Merge pull request #66 from tianxin1860/develop

 add ernie encoder

ERNIE/README.md

@@ -261,3 +261,30 @@ text_a  text_b  label
 [dev evaluation] f1: 0.951949, precision: 0.944636, recall: 0.959376, elapsed time: 19.156693 s
 [test evaluation] f1: 0.937390, precision: 0.925988, recall: 0.949077, elapsed time: 36.565929 s
 ```
+
+### FAQ
+
+#### 如何获取输入句子经过 ERNIE 编码后的 Embedding 表示?
+
+可以通过 ernie_encoder.py 抽取出输入句子的 Embedding 表示和句子中每个 token 的 Embedding 表示，数据格式和 [Fine-tuning 任务](#Fine-tuning-任务) 一节中介绍的各种类型 Fine-tuning 任务的训练数据格式一致；以获取 LCQM dev 数据集中的句子 Embedding 和 token embedding 为例，示例脚本如下:
+
+```
+export FLAGS_sync_nccl_allreduce=1
+export CUDA_VISIBLE_DEVICES=7
+
+python -u ernir_encoder.py \
+                   --use_cuda true \
+                   --batch_size 32 \
+                   --output_dir "./test" \
+                   --init_pretraining_params ${MODEL_PATH}/params \
+                   --data_set ${TASK_DATA_PATH}/lcqmc/dev.tsv \
+                   --vocab_path config/vocab.txt \
+                   --max_seq_len 128 \
+                   --ernie_config_path config/ernie_config.json
+```
+
+上述脚本运行结束后，会在当前路径的 test 目录下分别生成 `cls_emb.npy` 文件存储句子 embeddings 和 `top_layer_emb.npy` 文件存储 token embeddings; 实际使用时，参照示例脚本修改数据路径、embeddings 文件存储路径等配置即可运行；
+
+#### 如何获取输入句子中每个 token 经过 ERNIE 编码后的 Embedding 表示？
+
+[解决方案同上](#如何获取输入句子经过-ERNIE-编码后的-Embedding-表示?)

ERNIE/batching.py

@@ -166,7 +166,8 @@ def pad_batch_data(insts,
                    return_pos=False,
                    return_input_mask=False,
                    return_max_len=False,
-                   return_num_token=False):
+                   return_num_token=False,
+                   return_seq_lens=False):
     """
     Pad the instances to the max sequence length in batch, and generate the
     corresponding position data and attention bias.
@@ -205,6 +206,10 @@ def pad_batch_data(insts,
             num_token += len(inst)
         return_list += [num_token]
 
+    if return_seq_lens:
+        seq_lens = np.array([len(inst) for inst in insts])
+        return_list += [seq_lens.astype("int64").reshape([-1, 1])]
+
     return return_list if len(return_list) > 1 else return_list[0]
 
 

ERNIE/ernir_encoder.py

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""extract embeddings from ERNIE encoder."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import argparse
+import numpy as np
+import multiprocessing
+
+import paddle.fluid as fluid
+
+import reader.task_reader as task_reader
+from model.ernie import ErnieConfig, ErnieModel
+from utils.args import ArgumentGroup, print_arguments
+from utils.init import init_pretraining_params
+
+# yapf: disable
+parser = argparse.ArgumentParser(__doc__)
+model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
+model_g.add_arg("ernie_config_path",         str,  None, "Path to the json file for ernie model config.")
+model_g.add_arg("init_pretraining_params",   str,  None,
+                "Init pre-training params which preforms fine-tuning from. If the "
+                 "arg 'init_checkpoint' has been set, this argument wouldn't be valid.")
+model_g.add_arg("output_dir",                str,  "embeddings", "path to save embeddings extracted by ernie_encoder.")
+
+data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
+data_g.add_arg("data_set",            str,  None,  "Path to data for calculating ernie_embeddings.")
+data_g.add_arg("vocab_path",          str,  None,  "Vocabulary path.")
+data_g.add_arg("max_seq_len",         int,  512,   "Number of words of the longest seqence.")
