Add teacher model of bert.

paddleslim/teachers/bert/__init__.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.
+
+from __future__ import absolute_import
+from ..bert import cls
+from .cls import *
+
+__all__ = []
+__all__ += cls.__all__

paddleslim/teachers/bert/batching.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.
+"""Mask, padding and batching."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+
+def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3):
+    """
+    Add mask for batch_tokens, return out, mask_label, mask_pos;
+    Note: mask_pos responding the batch_tokens after padded;
+    """
+    max_len = max([len(sent) for sent in batch_tokens])
+    mask_label = []
+    mask_pos = []
+    prob_mask = np.random.rand(total_token_num)
+    # Note: the first token is [CLS], so [low=1]
+    replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num)
+    pre_sent_len = 0
+    prob_index = 0
+    for sent_index, sent in enumerate(batch_tokens):
+        mask_flag = False
+        prob_index += pre_sent_len
+        for token_index, token in enumerate(sent):
+            prob = prob_mask[prob_index + token_index]
+            if prob > 0.15:
+                continue
+            elif 0.03 < prob <= 0.15:
+                # mask
+                if token != SEP and token != CLS:
+                    mask_label.append(sent[token_index])
+                    sent[token_index] = MASK
+                    mask_flag = True
+                    mask_pos.append(sent_index * max_len + token_index)
+            elif 0.015 < prob <= 0.03:
+                # random replace
+                if token != SEP and token != CLS:
+                    mask_label.append(sent[token_index])
+                    sent[token_index] = replace_ids[prob_index + token_index]
+                    mask_flag = True
+                    mask_pos.append(sent_index * max_len + token_index)
+            else:
+                # keep the original token
+                if token != SEP and token != CLS:
+                    mask_label.append(sent[token_index])
+                    mask_pos.append(sent_index * max_len + token_index)
+        pre_sent_len = len(sent)
+
+        # ensure at least mask one word in a sentence
+        while not mask_flag:
+            token_index = int(np.random.randint(1, high=len(sent) - 1, size=1))
+            if sent[token_index] != SEP and sent[token_index] != CLS:
+                mask_label.append(sent[token_index])
+                sent[token_index] = MASK
+                mask_flag = True
+                mask_pos.append(sent_index * max_len + token_index)
+    mask_label = np.array(mask_label).astype("int64").reshape([-1, 1])
+    mask_pos = np.array(mask_pos).astype("int64").reshape([-1, 1])
+    return batch_tokens, mask_label, mask_pos
+
+
+def prepare_batch_data(insts,
+                       total_token_num,
+                       voc_size=0,
+                       pad_id=None,
+                       cls_id=None,
+                       sep_id=None,
+                       mask_id=None,
+                       return_input_mask=True,
+                       return_max_len=True,
+                       return_num_token=False):
+    """
+    1. generate Tensor of data
+    2. generate Tensor of position
+    3. generate self attention mask, [shape: batch_size *  max_len * max_len]
+    """
+
+    batch_src_ids = [inst[0] for inst in insts]
+    batch_sent_ids = [inst[1] for inst in insts]
+    batch_pos_ids = [inst[2] for inst in insts]
+    labels_list = []
+    # compatible with squad, whose example includes start/end positions, 
+    # or unique id
+
+    for i in range(3, len(insts[0]), 1):
+        labels = [inst[i] for inst in insts]
+        labels = np.array(labels).astype("int64").reshape([-1, 1])
+        labels_list.append(labels)
+
+    # First step: do mask without padding
+    if mask_id >= 0:
+        out, mask_label, mask_pos = mask(
+            batch_src_ids,
+            total_token_num,
+            vocab_size=voc_size,
+            CLS=cls_id,
+            SEP=sep_id,
+            MASK=mask_id)
+    else:
+        out = batch_src_ids
+    # Second step: padding
+    src_id, self_input_mask = pad_batch_data(
+        out, pad_idx=pad_id, return_input_mask=True)
+    pos_id = pad_batch_data(
+        batch_pos_ids,
+        pad_idx=pad_id,
+        return_pos=False,
+        return_input_mask=False)
+    sent_id = pad_batch_data(
+        batch_sent_ids,
+        pad_idx=pad_id,
+        return_pos=False,
+        return_input_mask=False)
+
+    if mask_id >= 0:
+        return_list = [
+            src_id, pos_id, sent_id, self_input_mask, mask_label, mask_pos
+        ] + labels_list
+    else:
+        return_list = [src_id, pos_id, sent_id, self_input_mask] + labels_list
+
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+def pad_batch_data(insts,
+                   pad_idx=0,
+                   return_pos=False,
+                   return_input_mask=False,
+                   return_max_len=False,
+                   return_num_token=False):
+    """
+    Pad the instances to the max sequence length in batch, and generate the
+    corresponding position data and input mask.
+    """
+    return_list = []
+    max_len = max(len(inst) for inst in insts)
+    # Any token included in dict can be used to pad, since the paddings' loss
+    # will be masked out by weights and make no effect on parameter gradients.
+
+    inst_data = np.array([
+        list(inst) + list([pad_idx] * (max_len - len(inst))) for inst in insts
+    ])
+    return_list += [inst_data.astype("int64").reshape([-1, max_len])]
+
+    # position data
+    if return_pos:
+        inst_pos = np.array([
+            list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst))
+            for inst in insts
+        ])
+
+        return_list += [inst_pos.astype("int64").reshape([-1, max_len])]
+
+    if return_input_mask:
+        # This is used to avoid attention on paddings.
+        input_mask_data = np.array([[1] * len(inst) + [0] *
+                                    (max_len - len(inst)) for inst in insts])
+        input_mask_data = np.expand_dims(input_mask_data, axis=-1)
+        return_list += [input_mask_data.astype("float32")]
+
+    if return_max_len:
+        return_list += [max_len]
+
+    if return_num_token:
+        num_token = 0
+        for inst in insts:
+            num_token += len(inst)
+        return_list += [num_token]
+
+    return return_list if len(return_list) > 1 else return_list[0]
+
+
+if __name__ == "__main__":
+    pass

paddleslim/teachers/bert/cls.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.
