release 0.3

demo/demo3/paddlepalm

+../../paddlepalm/
\ No newline at end of file

demo/demo3/pretrain

+/home/zhangyiming/yiming/v02-exe/pretrain
\ No newline at end of file

demo/demo3/run.py

+# coding=utf-8
 import paddlepalm as palm
+import json
 
 if __name__ == '__main__':
 
     max_seqlen = 512
-    batch_size = 32
+    batch_size = 3
+    num_epochs = 2
+    lr = 1e-3
+    vocab_path = './pretrain/ernie/vocab.txt'
+
+    train_file = './data/cls4mrqa/train.tsv'
+
+    config = json.load(open('./pretrain/ernie/ernie_config.json'))
+    print(config)
+    ernie = palm.backbone.ERNIE.from_config(config)
+    pred_ernie = palm.backbone.ERNIE.from_config(config, phase='pred')
+
+    # cls_reader2 = palm.reader.cls(train_file_topic, vocab_path, batch_size, max_seqlen)
+    # cls_reader3 = palm.reader.cls(train_file_subj, vocab_path, batch_size, max_seqlen)
+    # topic_trainer = palm.Trainer('topic_cls', cls_reader2, cls)
+    # subj_trainer = palm.Trainer('subj_cls', cls_reader3, cls)
+
+    # 创建该分类任务的reader，由诸多参数控制数据集读入格式、文件数量、预处理规则等
+    cls_reader = palm.reader.ClassifyReader(vocab_path, batch_size, max_seqlen)
+    print(cls_reader.outputs_attr)
+    # 不同的backbone会对任务reader有不同的特征要求，例如对于分类任务，基本的输入feature为token_ids和label_ids，但是对于BERT，还要求从输入中额外提取position、segment、input_mask等特征，因此经过register后，reader会自动补充backbone所要求的字段
+    cls_reader.register_with(ernie)
+    print(cls_reader.outputs_attr)
+    # 创建任务头（task head），如分类、匹配、机器阅读理解等。每个任务头有跟该任务相关的必选/可选参数。注意，任务头与reader是解耦合的，只要任务头依赖的数据集侧的字段能被reader提供，那么就是合法的
+    cls_head = palm.head.Classify(4, 1024, 0.1)
+    cls_pred_head = palm.head.Classify(4, 1024, 0.1, phase='pred')
+
+    # 根据reader和任务头来创建一个训练器trainer，trainer代表了一个训练任务，内部维护着训练进程、和任务的关键信息，并完成合法性校验，该任务的模型保存、载入等相关规则控制
+    trainer = palm.Trainer('senti_cls', cls_reader, cls_head, save_predict_model=True, \
+                           pred_head=cls_pred_head, save_path='./output')
+
+    # match4mrqa.reuse_head_with(mrc4mrqa)
+
+    # data_vars = cls_reader.build()
+    # output_vars = ernie.build(data_vars)
+    # cls_head.build({'backbone': output_vars, 'reader': data_vars})
+
+    loss_var = trainer.build_forward(ernie, pred_ernie)
+
+    # controller.build_forward()
+    # Error! a head/backbone can be only build once! Try NOT to call build_forward method for any Trainer!
+
+    print(trainer.num_examples)
+    trainer.load_data(train_file, 'csv', num_epochs=2, batch_size=32)
+    print(trainer.num_examples)
+
+    n_steps = trainer.num_examples * num_epochs // batch_size
+    warmup_steps = int(0.1 * n_steps)
+    print(warmup_steps)
+    sched = palm.lr_sched.TriangularSchedualer(warmup_steps, n_steps)
+
+    adam = palm.optimizer.Adam(loss_var, lr, sched)
+
+    trainer.build_backward(optimizer=adam, weight_decay=0.001, \
+                           use_ema=True, ema_decay=0.999)
+
+    trainer.random_init_params()
+    trainer.load_pretrain('../../pretrain_model/ernie/params')
+
+    # trainer.train_one_step()
+    # trainer.train_one_epoch()
+    trainer.train()
+    trainer.save()
+
+
 
-    match_reader = palm.reader.match(train_file, vocab, \
-        max_seqlen, file_format='csv', tokenizer='wordpiece', \
-        lang='en', shuffle_train=True)
-    mrc_reader = palm.reader.mrc(train_file, phase='train')
-    mlm_reader = palm.reader.mlm(train_file, phase='train')
-    palm.reader.
 