+data_g.add_arg("batch_size",          int,  32,    "Total examples' number in batch for training.")
+data_g.add_arg("do_lower_case",       bool, True,
+               "Whether to lower case the input text. Should be True for uncased models and False for cased models.")
+
+run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
+run_type_g.add_arg("use_cuda",                     bool,   True,  "If set, use GPU for training.")
+# yapf: enable
+
+
+def create_model(args, pyreader_name, ernie_config):
+    pyreader = fluid.layers.py_reader(
+        capacity=50,
+        shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
+                [-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1, 1]],
+        dtypes=['int64', 'int64', 'int64', 'float', 'int64'],
+        lod_levels=[0, 0, 0, 0, 0],
+        name=pyreader_name,
+        use_double_buffer=True)
+
+    (src_ids, sent_ids, pos_ids, input_mask,
+     seq_lens) = fluid.layers.read_file(pyreader)
+
+    ernie = ErnieModel(
+        src_ids=src_ids,
+        position_ids=pos_ids,
+        sentence_ids=sent_ids,
+        input_mask=input_mask,
+        config=ernie_config)
+
+    enc_out = ernie.get_sequence_output()
+    unpad_enc_out = fluid.layers.sequence_unpad(enc_out, length=seq_lens)
+    cls_feats = ernie.get_pooled_output()
+
+    # set persistable = True to avoid memory opimizing
+    enc_out.persistable = True
+    unpad_enc_out.persistable = True
+    cls_feats.persistable = True
+
+    graph_vars = {
+        "cls_embeddings": cls_feats,
+        "top_layer_embeddings": unpad_enc_out,
+    }
+
+    return pyreader, graph_vars
+
+
+def main(args):
+    args = parser.parse_args()
+    ernie_config = ErnieConfig(args.ernie_config_path)
+    ernie_config.print_config()
+
+    if args.use_cuda:
+        place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
+        dev_count = fluid.core.get_cuda_device_count()
+    else:
+        place = fluid.CPUPlace()
+        dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+
+    exe = fluid.Executor(place)
+
+    reader = task_reader.ExtractEmbeddingReader(
+        vocab_path=args.vocab_path,
+        max_seq_len=args.max_seq_len,
+        do_lower_case=args.do_lower_case)
+
+    startup_prog = fluid.Program()
+
+    data_generator = reader.data_generator(
+        input_file=args.data_set,
+        batch_size=args.batch_size,
+        epoch=1,
+        shuffle=False)
+
+    total_examples = reader.get_num_examples(args.data_set)
+
+    print("Device count: %d" % dev_count)
+    print("Total num examples: %d" % total_examples)
+
+    infer_program = fluid.Program()
+
+    with fluid.program_guard(infer_program, startup_prog):
+        with fluid.unique_name.guard():
+            pyreader, graph_vars = create_model(
+                args, pyreader_name='reader', ernie_config=ernie_config)
+
+            fluid.memory_optimize(input_program=infer_program)
+
+    infer_program = infer_program.clone(for_test=True)
+
+    exe.run(startup_prog)
+
+    if args.init_pretraining_params:
+        init_pretraining_params(
+            exe, args.init_pretraining_params, main_program=startup_prog)
+    else:
+        raise ValueError(
+            "WARNING: args 'init_pretraining_params' must be specified")
+
+    exec_strategy = fluid.ExecutionStrategy()
+    exec_strategy.num_threads = dev_count
+
+    pyreader.decorate_tensor_provider(data_generator)
+    pyreader.start()
+
+    total_cls_emb = []
+    total_top_layer_emb = []
+    total_labels = []
+    while True:
+        try:
+            cls_emb, unpad_top_layer_emb = exe.run(
+                program=infer_program,
+                fetch_list=[
+                    graph_vars["cls_embeddings"].name, graph_vars[
+                        "top_layer_embeddings"].name
+                ],
+                return_numpy=False)
+            # batch_size * embedding_size
+            total_cls_emb.append(np.array(cls_emb))
+            total_top_layer_emb.append(np.array(unpad_top_layer_emb))