+"""BERT fine-tuning in Paddle Dygraph Mode."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import six
+import sys
+if six.PY2:
+    reload(sys)
+    sys.setdefaultencoding('utf8')
+import ast
+import time
+import argparse
+import numpy as np
+import multiprocessing
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.dygraph import to_variable, Layer
+import reader.cls as reader
+from .model.bert import BertConfig
+from .model.cls import ClsModelLayer
+from .optimization import Optimizer
+from .utils.args import ArgumentGroup, print_arguments, check_cuda
+from .utils.init import init_from_static_model
+
+__all__ = ["BERTClassifier"]
+
+
+def create_data(batch):
+    """
+    convert data to variable
+    """
+    src_ids = to_variable(batch[0], "src_ids")
+    position_ids = to_variable(batch[1], "position_ids")
+    sentence_ids = to_variable(batch[2], "sentence_ids")
+    input_mask = to_variable(batch[3], "input_mask")
+    labels = to_variable(batch[4], "labels")
+    labels.stop_gradient = True
+    return src_ids, position_ids, sentence_ids, input_mask, labels
+
+
+class BERTClassifier(Layer):
+    def __init__(self,
+                 num_labels,
+                 task_name="mnli",
+                 model_path=None,
+                 use_cuda=True):
+        super(BERTClassifier, self).__init__()
+        self.task_name = task_name.lower()
+        BERT_BASE_PATH = "./data/pretrained_models/uncased_L-12_H-768_A-12/"
+        bert_config_path = BERT_BASE_PATH + "/bert_config.json"
+        self.vocab_path = BERT_BASE_PATH + "/vocab.txt"
+        self.init_pretraining_params = BERT_BASE_PATH + "/dygraph_params/"
+        self.do_lower_case = True
+        self.bert_config = BertConfig(bert_config_path)
+
+        if use_cuda:
+            self.dev_count = fluid.core.get_cuda_device_count()
+        else:
+            self.dev_count = int(
+                os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+
+        self.trainer_count = fluid.dygraph.parallel.Env().nranks
+
+        self.processors = {
+            'xnli': reader.XnliProcessor,
+            'cola': reader.ColaProcessor,
+            'mrpc': reader.MrpcProcessor,
+            'mnli': reader.MnliProcessor,
+        }
+
+        self.cls_model = ClsModelLayer(
+            self.bert_config, num_labels, return_pooled_out=True)
+
+        if self.init_pretraining_params:
+            print("Load pre-trained model from %s" %
+                  self.init_pretraining_params)
+            init_from_static_model(self.init_pretraining_params,
+                                   self.cls_model, self.bert_config)
+
+        if model_path is not None:
+            #restore the model
+            print("Load params from %s" % model_path)
+            model_dict, _ = fluid.load_dygraph(model_path)
+            self.cls_model.load_dict(model_dict)
+
+    def forward(self, input):
+        return self.cls_model(input)
+
+    def test(self, data_dir, batch_size=64, max_seq_len=512):
+
+        processor = self.processors[self.task_name](
+            data_dir=data_dir,
+            vocab_path=self.vocab_path,
+            max_seq_len=max_seq_len,
+            do_lower_case=self.do_lower_case,
+            in_tokens=False)
+
+        test_data_generator = processor.data_generator(
+            batch_size=batch_size, phase='dev', epoch=1, shuffle=False)
+
+        self.cls_model.eval()
+        total_cost, final_acc, avg_acc, total_num_seqs = [], [], [], []
+        for batch in test_data_generator():
+            data_ids = create_data(batch)
+
+            total_loss, _, _, np_acces, np_num_seqs = self.cls_model(data_ids)
+
+            np_loss = total_loss.numpy()
+            np_acc = np_acces[-1].numpy()
+            np_avg_acc = np.mean([acc.numpy() for acc in np_acces])
+            np_num_seqs = np_num_seqs.numpy()
+
+            total_cost.extend(np_loss * np_num_seqs)
+            final_acc.extend(np_acc * np_num_seqs)
+            avg_acc.extend(np_avg_acc * np_num_seqs)
+            total_num_seqs.extend(np_num_seqs)
+
+        print("[evaluation] classifier[-1] average acc: %f; average acc: %f" %
+              (np.sum(final_acc) / np.sum(total_num_seqs),
+               np.sum(avg_acc) / np.sum(total_num_seqs)))
+        self.cls_model.train()
+
+    def fit(self,
+            data_dir,
+            epoch,
+            batch_size=64,
+            use_cuda=True,
+            max_seq_len=512,
+            warmup_proportion=0.1,
+            use_data_parallel=False,
+            learning_rate=0.0001,
+            weight_decay=0.01,
+            lr_scheduler="linear_warmup_decay",
+            skip_steps=10,
+            save_steps=10000,
+            checkpoints="checkpoints"):
+
+        processor = self.processors[self.task_name](
+            data_dir=data_dir,
+            vocab_path=self.vocab_path,
+            max_seq_len=max_seq_len,
+            do_lower_case=self.do_lower_case,
+            in_tokens=False,
+            random_seed=5512)
+        shuffle_seed = 1 if self.trainer_count > 1 else None
+
+        train_data_generator = processor.data_generator(
+            batch_size=batch_size,
+            phase='train',
+            epoch=epoch,
+            dev_count=self.trainer_count,
+            shuffle=True,
+            shuffle_seed=shuffle_seed)
+        num_train_examples = processor.get_num_examples(phase='train')
+        max_train_steps = epoch * num_train_examples // batch_size // self.trainer_count
+        warmup_steps = int(max_train_steps * warmup_proportion)
+
+        print("Device count: %d" % self.dev_count)
+        print("Trainer count: %d" % self.trainer_count)
+        print("Num train examples: %d" % num_train_examples)
+        print("Max train steps: %d" % max_train_steps)
+        print("Num warmup steps: %d" % warmup_steps)
+
+        if use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+
+        optimizer = Optimizer(
+            warmup_steps=warmup_steps,
+            num_train_steps=max_train_steps,
+            learning_rate=learning_rate,
+            model_cls=self.cls_model,
+            weight_decay=weight_decay,
+            scheduler=lr_scheduler,
+            loss_scaling=1.0,
+            parameter_list=self.cls_model.parameters())
+
+        if use_data_parallel:
+            self.cls_model = fluid.dygraph.parallel.DataParallel(
+                self.cls_model, strategy)
+            train_data_generator = fluid.contrib.reader.distributed_batch_reader(
+                train_data_generator)
+
+        steps = 0
+        time_begin = time.time()
+
+        for batch in train_data_generator():
+            data_ids = create_data(batch)
+            total_loss, logits, losses, accuracys, num_seqs = self.cls_model(
+                data_ids)
+
+            optimizer.optimization(
+                total_loss,
+                use_data_parallel=use_data_parallel,
+                model=self.cls_model)
+            self.cls_model.clear_gradients()
+
+            if steps != 0 and steps % skip_steps == 0:
+                time_end = time.time()
+                used_time = time_end - time_begin
+                current_example, current_epoch = processor.get_train_progress()
+                localtime = time.asctime(time.localtime(time.time()))
+                print(
+                    "%s, epoch: %s, steps: %s, dy_graph loss: %f, acc: %f, speed: %f steps/s"
+                    % (localtime, current_epoch, steps, total_loss.numpy(),
+                       accuracys[-1].numpy(), skip_steps / used_time))
+                time_begin = time.time()
+
+            if steps != 0 and steps % save_steps == 0 and fluid.dygraph.parallel.Env(
+            ).local_rank == 0:
+
+                self.test(data_dir, batch_size=8, max_seq_len=512)
+
+                save_path = os.path.join(checkpoints,
+                                         "steps" + "_" + str(steps))
+                fluid.save_dygraph(self.cls_model.state_dict(), save_path)
+                fluid.save_dygraph(optimizer.optimizer.state_dict(), save_path)
+                print("Save model parameters and optimizer status at %s" %
+                      save_path)
+
+            steps += 1
+
+        if fluid.dygraph.parallel.Env().local_rank == 0:
+            save_path = os.path.join(checkpoints, "final")
+            fluid.save_dygraph(self.cls_model.state_dict(), save_path)
+            fluid.save_dygraph(optimizer.optimizer.state_dict(), save_path)
+            print("Save model parameters and optimizer status at %s" %
+                  save_path)

paddleslim/teachers/bert/model/bert.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.
+"dygraph transformer layers"
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+import json
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.dygraph import Embedding, LayerNorm, Linear, to_variable, Layer, guard
+
+from .transformer_encoder import EncoderLayer, PrePostProcessLayer
+
+
+class BertConfig(object):
+    def __init__(self, config_path):
+        self._config_dict = self._parse(config_path)
+
+    def _parse(self, config_path):
+        try:
+            with open(config_path) as json_file:
+                config_dict = json.load(json_file)
+        except Exception:
+            raise IOError("Error in parsing bert model config file '%s'" %
+                          config_path)
+        else:
+            return config_dict
+
+    def __getitem__(self, key):
+        return self._config_dict[key]
+
+    def print_config(self):
+        for arg, value in sorted(six.iteritems(self._config_dict)):
+            print('%s: %s' % (arg, value))
+        print('------------------------------------------------')
+
+
+class BertModelLayer(Layer):
+    """
