 1])
                         net.eval()
                         left_feat, pos_score = net(left, pos_right)
                         pred = pos_score
 
                         pred_list += list(pred.numpy())
-                    valid_result = valid_and_test(pred_list, simnet_process, "valid")     
+                    valid_result = valid_and_test(pred_list, simnet_process,
+                                                  "valid")
                     if args.compute_accuracy:
                         valid_auc, valid_acc = valid_result
                         logging.info(
-                            "global_steps: %d, valid_auc: %f, valid_acc: %f, valid_loss: %f" %
-                            (global_step, valid_auc, valid_acc, np.mean(losses)))
+                            "global_steps: %d, valid_auc: %f, valid_acc: %f, valid_loss: %f"
+                            % (global_step, valid_auc, valid_acc,
+                               np.mean(losses)))
                     else:
                         valid_auc = valid_result
-                        logging.info("global_steps: %d, valid_auc: %f, valid_loss: %f" %
+                        logging.info(
+                            "global_steps: %d, valid_auc: %f, valid_loss: %f" %
                             (global_step, valid_auc, np.mean(losses)))
 
                 if global_step % args.save_steps == 0:
@@ -198,7 +211,7 @@ def train(conf_dict, args):
 
                     logging.info("saving infer model in %s" % model_path)
         else:
-            for left, right, label in train_pyreader():
+            for left, right, label in train_loader():
                 left = fluid.layers.reshape(left, shape=[-1, 1])
                 right = fluid.layers.reshape(right, shape=[-1, 1])
                 label = fluid.layers.reshape(label, shape=[-1, 1])
@@ -212,21 +225,24 @@ def train(conf_dict, args):
                 net.clear_gradients()
 
                 if args.do_valid and global_step % args.validation_steps == 0:
-                    for left, right in valid_pyreader():
+                    for left, right in valid_loader():
                         left = fluid.layers.reshape(left, shape=[-1, 1])
                         right = fluid.layers.reshape(right, shape=[-1, 1])
                         net.eval()
                         left_feat, pred = net(left, right)
                         pred_list += list(pred.numpy())
-                    valid_result = valid_and_test(pred_list, simnet_process, "valid")     
+                    valid_result = valid_and_test(pred_list, simnet_process,
+                                                  "valid")
                     if args.compute_accuracy:
                         valid_auc, valid_acc = valid_result
                         logging.info(
-                            "global_steps: %d, valid_auc: %f, valid_acc: %f, valid_loss: %f" %
-                            (global_step, valid_auc, valid_acc, np.mean(losses)))
+                            "global_steps: %d, valid_auc: %f, valid_acc: %f, valid_loss: %f"
+                            % (global_step, valid_auc, valid_acc,
+                               np.mean(losses)))
                     else:
                         valid_auc = valid_result
-                        logging.info("global_steps: %d, valid_auc: %f, valid_loss: %f" %
+                        logging.info(
+                            "global_steps: %d, valid_auc: %f, valid_loss: %f" %
                             (global_step, valid_auc, np.mean(losses)))
 
                 if global_step % args.save_steps == 0:
@@ -244,8 +260,7 @@ def train(conf_dict, args):
         ce_info.append([np.mean(losses), end_time - start_time])
         # final save
         logging.info("the final step is %s" % global_step)
-        model_save_dir = os.path.join(args.output_dir,
-                                    conf_dict["model_path"])
+        model_save_dir = os.path.join(args.output_dir, conf_dict["model_path"])
         model_path = os.path.join(model_save_dir, str(global_step))
 
         if not os.path.exists(model_save_dir):
@@ -269,13 +284,18 @@ def train(conf_dict, args):
 
         if args.do_test:
             # Get Feeder and Reader
-            test_pyreader = fluid.io.PyReader(capacity=16, return_list=True, use_double_buffer=True)
+            test_loader = fluid.io.DataLoader.from_generator(
+                capacity=16,
+                return_list=True,
+                iterable=True,
+                use_double_buffer=True)
             get_test_examples = simnet_process.get_reader("test")
-            test_pyreader.decorate_sample_list_generator(
-                paddle.batch(get_test_examples, batch_size=args.batch_size),
+            test_loader.set_sample_list_generator(
+                paddle.batch(
+                    get_test_examples, batch_size=args.batch_size),
                 place)
             pred_list = []
-            for left, pos_right in test_pyreader():
+            for left, pos_right in test_loader():
                 left = fluid.layers.reshape(left, shape=[-1, 1])
                 pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
                 net.eval()
@@ -307,47 +327,53 @@ def test(conf_dict, args):
 
         vocab = utils.load_vocab(args.vocab_path)
         simnet_process = reader.SimNetProcessor(args, vocab)
-        test_pyreader = fluid.io.PyReader(capacity=16, return_list=True, use_double_buffer=True)
+        test_loader = fluid.io.DataLoader.from_generator(
+            capacity=16,
+            return_list=True,
+            iterable=True,
+            use_double_buffer=True)
         get_test_examples = simnet_process.get_reader("test")
-        test_pyreader.decorate_sample_list_generator(
-                paddle.batch(get_test_examples, batch_size=args.batch_size),
-                place)
-     
+        test_loader.set_sample_list_generator(
+            paddle.batch(
+                get_test_examples, batch_size=args.batch_size), place)
 