+        except fluid.core.EOFException:
+            break
+
+    total_cls_emb = np.concatenate(total_cls_emb)
+    total_top_layer_emb = np.concatenate(total_top_layer_emb)
+
+    with open(os.path.join(args.output_dir, "cls_emb.npy"),
+              "w") as cls_emb_file:
+        np.save(cls_emb_file, total_cls_emb)
+    with open(os.path.join(args.output_dir, "top_layer_emb.npy"),
+              "w") as top_layer_emb_file:
+        np.save(top_layer_emb_file, total_top_layer_emb)
+
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    print_arguments(args)
+
+    main(args)

ERNIE/finetune/classifier.py

@@ -20,9 +20,8 @@ from __future__ import print_function
 import time
 import numpy as np
 
-import paddle.fluid as fluid
-
 from six.moves import xrange
+import paddle.fluid as fluid
 
 from model.ernie import ErnieModel
 

ERNIE/model/ernie.py

@@ -17,10 +17,11 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import six
 import json
-import numpy as np
+
+import six
 import paddle.fluid as fluid
+
 from model.transformer_encoder import encoder, pre_process_layer
 
 

ERNIE/model/transformer_encoder.py

@@ -18,7 +18,6 @@ from __future__ import division
 from __future__ import print_function
 
 from functools import partial
-import numpy as np
 
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers

ERNIE/reader/task_reader.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import csv
 import json
 import numpy as np
@@ -50,7 +49,6 @@ class BaseReader(object):
                 self.label_map = json.load(f)
         else:
             self.label_map = None
-        pass
 
     def get_train_progress(self):
         """Gets progress for training phase."""
@@ -183,7 +181,7 @@ class BaseReader(object):
                 yield self._pad_batch_records(batch_records)
                 batch_records, max_len = [record], len(record.token_ids)
 
-        if len(batch_records) > 0:
+        if batch_records:
             yield self._pad_batch_records(batch_records)
 
     def get_num_examples(self, input_file):
@@ -268,19 +266,19 @@ class SequenceLabelReader(BaseReader):
         batch_text_type_ids = [record.text_type_ids for record in batch_records]
         batch_position_ids = [record.position_ids for record in batch_records]
         batch_label_ids = [record.label_ids for record in batch_records]
-        batch_seq_lens = [len(record.token_ids) for record in batch_records]
 
         # padding
-        padded_token_ids, input_mask = pad_batch_data(
-            batch_token_ids, pad_idx=self.pad_id, return_input_mask=True)
+        padded_token_ids, input_mask, batch_seq_lens = pad_batch_data(
+            batch_token_ids,
+            pad_idx=self.pad_id,
+            return_input_mask=True,
+            return_seq_lens=True)
         padded_text_type_ids = pad_batch_data(
             batch_text_type_ids, pad_idx=self.pad_id)
         padded_position_ids = pad_batch_data(
             batch_position_ids, pad_idx=self.pad_id)
         padded_label_ids = pad_batch_data(
             batch_label_ids, pad_idx=len(self.label_map) - 1)
-        batch_seq_lens = np.array(batch_seq_lens).astype("int64").reshape(
-            [-1, 1])
 
         return_list = [
             padded_token_ids, padded_text_type_ids, padded_position_ids,
@@ -337,5 +335,30 @@ class SequenceLabelReader(BaseReader):
         return record
 
 
+class ExtractEmbeddingReader(BaseReader):
+    def _pad_batch_records(self, batch_records):
+        batch_token_ids = [record.token_ids for record in batch_records]
+        batch_text_type_ids = [record.text_type_ids for record in batch_records]
+        batch_position_ids = [record.position_ids for record in batch_records]
+
+        # padding
+        padded_token_ids, input_mask, seq_lens = pad_batch_data(
+            batch_token_ids,
+            pad_idx=self.pad_id,
+            return_input_mask=True,
+            return_seq_lens=True)
+        padded_text_type_ids = pad_batch_data(
