Add nlp module (#433)

* add nlpmodule base class

paddlehub/__init__.py

@@ -64,4 +64,4 @@ from .finetune.strategy import CombinedStrategy
 
 from .autofinetune.evaluator import report_final_result
 
-from .module.nlp_module import BERTModule
+from .module.nlp_module import NLPPredictionModule, TransformerModule

paddlehub/module/module.py

-#coding:utf-8
+# coding:utf-8
 # Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"
@@ -135,19 +135,27 @@ class Module(object):
         if "_is_initialize" in self.__dict__ and self._is_initialize:
             return
 
-        mod = self.__class__.__module__ + "." + self.__class__.__name__
-        if mod in _module_runnable_func:
-            _run_func_name = _module_runnable_func[mod]
-            self._run_func = getattr(self, _run_func_name)
-        else:
-            self._run_func = None
-        self._serving_func_name = _module_serving_func.get(mod, None)
-        self._code_version = "v2"
+        _run_func_name = self._get_func_name(self.__class__,
+                                             _module_runnable_func)
+        self._run_func = getattr(self, _run_func_name)
+        self._serving_func_name = self._get_func_name(self.__class__,
+                                                      _module_serving_func)
         self._directory = directory
         self._initialize(**kwargs)
         self._is_initialize = True
         self._code_version = "v2"
 
+    def _get_func_name(self, current_cls, module_func_dict):
+        mod = current_cls.__module__ + "." + current_cls.__name__
+        if mod in module_func_dict:
+            _func_name = module_func_dict[mod]
+            return _func_name
+        elif current_cls.__bases__:
+            for base_class in current_cls.__bases__:
+                return self._get_func_name(base_class, module_func_dict)
+        else:
+            return None
+
     @classmethod
     def init_with_name(cls, name, version=None, **kwargs):
         fp_lock = open(os.path.join(CACHE_HOME, name), "a")

paddlehub/module/nlp_module.py

-#coding:utf-8
+# coding:utf-8
 # Copyright (c) 2019  PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"
@@ -17,15 +17,198 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import argparse
+import ast
+import json
 import os
 import re
+import six
 
-import paddlehub as hub
+import numpy as np
 import paddle.fluid as fluid
-from paddlehub import logger
+from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
+import paddlehub as hub
+from paddlehub.common.logger import logger
+from paddlehub.common.utils import sys_stdin_encoding
+from paddlehub.io.parser import txt_parser
+from paddlehub.module.module import runnable
+
+
+class DataFormatError(Exception):
+    def __init__(self, *args):
+        self.args = args
+
+
+class NLPBaseModule(hub.Module):
+    def _initialize(self):
+        """
+        initialize with the necessary elements
+        This method must be overrided.
+        """
+        raise NotImplementedError()
+
+    def get_vocab_path(self):
+        """
+        Get the path to the vocabulary whih was used to pretrain
+
+        Returns:
+             self.vocab_path(str): the path to vocabulary
+        """
+        return self.vocab_path
+
+
+class NLPPredictionModule(NLPBaseModule):
+    def _set_config(self):
+        """
+        predictor config setting
+        """
+        cpu_config = AnalysisConfig(self.pretrained_model_path)
+        cpu_config.disable_glog_info()
+        cpu_config.disable_gpu()
+        self.cpu_predictor = create_paddle_predictor(cpu_config)
+
+        try:
+            _places = os.environ["CUDA_VISIBLE_DEVICES"]
+            int(_places[0])
+            use_gpu = True
+        except:
+            use_gpu = False
+        if use_gpu:
+            gpu_config = AnalysisConfig(self.pretrained_model_path)
+            gpu_config.disable_glog_info()
+            gpu_config.enable_use_gpu(memory_pool_init_size_mb=500, device_id=0)
+            self.gpu_predictor = create_paddle_predictor(gpu_config)
+
+    def texts2tensor(self, texts):
+        """
+        Tranform the texts(dict) to PaddleTensor
+        Args:
+             texts(list): each element is a dict that must have a named 'processed' key whose value is word_ids, such as
+                          texts = [{'processed': [23, 89, 43, 906]}]
+        Returns:
+             tensor(PaddleTensor): tensor with texts data
+        """
+        lod = [0]
+        data = []
+        for i, text in enumerate(texts):
+            data += text['processed']
+            lod.append(len(text['processed']) + lod[i])
+        tensor = PaddleTensor(np.array(data).astype('int64'))
+        tensor.name = "words"
+        tensor.lod = [lod]
+        tensor.shape = [lod[-1], 1]
+        return tensor
+
+    def to_unicode(self, texts):
+        """
+        Convert each element's type(str) of texts(list) to unicode in python2.7
+        Args:
+             texts(list): each element's type is str in python2.7
+        Returns:
+             texts(list): each element's type is unicode in python2.7
+        """
+        if six.PY2:
+            unicode_texts = []
+            for text in texts:
+                if not isinstance(text, six.string_types):
+                    unicode_texts.append(
+                        text.decode(sys_stdin_encoding()).decode("utf8"))
+                else:
+                    unicode_texts.append(text)
+            texts = unicode_texts
+        return texts
+
+    @runnable
+    def run_cmd(self, argvs):
+        """
+        Run as a command
+        """
+        self.parser = argparse.ArgumentParser(
+            description='Run the %s module.' % self.module_name,
+            prog='hub run %s' % self.module_name,
+            usage='%(prog)s',
+            add_help=True)
+
+        self.arg_input_group = self.parser.add_argument_group(
+            title="Input options", description="Input data. Required")
+        self.arg_config_group = self.parser.add_argument_group(
+            title="Config options",
+            description=
+            "Run configuration for controlling module behavior, not required.")
+
+        self.add_module_config_arg()
+        self.add_module_input_arg()
+
+        args = self.parser.parse_args(argvs)
+
+        try:
+            input_data = self.check_input_data(args)
+        except DataFormatError and RuntimeError:
+            self.parser.print_help()
+            return None
 