-    match = palm.tasktype.cls(num_classes=4)
-    mrc = palm.tasktype.match(learning_strategy='pairwise')
-    mlm = palm.tasktype.mlm()
-    mlm.print()
 
-    
-    bb_flags = palm.load_json('./pretrain/ernie/ernie_config.json')
-    bb = palm.backbone.ernie(bb_flags['xx'], xxx)
-    bb.print()
 
-    match4mrqa = palm.Task('match4mrqa', match_reader, match_tt)
-    mrc4mrqa = palm.Task('match4mrqa', match_reader, match_tt)
 
-    # match4mrqa.reuse_with(mrc4mrqa)
 
 
-    controller = palm.Controller([mrqa, match4mrqa, mlm4mrqa])
+    # controller = palm.Controller([mrqa, match4mrqa, mlm4mrqa])
 
     loss = controller.build_forward(bb, mask_task=[])
 

paddlepalm/__init__.py

+import downloader
+from mtl_controller import Controller 
 
-import sys
-from paddlepalm.mtl_controller import Controller
-from paddlepalm.task_instance import Task
-
-sys.path.append('paddlepalm')
 
+del interface
+del task_instance
+del default_settings
+del utils
+del mtl_controller
\ No newline at end of file

paddlepalm/_downloader.py

+# -*- coding: UTF-8 -*-
+#   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.
+
+from __future__ import print_function
+import os
+import tarfile
+import shutil
+try:
+    from urllib.request import urlopen # Python 3
+except ImportError:
+    from urllib2 import urlopen # Python 2
+
+
+import ssl
+
+__all__ = ["download", "ls"]
+
+# for https
+ssl._create_default_https_context = ssl._create_unverified_context
+
+_items = {
+    'pretrain': {'ernie-en-uncased-large': 'https://ernie.bj.bcebos.com/ERNIE_Large_en_stable-2.0.0.tar.gz',
+                 'bert-en-uncased-large': 'https://bert-models.bj.bcebos.com/uncased_L-24_H-1024_A-16.tar.gz',
+                 'bert-en-uncased-base': 'https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz',
+                 'utils': None},
+    'reader': {'utils': None},
+    'backbone': {'utils': None},
+    'tasktype': {'utils': None},
+}
+
+def _download(item, scope, path, silent=False):
+    data_url = _items[item][scope]
+    if data_url == None:
+        return
+    if not silent:
+        print('Downloading {}: {} from {}...'.format(item, scope, data_url))
+    data_dir = path + '/' + item + '/' + scope
+    if not os.path.exists(data_dir):
+        os.makedirs(os.path.join(data_dir))
+    data_name = data_url.split('/')[-1]
+    filename = data_dir + '/' + data_name
+
+    # print process
+    def _chunk_report(bytes_so_far, total_size):
+        percent = float(bytes_so_far) / float(total_size)
+        if percent > 1:
+            percent = 1
+        if not silent:
+            print('\r>> Downloading... {:.1%}'.format(percent), end = "")
+    
+    # copy to local
+    def _chunk_read(response, url, chunk_size = 16 * 1024, report_hook = None):
+        total_size = response.info().getheader('Content-Length').strip()
+        total_size = int(total_size)
+        bytes_so_far = 0
+        with open("%s" % filename, "wb") as f:
+            while 1:
+                chunk = response.read(chunk_size)
+                f.write(chunk)
+                f.flush() 
+                bytes_so_far += len(chunk)
+                if not chunk:
+                    break
+                if report_hook:
+                    report_hook(bytes_so_far, total_size)
+        return bytes_so_far
+
+    response = urlopen(data_url)
+    _chunk_read(response, data_url, report_hook=_chunk_report)
+    
+    if not silent:
+        print(' done!')
+    
+    if item == 'pretrain':
+        if not silent:
+            print ('Extracting {}...'.format(data_name), end=" ")
+        if os.path.exists(filename):
+            tar = tarfile.open(filename, 'r')
+            tar.extractall(path = data_dir)
+            tar.close()
+            os.remove(filename)
+        if scope.startswith('bert'):
+            source_path = data_dir + '/' + data_name.split('.')[0]