+    bert
+    """
+
+    def __init__(self, config, return_pooled_out=True, use_fp16=False):
+        super(BertModelLayer, self).__init__()
+
+        self._emb_size = config['hidden_size']
+        self._n_layer = config['num_hidden_layers']
+        self._n_head = config['num_attention_heads']
+        self._voc_size = config['vocab_size']
+        self._max_position_seq_len = config['max_position_embeddings']
+        self._sent_types = config['type_vocab_size']
+        self._hidden_act = config['hidden_act']
+        self._prepostprocess_dropout = config['hidden_dropout_prob']
+        self._attention_dropout = config['attention_probs_dropout_prob']
+        self.return_pooled_out = return_pooled_out
+
+        self._word_emb_name = "word_embedding"
+        self._pos_emb_name = "pos_embedding"
+        self._sent_emb_name = "sent_embedding"
+        self._dtype = "float16" if use_fp16 else "float32"
+
+        self._param_initializer = fluid.initializer.TruncatedNormal(
+            scale=config['initializer_range'])
+
+        self._src_emb = Embedding(
+            size=[self._voc_size, self._emb_size],
+            param_attr=fluid.ParamAttr(
+                name=self._word_emb_name, initializer=self._param_initializer),
+            dtype=self._dtype)
+
+        self._pos_emb = Embedding(
+            size=[self._max_position_seq_len, self._emb_size],
+            param_attr=fluid.ParamAttr(
+                name=self._pos_emb_name, initializer=self._param_initializer),
+            dtype=self._dtype)
+
+        self._sent_emb = Embedding(
+            size=[self._sent_types, self._emb_size],
+            param_attr=fluid.ParamAttr(
+                name=self._sent_emb_name, initializer=self._param_initializer),
+            dtype=self._dtype)
+
+        self.pooled_fc = Linear(
+            input_dim=self._emb_size,
+            output_dim=self._emb_size,
+            param_attr=fluid.ParamAttr(
+                name="pooled_fc.w_0", initializer=self._param_initializer),
+            bias_attr="pooled_fc.b_0",
+            act="tanh")
+
+        self.pre_process_layer = PrePostProcessLayer(
+            "nd", self._emb_size, self._prepostprocess_dropout, "")
+
+        self._encoder = EncoderLayer(
+            hidden_act=self._hidden_act,
+            n_layer=self._n_layer,
+            n_head=self._n_head,
+            d_key=self._emb_size // self._n_head,
+            d_value=self._emb_size // self._n_head,
+            d_model=self._emb_size,
+            d_inner_hid=self._emb_size * 4,
+            prepostprocess_dropout=self._prepostprocess_dropout,
+            attention_dropout=self._attention_dropout,
+            relu_dropout=0,
+            preprocess_cmd="",
+            postprocess_cmd="dan",
+            param_initializer=self._param_initializer)
+
+    def forward(self, src_ids, position_ids, sentence_ids, input_mask):
+        """
+        forward
+        """
+        src_emb = self._src_emb(src_ids)
+        pos_emb = self._pos_emb(position_ids)
+        sent_emb = self._sent_emb(sentence_ids)
+
+        emb_out = src_emb + pos_emb
+        emb_out = emb_out + sent_emb
+
+        emb_out = self.pre_process_layer(emb_out)
+
+        self_attn_mask = fluid.layers.matmul(
+            x=input_mask, y=input_mask, transpose_y=True)
+        self_attn_mask = fluid.layers.scale(
+            x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False)
+        n_head_self_attn_mask = fluid.layers.stack(
+            x=[self_attn_mask] * self._n_head, axis=1)
+        n_head_self_attn_mask.stop_gradient = True
+
+        enc_outputs = self._encoder(emb_out, n_head_self_attn_mask)
+
+        if not self.return_pooled_out:
+            return enc_outputs
+        next_sent_feats = []
+        for enc_output in enc_outputs:
+            next_sent_feat = fluid.layers.slice(
+                input=enc_output, axes=[1], starts=[0], ends=[1])
+            next_sent_feat = self.pooled_fc(next_sent_feat)
+            next_sent_feat = fluid.layers.reshape(
+                next_sent_feat, shape=[-1, self._emb_size])
+            next_sent_feats.append(next_sent_feat)
+
+        return enc_outputs, next_sent_feats
+
+
+class PretrainModelLayer(Layer):
+    """
+    pretrain model
+    """
+
+    def __init__(self,
+                 config,
+                 return_pooled_out=True,
+                 weight_sharing=True,
+                 use_fp16=False):
+        super(PretrainModelLayer, self).__init__()
+        self.config = config
+        self._voc_size = config['vocab_size']
+        self._emb_size = config['hidden_size']
+        self._hidden_act = config['hidden_act']
+        self._prepostprocess_dropout = config['hidden_dropout_prob']
+
+        self._word_emb_name = "word_embedding"
+        self._param_initializer = fluid.initializer.TruncatedNormal(
+            scale=config['initializer_range'])
+        self._weight_sharing = weight_sharing
+        self.use_fp16 = use_fp16
+        self._dtype = "float16" if use_fp16 else "float32"
+
+        self.bert_layer = BertModelLayer(
+            config=self.config, return_pooled_out=True, use_fp16=self.use_fp16)
+
+        self.pre_process_layer = PrePostProcessLayer(
+            "n", self._emb_size, self._prepostprocess_dropout, "pre_encoder")
+
+        self.pooled_fc = Linear(
+            input_dim=self._emb_size,
+            output_dim=self._emb_size,
+            param_attr=fluid.ParamAttr(
+                name="mask_lm_trans_fc.w_0",
+                initializer=self._param_initializer),
+            bias_attr="mask_lm_trans_fc.b_0",
+            act="tanh")
+
+        self.mask_lm_out_bias_attr = fluid.ParamAttr(
+            name="mask_lm_out_fc.b_0",
+            initializer=fluid.initializer.Constant(value=0.0))
+
+        if not self._weight_sharing:
+            self.out_fc = Linear(
+                input_dim=self._emb_size,
+                output_dim=self._voc_size,
+                param_attr=fluid.ParamAttr(
+                    name="mask_lm_out_fc.w_0",
+                    initializer=self._param_initializer),
+                bias_attr=self.mask_lm_out_bias_attr)
+        else:
+            self.fc_create_params = self.create_parameter(
+                shape=[self._voc_size],
+                dtype=self._dtype,
+                attr=self.mask_lm_out_bias_attr,
+                is_bias=True)
+
+        self.next_sent_fc = Linear(
+            input_dim=self._emb_size,
+            output_dim=2,
+            param_attr=fluid.ParamAttr(
+                name="next_sent_fc.w_0", initializer=self._param_initializer),
+            bias_attr="next_sent_fc.b_0")
+
+    def forward(self, src_ids, position_ids, sentence_ids, input_mask,
+                mask_label, mask_pos, labels):
+        """
+        forward
+        """
+        mask_pos = fluid.layers.cast(x=mask_pos, dtype='int32')
+
+        enc_output, next_sent_feat = self.bert_layer(src_ids, position_ids,
+                                                     sentence_ids, input_mask)
+        reshaped_emb_out = fluid.layers.reshape(
+            x=enc_output, shape=[-1, self._emb_size])
+
+        mask_feat = fluid.layers.gather(input=reshaped_emb_out, index=mask_pos)
+
+        mask_trans_feat = self.pooled_fc(mask_feat)
+        mask_trans_feat = self.pre_process_layer(None, mask_trans_feat, "n",
+                                                 self._prepostprocess_dropout)
+
+        if self._weight_sharing:
+            fc_out = fluid.layers.matmul(
+                x=mask_trans_feat,
+                y=self.bert_layer._src_emb._w,
+                transpose_y=True)
+            fc_out += self.fc_create_params
+        else:
+            fc_out = self.out_fc(mask_trans_feat)
+
+        mask_lm_loss = fluid.layers.softmax_with_cross_entropy(
+            logits=fc_out, label=mask_label)
+        mean_mask_lm_loss = fluid.layers.mean(mask_lm_loss)
+
+        next_sent_fc_out = self.next_sent_fc(next_sent_feat)
+
+        next_sent_loss, next_sent_softmax = fluid.layers.softmax_with_cross_entropy(
+            logits=next_sent_fc_out, label=labels, return_softmax=True)
+
+        next_sent_acc = fluid.layers.accuracy(
+            input=next_sent_softmax, label=labels)
+
+        mean_next_sent_loss = fluid.layers.mean(next_sent_loss)
+
+        loss = mean_next_sent_loss + mean_mask_lm_loss
+        return next_sent_acc, mean_mask_lm_loss, loss

paddleslim/teachers/bert/model/cls.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.
+"dygraph transformer layers"
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import six
+import json
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.dygraph import Linear, Layer
+
+from .bert import BertModelLayer
+
+
+class ClsModelLayer(Layer):
+    """
+    classify model
+    """
+
+    def __init__(self,
+                 config,
+                 num_labels,
+                 is_training=True,
+                 return_pooled_out=True,
+                 loss_scaling=1.0,
+                 use_fp16=False):
+        super(ClsModelLayer, self).__init__()
+        self.config = config
+        self.is_training = is_training
+        self.use_fp16 = use_fp16
+        self.loss_scaling = loss_scaling
+
+        self.bert_layer = BertModelLayer(
+            config=self.config, return_pooled_out=True, use_fp16=self.use_fp16)
+
+        self.cls_fc = Linear(
+            input_dim=self.config["hidden_size"],
+            output_dim=num_labels,
+            param_attr=fluid.ParamAttr(
+                name="cls_out_w",
+                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
+            bias_attr=fluid.ParamAttr(
+                name="cls_out_b", initializer=fluid.initializer.Constant(0.)))