         conf_dict['dict_size'] = len(vocab)
         conf_dict['seq_len'] = args.seq_len
 
-        net = utils.import_class("./nets",
-                                conf_dict["net"]["module_name"],
+        net = utils.import_class("./nets", conf_dict["net"]["module_name"],
                                  conf_dict["net"]["class_name"])(conf_dict)
 
         model, _ = load_dygraph(args.init_checkpoint)
         net.set_dict(model)
         metric = fluid.metrics.Auc(name="auc")
         pred_list = []
-        with io.open("predictions.txt", "w", encoding="utf8") as predictions_file:
+        with io.open(
+                "predictions.txt", "w", encoding="utf8") as predictions_file:
             if args.task_mode == "pairwise":
-                for left, pos_right in test_pyreader():
+                for left, pos_right in test_loader():
                     left = fluid.layers.reshape(left, shape=[-1, 1])
                     pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
 
                     left_feat, pos_score = net(left, pos_right)
                     pred = pos_score
 
-                    pred_list += list(map(lambda item: float(item[0]), pred.numpy()))
+                    pred_list += list(
+                        map(lambda item: float(item[0]), pred.numpy()))
                     predictions_file.write(u"\n".join(
-                            map(lambda item: str((item[0] + 1) / 2), pred.numpy())) + "\n")
+                        map(lambda item: str((item[0] + 1) / 2), pred.numpy()))
+                                           + "\n")
 
             else:
-                for left, right in test_pyreader():
+                for left, right in test_loader():
                     left = fluid.layers.reshape(left, shape=[-1, 1])
                     right = fluid.layers.reshape(right, shape=[-1, 1])
                     left_feat, pred = net(left, right)
 
-                    pred_list += list(map(lambda item: float(item[0]), pred.numpy()))
+                    pred_list += list(
+                        map(lambda item: float(item[0]), pred.numpy()))
                     predictions_file.write(u"\n".join(
-                            map(lambda item: str(np.argmax(item)), pred.numpy())) + "\n")
-
+                        map(lambda item: str(np.argmax(item)), pred.numpy())) +
+                                           "\n")
 
             if args.task_mode == "pairwise":
                 pred_list = np.array(pred_list).reshape((-1, 1))
@@ -383,28 +409,30 @@ def infer(conf_dict, args):
     else:
         place = fluid.CPUPlace()
 
-
     with fluid.dygraph.guard(place):
         vocab = utils.load_vocab(args.vocab_path)
         simnet_process = reader.SimNetProcessor(args, vocab)
         get_infer_examples = simnet_process.get_infer_reader
-        infer_pyreader = fluid.io.PyReader(capacity=16, return_list=True, use_double_buffer=True)
-        infer_pyreader.decorate_sample_list_generator(
-                    paddle.batch(get_infer_examples, batch_size=args.batch_size),
-                    place) 
+        infer_loader = fluid.io.DataLoader.from_generator(
+            capacity=16,
+            return_list=True,
+            iterable=True,
+            use_double_buffer=True)
+        infer_loader.set_sample_list_generator(
+            paddle.batch(
+                get_infer_examples, batch_size=args.batch_size), place)
 
         conf_dict['dict_size'] = len(vocab)
         conf_dict['seq_len'] = args.seq_len
 
-        net = utils.import_class("./nets",
-                                conf_dict["net"]["module_name"],
+        net = utils.import_class("./nets", conf_dict["net"]["module_name"],
                                  conf_dict["net"]["class_name"])(conf_dict)
         model, _ = load_dygraph(args.init_checkpoint)
         net.set_dict(model)
 
         pred_list = []
         if args.task_mode == "pairwise":
-            for left, pos_right in infer_pyreader():
+            for left, pos_right in infer_loader():
                 left = fluid.layers.reshape(left, shape=[-1, 1])
                 pos_right = fluid.layers.reshape(pos_right, shape=[-1, 1])
 
@@ -414,14 +442,15 @@ def infer(conf_dict, args):
                     map(lambda item: str((item[0] + 1) / 2), pred.numpy()))
 
         else:
-            for left, right in infer_pyreader():
+            for left, right in infer_loader():
                 left = fluid.layers.reshape(left, shape=[-1, 1])
                 pos_right = fluid.layers.reshape(right, shape=[-1, 1])
                 left_feat, pred = net(left, right)
-                pred_list += map(lambda item: str(np.argmax(item)), pred.numpy())
+                pred_list += map(lambda item: str(np.argmax(item)),
+                                 pred.numpy())
 
-    
-        with io.open(args.infer_result_path, "w", encoding="utf8") as infer_file:
+        with io.open(
+                args.infer_result_path, "w", encoding="utf8") as infer_file:
             for _data, _pred in zip(simnet_process.get_infer_data(), pred_list):
                 infer_file.write(_data + "\t" + _pred + "\n")
         logging.info("infer result saved in %s" %
@@ -435,6 +464,7 @@ def get_cards():
         num = len(cards.split(","))
     return num
 