+            fileList = os.listdir(source_path)
+            for file in fileList:
+                filePath = os.path.join(source_path, file)
+                shutil.move(filePath, data_dir)
+            os.removedirs(source_path)
+        if not silent:
+            print ('done!')
+        if not silent:
+            print ('Converting params...', end=" ")
+        _convert(data_dir, silent)
+        if not silent:
+            print ('done!')
+
+
+def _convert(path, silent=False):
+    if os.path.isfile(path + '/params/__palminfo__'):
+        if not silent:
+            print ('already converted.')
+    else:
+        if os.path.exists(path + '/params/'):
+            os.rename(path + '/params/', path + '/params1/')
+            os.mkdir(path + '/params/')
+            tar_model = tarfile.open(path + '/params/' + '__palmmodel__', 'w')
+            tar_info = open(path + '/params/'+ '__palminfo__', 'w')
+            for root, dirs, files in os.walk(path + '/params1/'):
+                for file in files:
+                    src_file = os.path.join(root, file)
+                    tar_model.add(src_file, '__paddlepalm_' + file)
+                    tar_info.write('__paddlepalm_' + file)
+                    os.remove(src_file)
+            tar_model.close()
+            tar_info.close()
+            os.removedirs(path + '/params1/') 
+
+def download(item, scope='all', path='.'):
+    item = item.lower()
+    scope = scope.lower()
+    assert item in _items, '{} is not found. Support list: {}'.format(item, list(_items.keys()))
+   
+    if _items[item]['utils'] is not None:
+        _download(item, 'utils', path, silent=True)
+
+    if scope != 'all':
+        assert scope in _items[item], '{} is not found. Support scopes: {}'.format(scope, list(_items[item].keys()))
+        _download(item, scope, path)
+    else:
+        for s in _items[item].keys():
+            _download(item, s, path)
+
+
+def _ls(item, scope, l = 10):
+    if scope != 'all':
+        assert scope in _items[item], '{} is not found. Support scopes: {}'.format(scope, list(_items[item].keys()))
+        print ('{}'.format(scope))
+    else:
+        for s in _items[item].keys():
+            if s == 'utils':
+                continue
+            print ('  => '+s)
+
+def ls(item='all', scope='all'):
+    
+    if scope == 'utils':
+        return
+    if item != 'all':
+        assert item in _items, '{} is not found. Support scopes: {}'.format(item, list(_items.keys()))
+        print ('Available {} items:'.format(item))
+        _ls(item, scope)
+    else:
+        l = max(map(len, _items.keys()))
+        for i in _items.keys():
+            print ('Available {} items: '.format(i))
+            _ls(i, scope, l)
+
+
+    

paddlepalm/backbone/bert.py

@@ -52,9 +52,9 @@ class Model(backbone):
 
     @property
     def inputs_attr(self):
-        return {"token_ids": [[-1, -1, 1], 'int64'],
-                "position_ids": [[-1, -1, 1], 'int64'],
-                "segment_ids": [[-1, -1, 1], 'int64'],
+        return {"token_ids": [[-1, -1], 'int64'],
+                "position_ids": [[-1, -1], 'int64'],
+                "segment_ids": [[-1, -1], 'int64'],
                 "input_mask": [[-1, -1, 1], 'float32']}
 
     @property
@@ -73,7 +73,7 @@ class Model(backbone):
 
         self._emb_dtype = 'float32'
         # padding id in vocabulary must be set to 0
-        emb_out = fluid.layers.embedding(
+        emb_out = fluid.embedding(
             input=src_ids,
             size=[self._voc_size, self._emb_size],
             dtype=self._emb_dtype,
@@ -84,14 +84,14 @@ class Model(backbone):
         # fluid.global_scope().find_var('backbone-word_embedding').get_tensor()
         embedding_table = fluid.default_main_program().global_block().var(scope_name+self._word_emb_name)
         