+
+    def forward(self, data_ids):
+        """
+        forward
+        """
+        src_ids = data_ids[0]
+        position_ids = data_ids[1]
+        sentence_ids = data_ids[2]
+        input_mask = data_ids[3]
+        labels = data_ids[4]
+
+        enc_outputs, next_sent_feats = self.bert_layer(
+            src_ids, position_ids, sentence_ids, input_mask)
+        logits = []
+        losses = []
+        accuracys = []
+        for next_sent_feat in next_sent_feats:
+
+            cls_feat = fluid.layers.dropout(
+                x=next_sent_feat,
+                dropout_prob=0.1,
+                dropout_implementation="upscale_in_train")
+            logit = self.cls_fc(cls_feat)
+            logits.append(logit)
+
+            ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
+                logits=logit, label=labels, return_softmax=True)
+            loss = fluid.layers.mean(x=ce_loss)
+            losses.append(loss)
+
+            if self.use_fp16 and self.loss_scaling > 1.0:
+                loss *= self.loss_scaling
+
+            num_seqs = fluid.layers.create_tensor(dtype='int64')
+            accuracy = fluid.layers.accuracy(
+                input=probs, label=labels, total=num_seqs)
+            accuracys.append(accuracy)
+        total_loss = fluid.layers.sum(losses)
+
+        return total_loss, logits, losses, accuracys, num_seqs

paddleslim/teachers/bert/model/transformer_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.
+"dygraph transformer layers"
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.dygraph import Embedding, LayerNorm, Linear, Layer
+
+
+class PrePostProcessLayer(Layer):
+    """
+    PrePostProcessLayer
+    """
+
+    def __init__(self, process_cmd, d_model, dropout_rate, name):
+        super(PrePostProcessLayer, self).__init__()
+        self.process_cmd = process_cmd
+        self.functors = []
+        self.exec_order = ""
+
+        for cmd in self.process_cmd:
+            if cmd == "a":  # add residual connection
+                self.functors.append(
+                    lambda x, y: x + y if y is not None else x)
+                self.exec_order += "a"
+            elif cmd == "n":  # add layer normalization
+                self.functors.append(
+                    self.add_sublayer(
+                        "layer_norm_%d" % len(
+                            self.sublayers(include_sublayers=False)),
+                        LayerNorm(
+                            normalized_shape=d_model,
+                            param_attr=fluid.ParamAttr(
+                                name=name + "_layer_norm_scale",
+                                initializer=fluid.initializer.Constant(1.)),
+                            bias_attr=fluid.ParamAttr(
+                                name=name + "_layer_norm_bias",
+                                initializer=fluid.initializer.Constant(0.)))))
+                self.exec_order += "n"
+            elif cmd == "d":  # add dropout
+                if dropout_rate:
+                    self.functors.append(lambda x: fluid.layers.dropout(
+                        x, dropout_prob=dropout_rate, is_test=False))
+                    self.exec_order += "d"
+
+    def forward(self, x, residual=None):
+        for i, cmd in enumerate(self.exec_order):
+            if cmd == "a":
+                x = self.functors[i](x, residual)
+            else:
+                x = self.functors[i](x)
+        return x
+
+
+class PositionwiseFeedForwardLayer(Layer):
+    """
+    PositionwiseFeedForwardLayer
+    """
+
+    def __init__(self,
+                 hidden_act,
+                 d_inner_hid,
+                 d_model,
+                 dropout_rate,
+                 param_initializer=None,
+                 name=""):
+        super(PositionwiseFeedForwardLayer, self).__init__()
+
+        self._i2h = Linear(
+            input_dim=d_model,
+            output_dim=d_inner_hid,
+            param_attr=fluid.ParamAttr(
+                name=name + '_fc_0.w_0', initializer=param_initializer),
+            bias_attr=name + '_fc_0.b_0',
+            act=hidden_act)
+
+        self._h2o = Linear(
+            input_dim=d_inner_hid,
+            output_dim=d_model,
+            param_attr=fluid.ParamAttr(
+                name=name + '_fc_1.w_0', initializer=param_initializer),
+            bias_attr=name + '_fc_1.b_0')
+
+        self._dropout_rate = dropout_rate
+
+    def forward(self, x):
+        """
+        forward
+        :param x:
+        :return:
+        """
+        hidden = self._i2h(x)
+        if self._dropout_rate:
+            hidden = fluid.layers.dropout(
+                hidden,
+                dropout_prob=self._dropout_rate,
+                upscale_in_train="upscale_in_train",
+                is_test=False)
+        out = self._h2o(hidden)
+        return out
+
+
+class MultiHeadAttentionLayer(Layer):
+    """
+    MultiHeadAttentionLayer
+    """
+
+    def __init__(self,
+                 d_key,
+                 d_value,
+                 d_model,
+                 n_head=1,
+                 dropout_rate=0.,
+                 cache=None,
+                 gather_idx=None,
+                 static_kv=False,
+                 param_initializer=None,
+                 name=""):
+        super(MultiHeadAttentionLayer, self).__init__()
+        self._n_head = n_head
+        self._d_key = d_key
+        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,
+            param_attr=fluid.ParamAttr(
+                name=name + '_query_fc.w_0', initializer=param_initializer),
+            bias_attr=name + '_query_fc.b_0')
+
+        self._k_fc = Linear(
+            input_dim=d_model,
+            output_dim=d_key * n_head,
+            param_attr=fluid.ParamAttr(
+                name=name + '_key_fc.w_0', initializer=param_initializer),
+            bias_attr=name + '_key_fc.b_0')
+
+        self._v_fc = Linear(
+            input_dim=d_model,
+            output_dim=d_value * n_head,
+            param_attr=fluid.ParamAttr(
+                name=name + '_value_fc.w_0', initializer=param_initializer),
+            bias_attr=name + '_value_fc.b_0')
+
+        self._proj_fc = Linear(
+            input_dim=d_value * n_head,
+            output_dim=d_model,
+            param_attr=fluid.ParamAttr(
+                name=name + '_output_fc.w_0', initializer=param_initializer),
+            bias_attr=name + '_output_fc.b_0')
+
+    def forward(self, queries, keys, values, attn_bias):
+        """
+        forward
+        :param queries:
+        :param keys:
+        :param values:
+        :param attn_bias:
+        :return:
+        """
+        # compute q ,k ,v
+        keys = queries if keys is None else keys
+        values = keys if values is None else values
+
+        q = self._q_fc(queries)
+        k = self._k_fc(keys)
+        v = self._v_fc(values)
+
+        # split head
+
+        q_hidden_size = q.shape[-1]
+        reshaped_q = fluid.layers.reshape(
+            x=q,
+            shape=[0, 0, self._n_head, q_hidden_size // self._n_head],
+            inplace=False)
+        transpose_q = fluid.layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
+
+        k_hidden_size = k.shape[-1]
+        reshaped_k = fluid.layers.reshape(
+            x=k,
+            shape=[0, 0, self._n_head, k_hidden_size // self._n_head],
+            inplace=False)
+        transpose_k = fluid.layers.transpose(x=reshaped_k, perm=[0, 2, 1, 3])
+
+        v_hidden_size = v.shape[-1]
+        reshaped_v = fluid.layers.reshape(
+            x=v,
+            shape=[0, 0, self._n_head, v_hidden_size // self._n_head],
+            inplace=False)
+        transpose_v = fluid.layers.transpose(x=reshaped_v, perm=[0, 2, 1, 3])
+
+        scaled_q = fluid.layers.scale(x=transpose_q, scale=self._d_key**-0.5)
+        # scale dot product attention
+        product = fluid.layers.matmul(
+            #x=transpose_q,
+            x=scaled_q,
+            y=transpose_k,
+            transpose_y=True)
+        #alpha=self._d_model**-0.5)
+        if attn_bias is not None:
+            product += attn_bias
+        weights = fluid.layers.softmax(product)
+        if self._dropout_rate:
+            weights_droped = fluid.layers.dropout(
+                weights,
+                dropout_prob=self._dropout_rate,
+                dropout_implementation="upscale_in_train",
+                is_test=False)
+            out = fluid.layers.matmul(weights_droped, transpose_v)
+        else:
+            out = fluid.layers.matmul(weights, transpose_v)
+
+        # combine heads
+        if len(out.shape) != 4:
+            raise ValueError("Input(x) should be a 4-D Tensor.")