+
 if __name__ == "__main__":
 
     args = ArgConfig()

dygraph/transformer/README.md

@@ -28,7 +28,7 @@
 
 1. paddle安装
 
-   本项目依赖于 PaddlePaddle 1.7及以上版本或适当的develop版本，请参考 [安装指南](http://www.paddlepaddle.org/#quick-start) 进行安装
+   本项目依赖于 PaddlePaddle 1.8及以上版本或适当的develop版本，请参考 [安装指南](https://www.paddlepaddle.org.cn/install/quick) 进行安装
 
 2. 下载代码
 
@@ -40,7 +40,7 @@
 
 3. 环境依赖
 
-   请参考PaddlePaddle[安装说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/1.6/beginners_guide/install/index_cn.html)部分的内容
+   请参考PaddlePaddle[安装说明](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/index_cn.html)部分的内容
 
 
 ### 数据准备

dygraph/transformer/config.py

@@ -42,12 +42,11 @@ class InferTaskConfig(object):
     batch_size = 4
     # the parameters for beam search.
     beam_size = 4
-    alpha=0.6
+    alpha = 0.6
     # max decoded length, should be less than ModelHyperParams.max_length
     max_out_len = 30
 
 
-
 class ModelHyperParams(object):
     """
     ModelHyperParams
@@ -156,38 +155,32 @@ input_descs = {
 # Names of word embedding table which might be reused for weight sharing.
 word_emb_param_names = (
     "src_word_emb_table",
-    "trg_word_emb_table",
-)
+    "trg_word_emb_table", )
 # Names of position encoding table which will be initialized externally.
 pos_enc_param_names = (
     "src_pos_enc_table",
-    "trg_pos_enc_table",
-)
+    "trg_pos_enc_table", )
 # separated inputs for different usages.
 encoder_data_input_fields = (
     "src_word",
     "src_pos",
-    "src_slf_attn_bias",
-)
+    "src_slf_attn_bias", )
 decoder_data_input_fields = (
     "trg_word",
     "trg_pos",
     "trg_slf_attn_bias",
     "trg_src_attn_bias",
-    "enc_output",
-)
+    "enc_output", )
 label_data_input_fields = (
     "lbl_word",
-    "lbl_weight",
-)
+    "lbl_weight", )
 # In fast decoder, trg_pos (only containing the current time step) is generated
 # by ops and trg_slf_attn_bias is not needed.
 fast_decoder_data_input_fields = (
     "trg_word",
     # "init_score",
     # "init_idx",
-    "trg_src_attn_bias",
-)
+    "trg_src_attn_bias", )
 
 
 def merge_cfg_from_list(cfg_list, g_cfgs):

dygraph/transformer/model.py

@@ -34,10 +34,10 @@ def position_encoding_init(n_position, d_pos_vec):
     num_timescales = channels // 2
     log_timescale_increment = (np.log(float(1e4) / float(1)) /
                                (num_timescales - 1))
-    inv_timescales = np.exp(
-        np.arange(num_timescales)) * -log_timescale_increment
-    scaled_time = np.expand_dims(position, 1) * np.expand_dims(
-        inv_timescales, 0)
+    inv_timescales = np.exp(np.arange(
+        num_timescales)) * -log_timescale_increment
+    scaled_time = np.expand_dims(position, 1) * np.expand_dims(inv_timescales,
+                                                               0)
     signal = np.concatenate([np.sin(scaled_time), np.cos(scaled_time)], axis=1)
     signal = np.pad(signal, [[0, 0], [0, np.mod(channels, 2)]], 'constant')
     position_enc = signal
@@ -48,6 +48,7 @@ class NoamDecay(LearningRateDecay):
     """
     learning rate scheduler
     """
+
     def __init__(self,
                  d_model,
                  warmup_steps,
@@ -72,6 +73,7 @@ class PrePostProcessLayer(Layer):
     """
     PrePostProcessLayer
     """
+
     def __init__(self, process_cmd, d_model, dropout_rate):
         super(PrePostProcessLayer, self).__init__()
         self.process_cmd = process_cmd
@@ -82,8 +84,8 @@ class PrePostProcessLayer(Layer):
             elif cmd == "n":  # add layer normalization
                 self.functors.append(
                     self.add_sublayer(
-                        "layer_norm_%d" %
-                        len(self.sublayers(include_sublayers=False)),
+                        "layer_norm_%d" % len(
+                            self.sublayers(include_sublayers=False)),
                         LayerNorm(
                             normalized_shape=d_model,
                             param_attr=fluid.ParamAttr(
@@ -108,6 +110,7 @@ class MultiHeadAttention(Layer):
     """
     Multi-Head Attention
     """
+
     def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0.):
         super(MultiHeadAttention, self).__init__()
         self.n_head = n_head
@@ -115,18 +118,14 @@ class MultiHeadAttention(Layer):
         self.d_value = d_value
         self.d_model = d_model
         self.dropout_rate = dropout_rate
-        self.q_fc = Linear(input_dim=d_model,
-                           output_dim=d_key * n_head,
-                           bias_attr=False)
-        self.k_fc = Linear(input_dim=d_model,
-                           output_dim=d_key * n_head,
-                           bias_attr=False)
-        self.v_fc = Linear(input_dim=d_model,
-                           output_dim=d_value * n_head,
-                           bias_attr=False)
-        self.proj_fc = Linear(input_dim=d_value * n_head,
-                              output_dim=d_model,
-                              bias_attr=False)
+        self.q_fc = Linear(
+            input_dim=d_model, output_dim=d_key * n_head, bias_attr=False)
+        self.k_fc = Linear(
+            input_dim=d_model, output_dim=d_key * n_head, bias_attr=False)
+        self.v_fc = Linear(
+            input_dim=d_model, output_dim=d_value * n_head, bias_attr=False)
+        self.proj_fc = Linear(
+            input_dim=d_value * n_head, output_dim=d_model, bias_attr=False)
 
     def forward(self, queries, keys, values, attn_bias, cache=None):
         # compute q ,k ,v
@@ -152,17 +151,14 @@ class MultiHeadAttention(Layer):
             cache["k"], cache["v"] = k, v
 