-        position_emb_out = fluid.layers.embedding(
+        position_emb_out = fluid.embedding(
             input=pos_ids,
             size=[self._max_position_seq_len, self._emb_size],
             dtype=self._emb_dtype,
             param_attr=fluid.ParamAttr(
                 name=scope_name+self._pos_emb_name, initializer=self._param_initializer))
 
-        sent_emb_out = fluid.layers.embedding(
+        sent_emb_out = fluid.embedding(
             sent_ids,
             size=[self._sent_types, self._emb_size],
             dtype=self._emb_dtype,

paddlepalm/backbone/ernie.py

@@ -62,11 +62,11 @@ class Model(backbone):
 
     @property
     def inputs_attr(self):
-        return {"token_ids": [[-1, -1, 1], 'int64'],
-                "position_ids": [[-1, -1, 1], 'int64'],
-                "segment_ids": [[-1, -1, 1], 'int64'],
+        return {"token_ids": [[-1, -1], 'int64'],
+                "position_ids": [[-1, -1], 'int64'],
+                "segment_ids": [[-1, -1], 'int64'],
                 "input_mask": [[-1, -1, 1], 'float32'],
-                "task_ids": [[-1,-1, 1], 'int64']}
+                "task_ids": [[-1,-1], 'int64']}
 
     @property
     def outputs_attr(self):
@@ -85,7 +85,7 @@ class Model(backbone):
         task_ids = inputs['task_ids']
 
         # padding id in vocabulary must be set to 0
-        emb_out = fluid.layers.embedding(
+        emb_out = fluid.embedding(
             input=src_ids,
             size=[self._voc_size, self._emb_size],
             dtype=self._emb_dtype,
@@ -96,14 +96,14 @@ class Model(backbone):
         # fluid.global_scope().find_var('backbone-word_embedding').get_tensor()
         embedding_table = fluid.default_main_program().global_block().var(scope_name+self._word_emb_name)
         
-        position_emb_out = fluid.layers.embedding(
+        position_emb_out = fluid.embedding(
             input=pos_ids,
             size=[self._max_position_seq_len, self._emb_size],
             dtype=self._emb_dtype,
             param_attr=fluid.ParamAttr(
                 name=scope_name+self._pos_emb_name, initializer=self._param_initializer))
 
-        sent_emb_out = fluid.layers.embedding(
+        sent_emb_out = fluid.embedding(
             sent_ids,
             size=[self._sent_types, self._emb_size],
             dtype=self._emb_dtype,
@@ -113,7 +113,7 @@ class Model(backbone):
         emb_out = emb_out + position_emb_out
         emb_out = emb_out + sent_emb_out
 
-        task_emb_out = fluid.layers.embedding(
+        task_emb_out = fluid.embedding(
             task_ids,
             size=[self._task_types, self._emb_size],
             dtype=self._emb_dtype,

paddlepalm/downloader.py

-
-from __future__ import print_function
-import os
-
-items = {
-    'pretrain': {'ernie-en-uncased-large': 'http://xxxxx',
-                 'xxx': 'xxx',
-                 'utils': None}
-    'reader': {'cls': 'xxx',
-               'xxx': 'xxx',
-               'utils': 'xxx'}
-    'backbone': {xxx}
-    'tasktype': {xxx}
-}
-
-
-def download(item, scope='all', path='.'):
-    item = item.lower()
-    scope = scope.lower()
-    assert item in items, '{} is not found. Support list: {}'.format(item, list(items.keys()))
-
-    if not os.path.exists(path, item):
-        os.makedirs(os.path.join(path, item))
-
-    def _download(item, scope, silent=False):
-        if not silent:
-            print('downloading {}: {} from {}...'.format(item, scope, items[item][scope]), end='')
-        urllib.downloadxxx(items[item][scope])
-        if not silent:
-            print('done!')
-        
-    if items['utils'] is not None:
-        _download(item, 'utils', silent=True)
-
-    if scope != 'all':
-        assert scope in items[item], '{} is not found. Support scopes: {}'.format(item, list(items[item].keys()))
-        _download(item, scope)
-    else:
-        for s in items[item].keys():
-            _download(item, s)
-
-
-def ls(item=None, scope='all'):
-    pass
-
+from _downloader import *