+        trans_x = fluid.layers.transpose(out, perm=[0, 2, 1, 3])
+        final_out = fluid.layers.reshape(
+            x=trans_x,
+            shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],
+            inplace=False)
+
+        # fc to output
+        proj_out = self._proj_fc(final_out)
+        return proj_out
+
+
+class EncoderSubLayer(Layer):
+    """
+    EncoderSubLayer
+    """
+
+    def __init__(self,
+                 hidden_act,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
+                 attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd="n",
+                 postprocess_cmd="da",
+                 param_initializer=None,
+                 name=""):
+
+        super(EncoderSubLayer, self).__init__()
+        self.name = name
+        self._preprocess_cmd = preprocess_cmd
+        self._postprocess_cmd = postprocess_cmd
+        self._prepostprocess_dropout = prepostprocess_dropout
+
+        self._preprocess_layer = PrePostProcessLayer(
+            self._preprocess_cmd,
+            d_model,
+            prepostprocess_dropout,
+            name=name + "_pre_att")
+
+        self._multihead_attention_layer = MultiHeadAttentionLayer(
+            d_key,
+            d_value,
+            d_model,
+            n_head,
+            attention_dropout,
+            None,
+            None,
+            False,
+            param_initializer,
+            name=name + "_multi_head_att")
+
+        self._postprocess_layer = PrePostProcessLayer(
+            self._postprocess_cmd,
+            d_model,
+            self._prepostprocess_dropout,
+            name=name + "_post_att")
+        self._preprocess_layer2 = PrePostProcessLayer(
+            self._preprocess_cmd,
+            d_model,
+            self._prepostprocess_dropout,
+            name=name + "_pre_ffn")
+
+        self._positionwise_feed_forward = PositionwiseFeedForwardLayer(
+            hidden_act,
+            d_inner_hid,
+            d_model,
+            relu_dropout,
+            param_initializer,
+            name=name + "_ffn")
+
+        self._postprocess_layer2 = PrePostProcessLayer(
+            self._postprocess_cmd,
+            d_model,
+            self._prepostprocess_dropout,
+            name=name + "_post_ffn")
+
+    def forward(self, enc_input, attn_bias):
+        """
+        forward
+        :param enc_input:
+        :param attn_bias:
+        :return:
+        """
+        pre_process_multihead = self._preprocess_layer(enc_input)
+
+        attn_output = self._multihead_attention_layer(pre_process_multihead,
+                                                      None, None, attn_bias)
+        attn_output = self._postprocess_layer(attn_output, enc_input)
+
+        pre_process2_output = self._preprocess_layer2(attn_output)
+
+        ffd_output = self._positionwise_feed_forward(pre_process2_output)
+
+        return self._postprocess_layer2(ffd_output, attn_output)
+
+
+class EncoderLayer(Layer):
+    """
+    encoder
+    """
+
+    def __init__(self,
+                 hidden_act,
+                 n_layer,
+                 n_head,
+                 d_key,
+                 d_value,
+                 d_model,
+                 d_inner_hid,
+                 prepostprocess_dropout,
+                 attention_dropout,
+                 relu_dropout,
+                 preprocess_cmd="n",
+                 postprocess_cmd="da",
+                 param_initializer=None,
+                 name=""):
+
+        super(EncoderLayer, self).__init__()
+        self._preprocess_cmd = preprocess_cmd
+        self._encoder_sublayers = list()
+        self._prepostprocess_dropout = prepostprocess_dropout
+        self._n_layer = n_layer
+        self._hidden_act = hidden_act
+        self._preprocess_layer = PrePostProcessLayer(
+            self._preprocess_cmd, 3, self._prepostprocess_dropout,
+            "post_encoder")
+
+        for i in range(n_layer):
+            self._encoder_sublayers.append(
+                self.add_sublayer(
+                    'esl_%d' % i,
+                    EncoderSubLayer(
+                        hidden_act,
+                        n_head,
+                        d_key,
+                        d_value,
+                        d_model,
+                        d_inner_hid,
+                        prepostprocess_dropout,
+                        attention_dropout,
+                        relu_dropout,
+                        preprocess_cmd,
+                        postprocess_cmd,
+                        param_initializer,
+                        name=name + '_layer_' + str(i))))
+
+    def forward(self, enc_input, attn_bias):
+        """
+        forward
+        :param enc_input:
+        :param attn_bias:
+        :return:
+        """
+        outputs = []
+        for i in range(self._n_layer):
+            enc_output = self._encoder_sublayers[i](enc_input, attn_bias)
+            outputs.append(enc_output)
+            enc_input = enc_output
+
+        return outputs

paddleslim/teachers/bert/optimization.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.
+"""Optimization and learning rate scheduling."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid as fluid
+
+from paddle.fluid.dygraph.learning_rate_scheduler import LearningRateDecay
+
+
+class ConstantLR(LearningRateDecay):
+    def __init__(self, learning_rate, begin=0, step=1, dtype='float32'):
+        super(ConstantLR, self).__init__(begin, step, dtype)
+        self.learning_rate = learning_rate
+
+    def step(self):
+        return self.learning_rate
+
+
+class LinearDecay(LearningRateDecay):
+    def __init__(self,
+                 learning_rate,
+                 warmup_steps,
+                 decay_steps,
+                 end_learning_rate=0.0001,
+                 power=1.0,
+                 cycle=False,
+                 begin=0,
+                 step=1,
+                 dtype='float32'):
+        super(LinearDecay, self).__init__(begin, step, dtype)
+        self.learning_rate = learning_rate
+        self.warmup_steps = warmup_steps
+        self.decay_steps = decay_steps
+        self.end_learning_rate = end_learning_rate
+        self.power = power
+        self.cycle = cycle
+
+    def step(self):
+        if self.step_num < self.warmup_steps:
+            decayed_lr = self.learning_rate * (self.step_num /
+                                               self.warmup_steps)
+            decayed_lr = self.create_lr_var(decayed_lr)
+        else:
+            tmp_step_num = self.step_num
+            tmp_decay_steps = self.decay_steps
+            if self.cycle:
+                div_res = fluid.layers.ceil(
+                    self.create_lr_var(tmp_step_num / float(self.decay_steps)))
+                if tmp_step_num == 0:
+                    div_res = self.create_lr_var(1.0)
+                tmp_decay_steps = self.decay_steps * div_res
+            else:
+                tmp_step_num = self.create_lr_var(
+                    tmp_step_num
+                    if tmp_step_num < self.decay_steps else self.decay_steps)
+                decayed_lr = (self.learning_rate - self.end_learning_rate) * \
+                    ((1 - tmp_step_num / tmp_decay_steps) ** self.power) + self.end_learning_rate
+
+        return decayed_lr
+
+
+class Optimizer(object):
+    def __init__(self,
+                 warmup_steps,
+                 num_train_steps,
+                 learning_rate,
+                 model_cls,
+                 weight_decay,
+                 scheduler='linear_warmup_decay',
+                 loss_scaling=1.0,
+                 parameter_list=None):
+        self.warmup_steps = warmup_steps
+        self.num_train_steps = num_train_steps
+        self.learning_rate = learning_rate
+        self.model_cls = model_cls
+        self.weight_decay = weight_decay
+        self.scheduler = scheduler
+        self.loss_scaling = loss_scaling
+        self.parameter_list = parameter_list
+
+        self.scheduled_lr = 0.0
+        self.optimizer = self.lr_schedule()
+
+    def lr_schedule(self):
+        if self.warmup_steps > 0:
+            if self.scheduler == 'noam_decay':
+                self.scheduled_lr = fluid.dygraph.NoamDecay(1 / (
+                    self.warmup_steps * (self.learning_rate**2)),
+                                                            self.warmup_steps)
+            elif self.scheduler == 'linear_warmup_decay':
+                self.scheduled_lr = LinearDecay(self.learning_rate,
+                                                self.warmup_steps,
+                                                self.num_train_steps, 0.0)
+            else:
+                raise ValueError("Unkown learning rate scheduler, should be "
+                                 "'noam_decay' or 'linear_warmup_decay'")
+            optimizer = fluid.optimizer.Adam(
+                learning_rate=self.scheduled_lr,
+                parameter_list=self.parameter_list)
+        else:
+            self.scheduled_lr = ConstantLR(self.learning_rate)
+            optimizer = fluid.optimizer.Adam(
+                learning_rate=self.scheduled_lr,
+                parameter_list=self.parameter_list)
+
+        return optimizer
+
+    def exclude_from_weight_decay(self, name):
+        if name.find("layer_norm") > -1:
+            return True
+        bias_suffix = ["_bias", "_b", ".b_0"]
+        for suffix in bias_suffix:
+            if name.endswith(suffix):
+                return True
+        return False
+
+    def optimization(self, loss, use_data_parallel=False, model=None):
+        param_list = dict()
+
+        clip_norm_thres = 1.0
+        #grad_clip = fluid.clip.GradientClipByGlobalNorm(clip_norm_thres)
+
+        if use_data_parallel:
+            loss = model.scale_loss(loss)
+
+        loss.backward()
+
+        if self.weight_decay > 0:
+            for param in self.model_cls.parameters():
+                param_list[param.name] = param * 1.0
+                param_list[param.name].stop_gradient = True
+
+        if use_data_parallel:
+            assert model is not None
+            model.apply_collective_grads()
+
+        #_, param_grads = self.optimizer.minimize(loss, grad_clip=grad_clip)
+        _, param_grads = self.optimizer.minimize(loss)
+
+        if self.weight_decay > 0:
+            for param, grad in param_grads:
+                if self.exclude_from_weight_decay(param.name):
+                    continue
+                if isinstance(self.scheduled_lr.step(), float):
+                    updated_param = param.numpy() - param_list[
+                        param.name].numpy(
+                        ) * self.weight_decay * self.scheduled_lr.step()
+                else:
+                    updated_param = param.numpy(
+                    ) - param_list[param.name].numpy(
+                    ) * self.weight_decay * self.scheduled_lr.step().numpy()
+                updated_param_var = fluid.dygraph.to_variable(updated_param)
+                param = updated_param_var
+                #param = fluid.layers.reshape(x=updated_param_var, shape=list(updated_param_var.shape))

paddleslim/teachers/bert/reader/cls.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.