         # scale dot product attention
-        product = layers.matmul(x=q,
-                                y=k,
-                                transpose_y=True,
-                                alpha=self.d_model**-0.5)
+        product = layers.matmul(
+            x=q, y=k, transpose_y=True, alpha=self.d_model**-0.5)
         if attn_bias is not None:
             product += attn_bias
         weights = layers.softmax(product)
         if self.dropout_rate:
-            weights = layers.dropout(weights,
-                                     dropout_prob=self.dropout_rate,
-                                     is_test=False)
+            weights = layers.dropout(
+                weights, dropout_prob=self.dropout_rate, is_test=False)
 
             out = layers.matmul(weights, v)
 
@@ -179,6 +175,7 @@ class FFN(Layer):
     """
     Feed-Forward Network
     """
+
     def __init__(self, d_inner_hid, d_model, dropout_rate):
         super(FFN, self).__init__()
         self.dropout_rate = dropout_rate
@@ -188,9 +185,8 @@ class FFN(Layer):
     def forward(self, x):
         hidden = self.fc1(x)
         if self.dropout_rate:
-            hidden = layers.dropout(hidden,
-                                    dropout_prob=self.dropout_rate,
-                                    is_test=False)
+            hidden = layers.dropout(
+                hidden, dropout_prob=self.dropout_rate, is_test=False)
         out = self.fc2(hidden)
         return out
 
@@ -199,6 +195,7 @@ class EncoderLayer(Layer):
     """
     EncoderLayer
     """
+
     def __init__(self,
                  n_head,
                  d_key,
@@ -227,8 +224,8 @@ class EncoderLayer(Layer):
                                                   prepostprocess_dropout)
 
     def forward(self, enc_input, attn_bias):
-        attn_output = self.self_attn(self.preprocesser1(enc_input), None, None,
-                                     attn_bias)
+        attn_output = self.self_attn(
+            self.preprocesser1(enc_input), None, None, attn_bias)
         attn_output = self.postprocesser1(attn_output, enc_input)
 
         ffn_output = self.ffn(self.preprocesser2(attn_output))
@@ -240,6 +237,7 @@ class Encoder(Layer):
     """
     encoder
     """
+
     def __init__(self,
                  n_layer,
                  n_head,
@@ -279,6 +277,7 @@ class Embedder(Layer):
     """
     Word Embedding + Position Encoding
     """
+
     def __init__(self, vocab_size, emb_dim, bos_idx=0):
         super(Embedder, self).__init__()
 
@@ -297,6 +296,7 @@ class WrapEncoder(Layer):
     """
     embedder + encoder
     """
+
     def __init__(self, src_vocab_size, max_length, n_layer, n_head, d_key,
                  d_value, d_model, d_inner_hid, prepostprocess_dropout,
                  attention_dropout, relu_dropout, preprocess_cmd,
@@ -324,8 +324,8 @@ class WrapEncoder(Layer):
         pos_enc = self.pos_encoder(src_pos)
         pos_enc.stop_gradient = True
         emb = word_emb + pos_enc
-        enc_input = layers.dropout(emb,
-                                   dropout_prob=self.emb_dropout,
+        enc_input = layers.dropout(
+            emb, dropout_prob=self.emb_dropout,
             is_test=False) if self.emb_dropout else emb
 
         enc_output = self.encoder(enc_input, src_slf_attn_bias)
@@ -336,6 +336,7 @@ class DecoderLayer(Layer):
     """
     decoder
     """
+
     def __init__(self,
                  n_head,
                  d_key,
@@ -375,8 +376,8 @@ class DecoderLayer(Layer):
                 self_attn_bias,
                 cross_attn_bias,
                 cache=None):
-        self_attn_output = self.self_attn(self.preprocesser1(dec_input), None,
-                                          None, self_attn_bias, cache)
+        self_attn_output = self.self_attn(
+            self.preprocesser1(dec_input), None, None, self_attn_bias, cache)
         self_attn_output = self.postprocesser1(self_attn_output, dec_input)
 
         cross_attn_output = self.cross_attn(
@@ -395,6 +396,7 @@ class Decoder(Layer):
     """
     decoder
     """
+
     def __init__(self, n_layer, n_head, d_key, d_value, d_model, d_inner_hid,
                  prepostprocess_dropout, attention_dropout, relu_dropout,
                  preprocess_cmd, postprocess_cmd):
@@ -420,8 +422,8 @@ class Decoder(Layer):
                 caches=None):
         for i, decoder_layer in enumerate(self.decoder_layers):
             dec_output = decoder_layer(dec_input, enc_output, self_attn_bias,
-                                       cross_attn_bias,
-                                       None if caches is None else caches[i])
+                                       cross_attn_bias, None
+                                       if caches is None else caches[i])
             dec_input = dec_output
 