+            batch_text_type_ids, pad_idx=self.pad_id)
+        padded_position_ids = pad_batch_data(
+            batch_position_ids, pad_idx=self.pad_id)
+
+        return_list = [
+            padded_token_ids, padded_text_type_ids, padded_position_ids,
+            input_mask, seq_lens
+        ]
+
+        return return_list
+
+
 if __name__ == '__main__':
     pass

ERNIE/run_classifier.py

@@ -19,18 +19,15 @@ from __future__ import print_function
 
 import os
 import time
-import argparse
-import numpy as np
 import multiprocessing
 
-import paddle
 import paddle.fluid as fluid
 
 import reader.task_reader as task_reader
 from model.ernie import ErnieConfig
 from finetune.classifier import create_model, evaluate
 from optimization import optimization
-from utils.args import ArgumentGroup, print_arguments
+from utils.args import print_arguments
 from utils.init import init_pretraining_params, init_checkpoint
 from finetune_args import parser
 
@@ -184,12 +181,6 @@ def main(args):
     else:
         train_exe = None
 
-    if args.do_val or args.do_test:
-        test_exe = fluid.ParallelExecutor(
-            use_cuda=args.use_cuda,
-            main_program=test_prog,
-            share_vars_from=train_exe)
-
     if args.do_train:
         train_pyreader.start()
         steps = 0
@@ -238,7 +229,8 @@ def main(args):
                                 batch_size=args.batch_size,
                                 epoch=1,
                                 shuffle=False))
-                        evaluate(exe, test_prog, test_pyreader, graph_vars, "dev")
+                        evaluate(exe, test_prog, test_pyreader, graph_vars,
+                                 "dev")
                     # evaluate test set
                     if args.do_test:
                         test_pyreader.decorate_tensor_provider(
@@ -247,7 +239,8 @@ def main(args):
                                 batch_size=args.batch_size,
                                 epoch=1,
                                 shuffle=False))
-                        evaluate(exe, test_prog, test_pyreader, graph_vars, "test")
+                        evaluate(exe, test_prog, test_pyreader, graph_vars,
+                                 "test")
             except fluid.core.EOFException:
                 save_path = os.path.join(args.checkpoints, "step_" + str(steps))
                 fluid.io.save_persistables(exe, save_path, train_program)

ERNIE/run_sequence_labeling.py

@@ -19,10 +19,8 @@ from __future__ import print_function
 
 import os
 import time
-import numpy as np
 import multiprocessing
 
-import paddle
 import paddle.fluid as fluid
 
 import reader.task_reader as task_reader
@@ -264,7 +262,8 @@ def main(args):
                 epoch=1,
                 shuffle=False))
         print("Final validation result:")
-        evaluate(exe, test_prog, test_pyreader, graph_vars, args.num_labels, "dev")
+        evaluate(exe, test_prog, test_pyreader, graph_vars, args.num_labels,
+                 "dev")
 
     # final eval on test set
     if args.do_test:
@@ -275,7 +274,8 @@ def main(args):
                 epoch=1,
                 shuffle=False))
         print("Final test result:")
-        evaluate(exe, test_prog, test_pyreader, graph_vars, args.num_labels, "test")
+        evaluate(exe, test_prog, test_pyreader, graph_vars, args.num_labels,
+                 "test")
 
 
 if __name__ == '__main__':

ERNIE/train.py

@@ -19,17 +19,15 @@ from __future__ import print_function
 
 import os
 import time
-import argparse
-import numpy as np
 import multiprocessing
 
-import paddle
+import numpy as np
 import paddle.fluid as fluid
 
 from reader.pretraining import ErnieDataReader
 from model.ernie import ErnieModel, ErnieConfig
 from optimization import optimization
-from utils.args import ArgumentGroup, print_arguments
+from utils.args import print_arguments
 from utils.init import init_checkpoint, init_pretraining_params
 
 from pretrain_args import parser