+
+import io
+import os
+import types
+import csv
+import numpy as np
+import tokenization
+from batching import prepare_batch_data
+
+
+class DataProcessor(object):
+    """Base class for data converters for sequence classification data sets."""
+
+    def __init__(self,
+                 data_dir,
+                 vocab_path,
+                 max_seq_len,
+                 do_lower_case,
+                 in_tokens,
+                 random_seed=None):
+        self.data_dir = data_dir
+        self.max_seq_len = max_seq_len
+        self.tokenizer = tokenization.FullTokenizer(
+            vocab_file=vocab_path, do_lower_case=do_lower_case)
+        self.vocab = self.tokenizer.vocab
+        self.in_tokens = in_tokens
+
+        np.random.seed(random_seed)
+
+        self.current_train_example = -1
+        self.num_examples = {'train': -1, 'dev': -1, 'test': -1}
+        self.current_train_epoch = -1
+
+    def get_train_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the train set."""
+        raise NotImplementedError()
+
+    def get_dev_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for the dev set."""
+        raise NotImplementedError()
+
+    def get_test_examples(self, data_dir):
+        """Gets a collection of `InputExample`s for prediction."""
+        raise NotImplementedError()
+
+    def get_labels(self):
+        """Gets the list of labels for this data set."""
+        raise NotImplementedError()
+
+    def convert_example(self, index, example, labels, max_seq_len, tokenizer):
+        """Converts a single `InputExample` into a single `InputFeatures`."""
+        feature = convert_single_example(index, example, labels, max_seq_len,
+                                         tokenizer)
+        return feature
+
+    def generate_instance(self, feature):
+        """
+        generate instance with given feature
+
+        Args:
+            feature: InputFeatures(object). A single set of features of data.
+        """
+        input_pos = list(range(len(feature.input_ids)))
+        return [
+            feature.input_ids, feature.segment_ids, input_pos, feature.label_id
+        ]
+
+    def generate_batch_data(self,
+                            batch_data,
+                            total_token_num,
+                            voc_size=-1,
+                            mask_id=-1,
+                            return_input_mask=True,
+                            return_max_len=False,
+                            return_num_token=False):
+        return prepare_batch_data(
+            batch_data,
+            total_token_num,
+            voc_size=-1,
+            pad_id=self.vocab["[PAD]"],
+            cls_id=self.vocab["[CLS]"],
+            sep_id=self.vocab["[SEP]"],
+            mask_id=-1,
+            return_input_mask=True,
+            return_max_len=False,
+            return_num_token=False)
+
+    @classmethod
+    def _read_tsv(cls, input_file, quotechar=None):
+        """Reads a tab separated value file."""
+        with io.open(input_file, "r", encoding="utf8") as f:
+            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
+            lines = []
+            for line in reader:
+                lines.append(line)
+            return lines
+
+    def get_num_examples(self, phase):
+        """Get number of examples for train, dev or test."""
+        if phase not in ['train', 'dev', 'test']:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'dev', 'test'].")
+        return self.num_examples[phase]
+
+    def get_train_progress(self):
+        """Gets progress for training phase."""
+        return self.current_train_example, self.current_train_epoch
+
+    def data_generator(self,
+                       batch_size,
+                       phase='train',
+                       epoch=1,
+                       dev_count=1,
+                       shuffle=True,
+                       shuffle_seed=None):
+        """
+        Generate data for train, dev or test.
+    
+        Args:
+          batch_size: int. The batch size of generated data.
+          phase: string. The phase for which to generate data.
+          epoch: int. Total epoches to generate data.
+          shuffle: bool. Whether to shuffle examples.
+        """
+        if phase == 'train':
+            examples = self.get_train_examples(self.data_dir)
+            self.num_examples['train'] = len(examples)
+        elif phase == 'dev':
+            examples = self.get_dev_examples(self.data_dir)
+            self.num_examples['dev'] = len(examples)
+        elif phase == 'test':
+            examples = self.get_test_examples(self.data_dir)
+            self.num_examples['test'] = len(examples)
+        else:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'dev', 'test'].")
+
+        def instance_reader():
+            for epoch_index in range(epoch):
+                if shuffle:
+                    if shuffle_seed is not None:
+                        np.random.seed(shuffle_seed)
+                    np.random.shuffle(examples)
+                if phase == 'train':
+                    self.current_train_epoch = epoch_index
+                for (index, example) in enumerate(examples):
+                    if phase == 'train':
+                        self.current_train_example = index + 1
+                    feature = self.convert_example(
+                        index, example,
+                        self.get_labels(), self.max_seq_len, self.tokenizer)
+
+                    instance = self.generate_instance(feature)
+                    yield instance
+
+        def batch_reader(reader, batch_size, in_tokens):
+            batch, total_token_num, max_len = [], 0, 0
+            for instance in reader():
+                token_ids, sent_ids, pos_ids, label = instance[:4]
+                max_len = max(max_len, len(token_ids))
+                if in_tokens:
+                    to_append = (len(batch) + 1) * max_len <= batch_size
+                else:
+                    to_append = len(batch) < batch_size
+                if to_append:
+                    batch.append(instance)
+                    total_token_num += len(token_ids)
+                else:
+                    yield batch, total_token_num
+                    batch, total_token_num, max_len = [instance], len(
+                        token_ids), len(token_ids)
+
+            if len(batch) > 0:
+                yield batch, total_token_num
+
+        def wrapper():
+            all_dev_batches = []
+            for batch_data, total_token_num in batch_reader(
+                    instance_reader, batch_size, self.in_tokens):
+                batch_data = self.generate_batch_data(
+                    batch_data,
+                    total_token_num,
+                    voc_size=-1,
+                    mask_id=-1,
+                    return_input_mask=True,
+                    return_max_len=False,
+                    return_num_token=False)
+                if len(all_dev_batches) < dev_count:
+                    all_dev_batches.append(batch_data)
+
+                if len(all_dev_batches) == dev_count:
+                    for batch in all_dev_batches:
+                        yield batch
+                    all_dev_batches = []
+
+        return wrapper
+
+
+class InputExample(object):
+    """A single training/test example for simple sequence classification."""
+
+    def __init__(self, guid, text_a, text_b=None, label=None):
+        """Constructs a InputExample.
+
+    Args:
+      guid: Unique id for the example.
+      text_a: string. The untokenized text of the first sequence. For single
+        sequence tasks, only this sequence must be specified.
+      text_b: (Optional) string. The untokenized text of the second sequence.
+        Only must be specified for sequence pair tasks.
+      label: (Optional) string. The label of the example. This should be
+        specified for train and dev examples, but not for test examples.
+    """
+        self.guid = guid
+        self.text_a = text_a
+        self.text_b = text_b
+        self.label = label
+
+
+def _truncate_seq_pair(tokens_a, tokens_b, max_length):
+    """Truncates a sequence pair in place to the maximum length."""
+
+    # This is a simple heuristic which will always truncate the longer sequence
+    # one token at a time. This makes more sense than truncating an equal percent
+    # of tokens from each, since if one sequence is very short then each token
+    # that's truncated likely contains more information than a longer sequence.