         return self.processer(dec_output)
@@ -431,6 +433,7 @@ class WrapDecoder(Layer):
     """
     embedder + decoder
     """
+
     def __init__(self, trg_vocab_size, max_length, n_layer, n_head, d_key,
                  d_value, d_model, d_inner_hid, prepostprocess_dropout,
                  attention_dropout, relu_dropout, preprocess_cmd,
@@ -458,9 +461,8 @@ class WrapDecoder(Layer):
                                                   word_embedder.weight,
                                                   transpose_y=True)
         else:
-            self.linear = Linear(input_dim=d_model,
-                                 output_dim=trg_vocab_size,
-                                 bias_attr=False)
+            self.linear = Linear(
+                input_dim=d_model, output_dim=trg_vocab_size, bias_attr=False)
 
     def forward(self,
                 trg_word,
@@ -474,15 +476,14 @@ class WrapDecoder(Layer):
         pos_enc = self.pos_encoder(trg_pos)
         pos_enc.stop_gradient = True
         emb = word_emb + pos_enc
-        dec_input = layers.dropout(emb,
-                                   dropout_prob=self.emb_dropout,
+        dec_input = layers.dropout(
+            emb, dropout_prob=self.emb_dropout,
             is_test=False) if self.emb_dropout else emb
         dec_output = self.decoder(dec_input, enc_output, trg_slf_attn_bias,
                                   trg_src_attn_bias, caches)
         dec_output = layers.reshape(
             dec_output,
-            shape=[-1, dec_output.shape[-1]],
-        )
+            shape=[-1, dec_output.shape[-1]], )
         logits = self.linear(dec_output)
         return logits
 
@@ -493,7 +494,8 @@ class CrossEntropyCriterion(object):
 
     def __call__(self, predict, label, weights):
         if self.label_smooth_eps:
-            label_out = layers.label_smooth(label=layers.one_hot(
+            label_out = layers.label_smooth(
+                label=layers.one_hot(
                     input=label, depth=predict.shape[-1]),
                 epsilon=self.label_smooth_eps)
 
@@ -513,6 +515,7 @@ class Transformer(Layer):
     """
     model
     """
+
     def __init__(self,
                  src_vocab_size,
                  trg_vocab_size,
@@ -532,28 +535,24 @@ class Transformer(Layer):
                  bos_id=0,
                  eos_id=1):
         super(Transformer, self).__init__()
-        src_word_embedder = Embedder(vocab_size=src_vocab_size,
-                                     emb_dim=d_model,
-                                     bos_idx=bos_id)
-        self.encoder = WrapEncoder(src_vocab_size, max_length, n_layer, n_head,
-                                   d_key, d_value, d_model, d_inner_hid,
-                                   prepostprocess_dropout, attention_dropout,
-                                   relu_dropout, preprocess_cmd,
-                                   postprocess_cmd, src_word_embedder)
+        src_word_embedder = Embedder(
+            vocab_size=src_vocab_size, emb_dim=d_model, bos_idx=bos_id)
+        self.encoder = WrapEncoder(
+            src_vocab_size, max_length, n_layer, n_head, d_key, d_value,
+            d_model, d_inner_hid, prepostprocess_dropout, attention_dropout,
+            relu_dropout, preprocess_cmd, postprocess_cmd, src_word_embedder)
         if weight_sharing:
             assert src_vocab_size == trg_vocab_size, (
                 "Vocabularies in source and target should be same for weight sharing."
             )
             trg_word_embedder = src_word_embedder
         else:
-            trg_word_embedder = Embedder(vocab_size=trg_vocab_size,
-                                         emb_dim=d_model,
-                                         bos_idx=bos_id)
-        self.decoder = WrapDecoder(trg_vocab_size, max_length, n_layer, n_head,
-                                   d_key, d_value, d_model, d_inner_hid,
-                                   prepostprocess_dropout, attention_dropout,
-                                   relu_dropout, preprocess_cmd,
-                                   postprocess_cmd, weight_sharing,
+            trg_word_embedder = Embedder(
+                vocab_size=trg_vocab_size, emb_dim=d_model, bos_idx=bos_id)
+        self.decoder = WrapDecoder(
+            trg_vocab_size, max_length, n_layer, n_head, d_key, d_value,
+            d_model, d_inner_hid, prepostprocess_dropout, attention_dropout,
+            relu_dropout, preprocess_cmd, postprocess_cmd, weight_sharing,
             trg_word_embedder)
 
         self.trg_vocab_size = trg_vocab_size
@@ -595,6 +594,7 @@ class Transformer(Layer):
         and newly added finished candidates from `grow_topk`, and selects the top
         `beam_size` finished candidates.
         """
+
         def expand_to_beam_size(tensor, beam_size):
             tensor = layers.reshape(tensor,
                                     [tensor.shape[0], 1] + tensor.shape[1:])
@@ -616,19 +616,23 @@ class Transformer(Layer):
         ### initialize states of beam search ###
         ## init for the alive ##
         initial_log_probs = to_variable(
-            np.array([[0.] + [-inf] * (beam_size - 1)], dtype="float32"))
+            np.array(
+                [[0.] + [-inf] * (beam_size - 1)], dtype="float32"))
         alive_log_probs = layers.expand(initial_log_probs, [batch_size, 1])
         alive_seq = to_variable(
-            np.tile(np.array([[[bos_id]]], dtype="int64"),
-                    (batch_size, beam_size, 1)))
+            np.tile(
+                np.array(
+                    [[[bos_id]]], dtype="int64"), (batch_size, beam_size, 1)))
 