+        results = self.predict(
+            texts=input_data, use_gpu=args.use_gpu, batch_size=args.batch_size)
+
+        return results
 
-class _BERTEmbeddingTask(hub.BaseTask):
+    def add_module_config_arg(self):
+        """
+        Add the command config options
+        """
+        self.arg_config_group.add_argument(
+            '--use_gpu',
+            type=ast.literal_eval,
+            default=False,
+            help="whether use GPU for prediction")
+
+        self.arg_config_group.add_argument(
+            '--batch_size',
+            type=int,
+            default=1,
+            help="batch size for prediction")
+
+    def add_module_input_arg(self):
+        """
+        Add the command input options
+        """
+        self.arg_input_group.add_argument(
+            '--input_file',
+            type=str,
+            default=None,
+            help="file contain input data")
+        self.arg_input_group.add_argument(
+            '--input_text', type=str, default=None, help="text to predict")
+
+    def check_input_data(self, args):
+        input_data = []
+        if args.input_file:
+            if not os.path.exists(args.input_file):
+                print("File %s is not exist." % args.input_file)
+                raise RuntimeError
+            else:
+                input_data = txt_parser.parse(args.input_file, use_strip=True)
+        elif args.input_text:
+            if args.input_text.strip() != '':
+                if six.PY2:
+                    input_data = [
+                        args.input_text.decode(
+                            sys_stdin_encoding()).decode("utf8")
+                    ]
+                else:
+                    input_data = [args.input_text]
+            else:
+                print(
+                    "ERROR: The input data is inconsistent with expectations.")
+
+        if input_data == []:
+            print("ERROR: The input data is inconsistent with expectations.")
+            raise DataFormatError
+
+        return input_data
+
+
+class _TransformerEmbeddingTask(hub.BaseTask):
     def __init__(self,
                  pooled_feature,
                  seq_feature,
@@ -33,7 +216,7 @@ class _BERTEmbeddingTask(hub.BaseTask):
                  data_reader,
                  config=None):
         main_program = pooled_feature.block.program
-        super(_BERTEmbeddingTask, self).__init__(
+        super(_TransformerEmbeddingTask, self).__init__(
             main_program=main_program,
             data_reader=data_reader,
             feed_list=feed_list,
@@ -57,21 +240,10 @@ class _BERTEmbeddingTask(hub.BaseTask):
         return results
 
 
-class BERTModule(hub.Module):
-    def _initialize(self):
-        """
-        Must override this method.
-
-        some member variables are required, others are optional.
-        """
-        # required config
-        self.MAX_SEQ_LEN = None
-        self.params_path = None
-        self.vocab_path = None
-        # optional config
-        self.spm_path = None
-        self.word_dict_path = None
-        raise NotImplementedError
+class TransformerModule(NLPBaseModule):
+    """
+    Tranformer Module base class can be used by BERT, ERNIE, RoBERTa and so on.
+    """
 
     def init_pretraining_params(self, exe, pretraining_params_path,
                                 main_program):
@@ -157,7 +329,6 @@ class BERTModule(hub.Module):
 
         place = fluid.CPUPlace()
         exe = fluid.Executor(place)
-        exe.run(startup_program)
 
         self.init_pretraining_params(
             exe, self.params_path, main_program=startup_program)
@@ -176,7 +347,7 @@ class BERTModule(hub.Module):
     def get_embedding(self, texts, use_gpu=False, batch_size=1):
         """
         get pooled_output and sequence_output for input texts.
-        Warnings: this method depends on Paddle Inference Library, it may not work properly in PaddlePaddle < 1.6.2.
+        Warnings: this method depends on Paddle Inference Library, it may not work properly in PaddlePaddle <= 1.6.2.
 
         Args:
             texts (list): each element is a text sample, each sample include text_a and text_b where text_b can be omitted.
@@ -220,7 +391,7 @@ class BERTModule(hub.Module):
                 batch_size=batch_size)
 
             self.emb_job = {}
-            self.emb_job["task"] = _BERTEmbeddingTask(
+            self.emb_job["task"] = _TransformerEmbeddingTask(
                 pooled_feature=pooled_feature,
                 seq_feature=seq_feature,
                 feed_list=feed_list,
@@ -233,9 +404,6 @@ class BERTModule(hub.Module):
         return self.emb_job["task"].predict(
             data=texts, return_result=True, accelerate_mode=True)
 
-    def get_vocab_path(self):
-        return self.vocab_path
-
     def get_spm_path(self):
         if hasattr(self, "spm_path"):
             return self.spm_path