+    while True:
+        total_length = len(tokens_a) + len(tokens_b)
+        if total_length <= max_length:
+            break
+        if len(tokens_a) > len(tokens_b):
+            tokens_a.pop()
+        else:
+            tokens_b.pop()
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+
+    def __init__(self, input_ids, input_mask, segment_ids, label_id):
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.label_id = label_id
+
+
+class XnliProcessor(DataProcessor):
+    """Processor for the XNLI data set."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        self.language = "zh"
+        lines = self._read_tsv(
+            os.path.join(data_dir, "multinli", "multinli.train.%s.tsv" %
+                         self.language))
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "train-%d" % (i)
+            text_a = tokenization.convert_to_unicode(line[0])
+            text_b = tokenization.convert_to_unicode(line[1])
+            label = tokenization.convert_to_unicode(line[2])
+            if label == tokenization.convert_to_unicode("contradictory"):
+                label = tokenization.convert_to_unicode("contradiction")
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        self.language = "zh"
+        lines = self._read_tsv(os.path.join(data_dir, "xnli.dev.tsv"))
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "dev-%d" % (i)
+            language = tokenization.convert_to_unicode(line[0])
+            if language != tokenization.convert_to_unicode(self.language):
+                continue
+            text_a = tokenization.convert_to_unicode(line[6])
+            text_b = tokenization.convert_to_unicode(line[7])
+            label = tokenization.convert_to_unicode(line[1])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        self.language = "zh"
+        lines = self._read_tsv(os.path.join(data_dir, "xnli.test.tsv"))
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "test-%d" % (i)
+            language = tokenization.convert_to_unicode(line[0])
+            if language != tokenization.convert_to_unicode(self.language):
+                continue
+            text_a = tokenization.convert_to_unicode(line[6])
+            text_b = tokenization.convert_to_unicode(line[7])
+            label = tokenization.convert_to_unicode(line[1])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+    def get_labels(self):
+        """See base class."""
+        return ["contradiction", "entailment", "neutral"]
+
+
+class MnliProcessor(DataProcessor):
+    """Processor for the MultiNLI data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev_matched.tsv")),
+            "dev_matched")
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test_matched.tsv")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["contradiction", "entailment", "neutral"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type,
+                              tokenization.convert_to_unicode(line[0]))
+            text_a = tokenization.convert_to_unicode(line[8])
+            text_b = tokenization.convert_to_unicode(line[9])
+            if set_type == "test":
+                label = "contradiction"
+            else:
+                label = tokenization.convert_to_unicode(line[-1])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class MrpcProcessor(DataProcessor):
+    """Processor for the MRPC data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            if i == 0:
+                continue
+            guid = "%s-%s" % (set_type, i)
+            text_a = tokenization.convert_to_unicode(line[3])
+            text_b = tokenization.convert_to_unicode(line[4])
+            if set_type == "test":
+                label = "0"
+            else:
+                label = tokenization.convert_to_unicode(line[0])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=text_b, label=label))
+        return examples
+
+
+class ColaProcessor(DataProcessor):
+    """Processor for the CoLA data set (GLUE version)."""
+
+    def get_train_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")
+
+    def get_dev_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev")
+
+    def get_test_examples(self, data_dir):
+        """See base class."""
+        return self._create_examples(
+            self._read_tsv(os.path.join(data_dir, "test.tsv")), "test")
+
+    def get_labels(self):
+        """See base class."""
+        return ["0", "1"]
+
+    def _create_examples(self, lines, set_type):
+        """Creates examples for the training and dev sets."""
+        examples = []
+        for (i, line) in enumerate(lines):
+            # Only the test set has a header
+            if set_type == "test" and i == 0:
+                continue
+            guid = "%s-%s" % (set_type, i)
+            if set_type == "test":
+                text_a = tokenization.convert_to_unicode(line[1])
+                label = "0"
+            else:
+                text_a = tokenization.convert_to_unicode(line[3])
+                label = tokenization.convert_to_unicode(line[1])
+            examples.append(
+                InputExample(
+                    guid=guid, text_a=text_a, text_b=None, label=label))
+        return examples
+
+
+def convert_single_example_to_unicode(guid, single_example):
+    text_a = tokenization.convert_to_unicode(single_example[0])
+    text_b = tokenization.convert_to_unicode(single_example[1])
+    label = tokenization.convert_to_unicode(single_example[2])
+    return InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label)
+
+
+def convert_single_example(ex_index, example, label_list, max_seq_length,
+                           tokenizer):
+    """Converts a single `InputExample` into a single `InputFeatures`."""
+    label_map = {}
+    for (i, label) in enumerate(label_list):
+        label_map[label] = i
+
+    tokens_a = tokenizer.tokenize(example.text_a)
+    tokens_b = None
+    if example.text_b:
+        tokens_b = tokenizer.tokenize(example.text_b)
+
+    if tokens_b:
+        # Modifies `tokens_a` and `tokens_b` in place so that the total
+        # length is less than the specified length.
+        # Account for [CLS], [SEP], [SEP] with "- 3"
+        _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
+    else:
+        # Account for [CLS] and [SEP] with "- 2"
+        if len(tokens_a) > max_seq_length - 2:
+            tokens_a = tokens_a[0:(max_seq_length - 2)]
+
+    # The convention in BERT is:
+    # (a) For sequence pairs:
+    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
+    #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
+    # (b) For single sequences:
+    #  tokens:   [CLS] the dog is hairy . [SEP]
+    #  type_ids: 0     0   0   0  0     0 0
+    #
+    # Where "type_ids" are used to indicate whether this is the first
+    # sequence or the second sequence. The embedding vectors for `type=0` and
+    # `type=1` were learned during pre-training and are added to the wordpiece
+    # embedding vector (and position vector). This is not *strictly* necessary
+    # since the [SEP] token unambiguously separates the sequences, but it makes
+    # it easier for the model to learn the concept of sequences.
+    #
+    # For classification tasks, the first vector (corresponding to [CLS]) is
+    # used as as the "sentence vector". Note that this only makes sense because
+    # the entire model is fine-tuned.
+    tokens = []
+    segment_ids = []
+    tokens.append("[CLS]")
+    segment_ids.append(0)
+    for token in tokens_a:
+        tokens.append(token)
+        segment_ids.append(0)
+    tokens.append("[SEP]")
+    segment_ids.append(0)
+
+    if tokens_b:
+        for token in tokens_b:
+            tokens.append(token)
+            segment_ids.append(1)
+        tokens.append("[SEP]")
+        segment_ids.append(1)
+
+    input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+    # The mask has 1 for real tokens and 0 for padding tokens. Only real
+    # tokens are attended to.
+    input_mask = [1] * len(input_ids)
+
+    label_id = label_map[example.label]
+
+    feature = InputFeatures(
+        input_ids=input_ids,
+        input_mask=input_mask,
+        segment_ids=segment_ids,
+        label_id=label_id)
+    return feature
+
+
+def convert_examples_to_features(examples, label_list, max_seq_length,
+                                 tokenizer):
+    """Convert a set of `InputExample`s to a list of `InputFeatures`."""
+
+    features = []
+    for (ex_index, example) in enumerate(examples):
+        if ex_index % 10000 == 0:
+            print("Writing example %d of %d" % (ex_index, len(examples)))
+
+        feature = convert_single_example(ex_index, example, label_list,
+                                         max_seq_length, tokenizer)
+
+        features.append(feature)
+    return features
+
+
+if __name__ == '__main__':
+    pass

paddleslim/teachers/bert/reader/pretraining.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.
+
+from __future__ import print_function
+from __future__ import division
+
+import os
+import numpy as np
+import types
+import gzip
+import logging
+import re
+import six
+import collections
+import tokenization
+
+import paddle
+import paddle.fluid as fluid
+
+from batching import prepare_batch_data
+
+
+class DataReader(object):
+    def __init__(self,
+                 data_dir,
+                 vocab_path,
+                 batch_size=4096,
+                 in_tokens=True,
+                 max_seq_len=512,
+                 shuffle_files=True,
+                 epoch=100,
+                 voc_size=0,
+                 is_test=False,
+                 generate_neg_sample=False):
+
+        self.vocab = self.load_vocab(vocab_path)
+        self.data_dir = data_dir
+        self.batch_size = batch_size
+        self.in_tokens = in_tokens
+        self.shuffle_files = shuffle_files
+        self.epoch = epoch
+        self.current_epoch = 0
+        self.current_file_index = 0
+        self.total_file = 0
+        self.current_file = None
+        self.voc_size = voc_size
+        self.max_seq_len = max_seq_len
+        self.pad_id = self.vocab["[PAD]"]
+        self.cls_id = self.vocab["[CLS]"]
+        self.sep_id = self.vocab["[SEP]"]
+        self.mask_id = self.vocab["[MASK]"]
+        self.is_test = is_test
+        self.generate_neg_sample = generate_neg_sample
+        if self.in_tokens:
+            assert self.batch_size >= self.max_seq_len, "The number of " \
+                   "tokens in batch should not be smaller than max seq length."