         ## init for the finished ##
         finished_scores = to_variable(
-            np.array([[-inf] * beam_size], dtype="float32"))
+            np.array(
+                [[-inf] * beam_size], dtype="float32"))
         finished_scores = layers.expand(finished_scores, [batch_size, 1])
         finished_seq = to_variable(
-            np.tile(np.array([[[bos_id]]], dtype="int64"),
-                    (batch_size, beam_size, 1)))
+            np.tile(
+                np.array(
+                    [[[bos_id]]], dtype="int64"), (batch_size, beam_size, 1)))
         finished_flags = layers.zeros_like(finished_scores)
 
         ### initialize inputs and states of transformer decoder ###
@@ -640,13 +644,11 @@ class Transformer(Layer):
         enc_output = merge_beam_dim(expand_to_beam_size(enc_output, beam_size))
         ## init states (caches) for transformer, need to be updated according to selected beam
         caches = [{
-            "k":
-            layers.fill_constant(
+            "k": layers.fill_constant(
                 shape=[batch_size * beam_size, self.n_head, 0, self.d_key],
                 dtype=enc_output.dtype,
                 value=0),
-            "v":
-            layers.fill_constant(
+            "v": layers.fill_constant(
                 shape=[batch_size * beam_size, self.n_head, 0, self.d_value],
                 dtype=enc_output.dtype,
                 value=0),
@@ -665,11 +667,11 @@ class Transformer(Layer):
                                 beam_size,
                                 batch_size,
                                 need_flat=True):
-            batch_idx = layers.range(0, batch_size, 1,
-                                     dtype="int64") * beam_size
+            batch_idx = layers.range(
+                0, batch_size, 1, dtype="int64") * beam_size
             flat_tensor = merge_beam_dim(tensor_nd) if need_flat else tensor_nd
-            idx = layers.reshape(layers.elementwise_add(beam_idx, batch_idx, 0),
-                                 [-1])
+            idx = layers.reshape(
+                layers.elementwise_add(beam_idx, batch_idx, 0), [-1])
             new_flat_tensor = layers.gather(flat_tensor, idx)
             new_tensor_nd = layers.reshape(
                 new_flat_tensor,
@@ -681,7 +683,8 @@ class Transformer(Layer):
                          finished_in_finished):
             max_length_penalty = np.power(((5. + max_len) / 6.), alpha)
             # The best possible score of the most likely alive sequence
-            lower_bound_alive_scores = alive_log_probs[:, 0] / max_length_penalty
+            lower_bound_alive_scores = alive_log_probs[:,
+                                                       0] / max_length_penalty
 
             # Now to compute the lowest score of a finished sequence in finished
             # If the sequence isn't finished, we multiply it's score by 0. since
@@ -711,8 +714,8 @@ class Transformer(Layer):
             curr_scores = log_probs / length_penalty
             flat_curr_scores = layers.reshape(curr_scores, [batch_size, -1])
 
-            topk_scores, topk_ids = layers.topk(flat_curr_scores,
-                                                k=beam_size * 2)
+            topk_scores, topk_ids = layers.topk(
+                flat_curr_scores, k=beam_size * 2)
 
             topk_log_probs = topk_scores * length_penalty
 
@@ -723,13 +726,11 @@ class Transformer(Layer):
             topk_seq = gather_2d_by_gather(alive_seq, topk_beam_index,
                                            beam_size, batch_size)
             topk_seq = layers.concat(
-                [topk_seq,
-                 layers.reshape(topk_ids, topk_ids.shape + [1])],
+                [topk_seq, layers.reshape(topk_ids, topk_ids.shape + [1])],
                 axis=2)
             states = update_states(states, topk_beam_index, beam_size)
-            eos = layers.fill_constant(shape=topk_ids.shape,
-                                       dtype="int64",
-                                       value=eos_id)
+            eos = layers.fill_constant(
+                shape=topk_ids.shape, dtype="int64", value=eos_id)
             topk_finished = layers.cast(layers.equal(topk_ids, eos), "float32")
 
             #topk_seq: [batch_size, 2*beam_size, i+1]
@@ -751,9 +752,10 @@ class Transformer(Layer):
         def grow_finished(finished_seq, finished_scores, finished_flags,
                           curr_seq, curr_scores, curr_finished):
             # finished scores
-            finished_seq = layers.concat([
-                finished_seq,
-                layers.fill_constant(shape=[batch_size, beam_size, 1],
+            finished_seq = layers.concat(
+                [
+                    finished_seq, layers.fill_constant(
+                        shape=[batch_size, beam_size, 1],
                         dtype="int64",
                         value=eos_id)
                 ],
@@ -763,25 +765,22 @@ class Transformer(Layer):
             curr_scores += (1. - curr_finished) * -inf
             # concatenating the sequences and scores along beam axis
             curr_finished_seq = layers.concat([finished_seq, curr_seq], axis=1)
-            curr_finished_scores = layers.concat([finished_scores, curr_scores],
-                                                 axis=1)
-            curr_finished_flags = layers.concat([finished_flags, curr_finished],
-                                                axis=1)
+            curr_finished_scores = layers.concat(
+                [finished_scores, curr_scores], axis=1)
+            curr_finished_flags = layers.concat(
+                [finished_flags, curr_finished], axis=1)
             _, topk_indexes = layers.topk(curr_finished_scores, k=beam_size)
             finished_seq = gather_2d_by_gather(curr_finished_seq, topk_indexes,
                                                beam_size * 3, batch_size)
-            finished_scores = gather_2d_by_gather(curr_finished_scores,
-                                                  topk_indexes, beam_size * 3,
-                                                  batch_size)
-            finished_flags = gather_2d_by_gather(curr_finished_flags,
-                                                 topk_indexes, beam_size * 3,
-                                                 batch_size)
+            finished_scores = gather_2d_by_gather(
+                curr_finished_scores, topk_indexes, beam_size * 3, batch_size)
+            finished_flags = gather_2d_by_gather(
+                curr_finished_flags, topk_indexes, beam_size * 3, batch_size)
             return finished_seq, finished_scores, finished_flags
 
         for i in range(max_len):
-            trg_pos = layers.fill_constant(shape=trg_word.shape,
-                                           dtype="int64",
-                                           value=i)
+            trg_pos = layers.fill_constant(
+                shape=trg_word.shape, dtype="int64", value=i)
             logits = self.decoder(trg_word, trg_pos, None, trg_src_attn_bias,
                                   enc_output, caches)
             topk_seq, topk_log_probs, topk_scores, topk_finished, states = grow_topk(
@@ -818,20 +817,23 @@ class Transformer(Layer):
             return layers.expand(tensor, tile_dims)
 
         def merge_batch_beams(tensor):
-            return layers.reshape(tensor, [tensor.shape[0] * tensor.shape[1]] +
-                                  tensor.shape[2:])
+            return layers.reshape(
+                tensor, [tensor.shape[0] * tensor.shape[1]] + tensor.shape[2:])
 
         def split_batch_beams(tensor):
-            return fluid.layers.reshape(tensor,
-                                        shape=[-1, beam_size] +
-                                        list(tensor.shape[1:]))
+            return fluid.layers.reshape(
+                tensor, shape=[-1, beam_size] + list(tensor.shape[1:]))
 
         def mask_probs(probs, finished, noend_mask_tensor):
             # TODO: use where_op
             finished = layers.cast(finished, dtype=probs.dtype)
             probs = layers.elementwise_mul(
-                layers.expand(layers.unsqueeze(finished, [2]), [1, 1, self.trg_vocab_size]),
-                noend_mask_tensor, axis=-1) - layers.elementwise_mul(probs, (finished - 1), axis=0)
+                layers.expand(
+                    layers.unsqueeze(finished, [2]),
+                    [1, 1, self.trg_vocab_size]),
+                noend_mask_tensor,
+                axis=-1) - layers.elementwise_mul(
+                    probs, (finished - 1), axis=0)
             return probs
 
         def gather(x, indices, batch_pos):
@@ -845,54 +847,53 @@ class Transformer(Layer):
         inf = float(1. * 1e7)
         batch_size = enc_output.shape[0]
         max_len = (enc_output.shape[1] + 20) if max_len is None else max_len
-        vocab_size_tensor = layers.fill_constant(shape=[1],
-                                                 dtype="int64",
-                                                 value=self.trg_vocab_size)
+        vocab_size_tensor = layers.fill_constant(
+            shape=[1], dtype="int64", value=self.trg_vocab_size)
         end_token_tensor = to_variable(
-            np.full([batch_size, beam_size], eos_id, dtype="int64"))
+            np.full(
+                [batch_size, beam_size], eos_id, dtype="int64"))
         noend_array = [-inf] * self.trg_vocab_size
         noend_array[eos_id] = 0
-        noend_mask_tensor = to_variable(np.array(noend_array,dtype="float32"))
+        noend_mask_tensor = to_variable(np.array(noend_array, dtype="float32"))
         batch_pos = layers.expand(
             layers.unsqueeze(
-                to_variable(np.arange(0, batch_size, 1, dtype="int64")), [1]),
-            [1, beam_size])
+                to_variable(np.arange(
+                    0, batch_size, 1, dtype="int64")), [1]), [1, beam_size])
 
         predict_ids = []
         parent_ids = []
         ### initialize states of beam search ###
         log_probs = to_variable(
-            np.array([[0.] + [-inf] * (beam_size - 1)] * batch_size,
+            np.array(
+                [[0.] + [-inf] * (beam_size - 1)] * batch_size,
                 dtype="float32"))
-        finished = to_variable(np.full([batch_size, beam_size], 0,
-                                       dtype="bool"))
+        finished = to_variable(
+            np.full(
+                [batch_size, beam_size], 0, dtype="bool"))
         ### initialize inputs and states of transformer decoder ###
         ## init inputs for decoder, shaped `[batch_size*beam_size, ...]`
-        trg_word = layers.fill_constant(shape=[batch_size * beam_size, 1],
-                                        dtype="int64",
-                                        value=bos_id)
+        trg_word = layers.fill_constant(
+            shape=[batch_size * beam_size, 1], dtype="int64", value=bos_id)
         trg_pos = layers.zeros_like(trg_word)
         trg_src_attn_bias = merge_batch_beams(
             expand_to_beam_size(trg_src_attn_bias, beam_size))
-        enc_output = merge_batch_beams(expand_to_beam_size(enc_output, beam_size))
+        enc_output = merge_batch_beams(
+            expand_to_beam_size(enc_output, beam_size))
         ## init states (caches) for transformer, need to be updated according to selected beam
         caches = [{
-            "k":
-            layers.fill_constant(
+            "k": layers.fill_constant(
                 shape=[batch_size * beam_size, self.n_head, 0, self.d_key],
                 dtype=enc_output.dtype,
                 value=0),
-            "v":
-            layers.fill_constant(
+            "v": layers.fill_constant(
                 shape=[batch_size * beam_size, self.n_head, 0, self.d_value],
                 dtype=enc_output.dtype,
                 value=0),
         } for i in range(self.n_layer)]
 
         for i in range(max_len):
-            trg_pos = layers.fill_constant(shape=trg_word.shape,
-                                           dtype="int64",
-                                           value=i)
+            trg_pos = layers.fill_constant(
+                shape=trg_word.shape, dtype="int64", value=i)
             caches = map_structure(  # can not be reshaped since the 0 size
                 lambda x: x if i == 0 else merge_batch_beams(x), caches)
             logits = self.decoder(trg_word, trg_pos, None, trg_src_attn_bias,
@@ -902,18 +903,17 @@ class Transformer(Layer):
                 fluid.layers.log(fluid.layers.softmax(logits)))
             step_log_probs = mask_probs(step_log_probs, finished,
                                         noend_mask_tensor)
-            log_probs = layers.elementwise_add(x=step_log_probs,
-                                                    y=log_probs,
-                                                    axis=0)
+            log_probs = layers.elementwise_add(
+                x=step_log_probs, y=log_probs, axis=0)
             log_probs = layers.reshape(log_probs,
                                        [-1, beam_size * self.trg_vocab_size])
             scores = log_probs
-            topk_scores, topk_indices = fluid.layers.topk(input=scores,
-                                                          k=beam_size)
-            beam_indices = fluid.layers.elementwise_floordiv(
-                topk_indices, vocab_size_tensor)
-            token_indices = fluid.layers.elementwise_mod(
-                topk_indices, vocab_size_tensor)
+            topk_scores, topk_indices = fluid.layers.topk(
+                input=scores, k=beam_size)
+            beam_indices = fluid.layers.elementwise_floordiv(topk_indices,
+                                                             vocab_size_tensor)
+            token_indices = fluid.layers.elementwise_mod(topk_indices,
+                                                         vocab_size_tensor)
 
             # update states
             caches = map_structure(lambda x: gather(x, beam_indices, batch_pos),

dygraph/transformer/reader.py

@@ -306,6 +306,7 @@ class DataProcessor(object):
     :param seed: The seed for random.
     :type seed: int
     """
+
     def __init__(self,
                  src_vocab_fpath,
                  trg_vocab_fpath,
@@ -360,7 +361,8 @@ class DataProcessor(object):
 
     def load_src_trg_ids(self, fpattern, tar_fname):
         converters = [
-            Converter(vocab=self._src_vocab,
+            Converter(
+                vocab=self._src_vocab,
                 beg=self._bos_idx,
                 end=self._eos_idx,
                 unk=self._unk_idx,
@@ -369,7 +371,8 @@ class DataProcessor(object):
         ]
         if not self._only_src:
             converters.append(
-                Converter(vocab=self._trg_vocab,
+                Converter(
+                    vocab=self._trg_vocab,
                     beg=self._bos_idx,
                     end=self._eos_idx,
                     unk=self._unk_idx,
@@ -402,9 +405,8 @@ class DataProcessor(object):
             f = tarfile.open(fpaths[0], "rb")
             for line in f.extractfile(tar_fname):
                 fields = line.strip(b"\n").split(self._field_delimiter)
-                if (not self._only_src
-                        and len(fields) == 2) or (self._only_src
-                                                  and len(fields) == 1):
+                if (not self._only_src and len(fields) == 2) or (
+                        self._only_src and len(fields) == 1):
                     yield fields
         else:
             for fpath in fpaths:
@@ -414,9 +416,8 @@ class DataProcessor(object):
                 with open(fpath, "rb") as f:
                     for line in f:
                         fields = line.strip(b"\n").split(self._field_delimiter)
-                        if (not self._only_src
-                                and len(fields) == 2) or (self._only_src
-                                                          and len(fields) == 1):
+                        if (not self._only_src and len(fields) == 2) or (
+                                self._only_src and len(fields) == 1):
                             yield fields
 
     @staticmethod
@@ -512,8 +513,8 @@ class DataProcessor(object):
             for item in data_reader():
                 inst_num_per_part = len(item) // count
                 for i in range(count):
-                    yield item[inst_num_per_part * i:inst_num_per_part *
-                               (i + 1)]
+                    yield item[inst_num_per_part * i:inst_num_per_part * (i + 1
+                                                                          )]
 
         return __impl__