+
+        if self.is_test:
+            self.epoch = 1
+            self.shuffle_files = False
+
+    def get_progress(self):
+        """return current progress of traning data
+        """
+        return self.current_epoch, self.current_file_index, self.total_file, self.current_file
+
+    def parse_line(self, line, max_seq_len=512):
+        """ parse one line to token_ids, sentence_ids, pos_ids, label
+        """
+        line = line.strip().decode().split(";")
+        assert len(line) == 4, "One sample must have 4 fields!"
+        (token_ids, sent_ids, pos_ids, label) = line
+        token_ids = [int(token) for token in token_ids.split(" ")]
+        sent_ids = [int(token) for token in sent_ids.split(" ")]
+        pos_ids = [int(token) for token in pos_ids.split(" ")]
+        assert len(token_ids) == len(sent_ids) == len(
+            pos_ids
+        ), "[Must be true]len(token_ids) == len(sent_ids) == len(pos_ids)"
+        label = int(label)
+        if len(token_ids) > max_seq_len:
+            return None
+        return [token_ids, sent_ids, pos_ids, label]
+
+    def read_file(self, file):
+        assert file.endswith('.gz'), "[ERROR] %s is not a gzip file" % file
+        file_path = self.data_dir + "/" + file
+        with gzip.open(file_path, "rb") as f:
+            for line in f:
+                parsed_line = self.parse_line(
+                    line, max_seq_len=self.max_seq_len)
+                if parsed_line is None:
+                    continue
+                yield parsed_line
+
+    def convert_to_unicode(self, text):
+        """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
+        if six.PY3:
+            if isinstance(text, str):
+                return text
+            elif isinstance(text, bytes):
+                return text.decode("utf-8", "ignore")
+            else:
+                raise ValueError("Unsupported string type: %s" % (type(text)))
+        elif six.PY2:
+            if isinstance(text, str):
+                return text.decode("utf-8", "ignore")
+            elif isinstance(text, unicode):
+                return text
+            else:
+                raise ValueError("Unsupported string type: %s" % (type(text)))
+        else:
+            raise ValueError("Not running on Python2 or Python 3?")
+
+    def load_vocab(self, vocab_file):
+        """Loads a vocabulary file into a dictionary."""
+        vocab = collections.OrderedDict()
+        fin = open(vocab_file)
+        for num, line in enumerate(fin):
+            items = self.convert_to_unicode(line.strip()).split("\t")
+            if len(items) > 2:
+                break
+            token = items[0]
+            index = items[1] if len(items) == 2 else num
+            token = token.strip()
+            vocab[token] = int(index)
+        return vocab
+
+    def random_pair_neg_samples(self, pos_samples):
+        """ randomly generate negtive samples using pos_samples
+
+            Args:
+                pos_samples: list of positive samples
+            
+            Returns:
+                neg_samples: list of negtive samples
+        """
+        np.random.shuffle(pos_samples)
+        num_sample = len(pos_samples)
+        neg_samples = []
+        miss_num = 0
+
+        for i in range(num_sample):
+            pair_index = (i + 1) % num_sample
+            origin_src_ids = pos_samples[i][0]
+            origin_sep_index = origin_src_ids.index(2)
+            pair_src_ids = pos_samples[pair_index][0]
+            pair_sep_index = pair_src_ids.index(2)
+
+            src_ids = origin_src_ids[:origin_sep_index + 1] + pair_src_ids[
+                pair_sep_index + 1:]
+            if len(src_ids) >= self.max_seq_len:
+                miss_num += 1
+                continue
+            sent_ids = [0] * len(origin_src_ids[:origin_sep_index + 1]) + [
+                1
+            ] * len(pair_src_ids[pair_sep_index + 1:])
+            pos_ids = list(range(len(src_ids)))
+            neg_sample = [src_ids, sent_ids, pos_ids, 0]
+            assert len(src_ids) == len(sent_ids) == len(
+                pos_ids
+            ), "[ERROR]len(src_id) == lne(sent_id) == len(pos_id) must be True"
+            neg_samples.append(neg_sample)
+        return neg_samples, miss_num
+
+    def mixin_negtive_samples(self, pos_sample_generator, buffer=1000):
+        """ 1. generate negtive samples by randomly group sentence_1 and sentence_2 of positive samples
+            2. combine negtive samples and positive samples
+            
+            Args:
+                pos_sample_generator: a generator producing a parsed positive sample, which is a list: [token_ids, sent_ids, pos_ids, 1]
+
+            Returns:
+                sample: one sample from shuffled positive samples and negtive samples
+        """
+        pos_samples = []
+        num_total_miss = 0
+        pos_sample_num = 0
+        try:
+            while True:
+                while len(pos_samples) < buffer:
+                    pos_sample = next(pos_sample_generator)
+                    label = pos_sample[3]
+                    assert label == 1, "positive sample's label must be 1"
+                    pos_samples.append(pos_sample)
+                    pos_sample_num += 1
+
+                neg_samples, miss_num = self.random_pair_neg_samples(
+                    pos_samples)
+                num_total_miss += miss_num
+                samples = pos_samples + neg_samples
+                pos_samples = []
+                np.random.shuffle(samples)
+                for sample in samples:
+                    yield sample
+        except StopIteration:
+            print("stopiteration: reach end of file")
+            if len(pos_samples) == 1:
+                yield pos_samples[0]
+            elif len(pos_samples) == 0:
+                yield None
+            else:
+                neg_samples, miss_num = self.random_pair_neg_samples(
+                    pos_samples)
+                num_total_miss += miss_num
+                samples = pos_samples + neg_samples
+                pos_samples = []
+                np.random.shuffle(samples)
+                for sample in samples:
+                    yield sample
+            print("miss_num:%d\tideal_total_sample_num:%d\tmiss_rate:%f" %
+                  (num_total_miss, pos_sample_num * 2,
+                   num_total_miss / (pos_sample_num * 2)))
+
+    def data_generator(self):
+        """
+        data_generator
+        """
+        files = os.listdir(self.data_dir)
+        self.total_file = len(files)
+        assert self.total_file > 0, "[Error] data_dir is empty"
+
+        def wrapper():
+            def reader():
+                for epoch in range(self.epoch):
+                    self.current_epoch = epoch + 1
+                    if self.shuffle_files:
+                        np.random.shuffle(files)
+                    for index, file in enumerate(files):
+                        self.current_file_index = index + 1
+                        self.current_file = file
+                        sample_generator = self.read_file(file)
+                        if not self.is_test and self.generate_neg_sample:
+                            sample_generator = self.mixin_negtive_samples(
+                                sample_generator)
+                        for sample in sample_generator:
+                            if sample is None:
+                                continue
+                            yield sample
+
+            def batch_reader(reader, batch_size, in_tokens):
+                batch, total_token_num, max_len = [], 0, 0
+                for parsed_line in reader():
+                    token_ids, sent_ids, pos_ids, label = parsed_line
+                    max_len = max(max_len, len(token_ids))
+                    if in_tokens:
+                        to_append = (len(batch) + 1) * max_len <= batch_size
+                    else:
+                        to_append = len(batch) < batch_size
+                    if to_append:
+                        batch.append(parsed_line)
+                        total_token_num += len(token_ids)
+                    else:
+                        yield batch, total_token_num
+                        batch, total_token_num, max_len = [parsed_line], len(
+                            token_ids), len(token_ids)
+
+                if len(batch) > 0:
+                    yield batch, total_token_num
+
+            for batch_data, total_token_num in batch_reader(
+                    reader, self.batch_size, self.in_tokens):
+                yield prepare_batch_data(
+                    batch_data,
+                    total_token_num,
+                    voc_size=self.voc_size,
+                    pad_id=self.pad_id,
+                    cls_id=self.cls_id,
+                    sep_id=self.sep_id,
+                    mask_id=self.mask_id,
+                    return_input_mask=True,
+                    return_max_len=False,
+                    return_num_token=False)
+
+        return wrapper
+
+
+if __name__ == "__main__":
+    pass

paddleslim/teachers/bert/reader/squad.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# 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.
+"""Run BERT on SQuAD 1.1 and SQuAD 2.0."""
+
+import six
+import math
+import json
+import random
+import collections
+import tokenization
+from batching import prepare_batch_data
+
+
+class SquadExample(object):
+    """A single training/test example for simple sequence classification.
+
+     For examples without an answer, the start and end position are -1.
+  """
+
+    def __init__(self,
+                 qas_id,
+                 question_text,
+                 doc_tokens,
+                 orig_answer_text=None,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=False):
+        self.qas_id = qas_id
+        self.question_text = question_text
+        self.doc_tokens = doc_tokens
+        self.orig_answer_text = orig_answer_text
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+    def __str__(self):
+        return self.__repr__()
+
+    def __repr__(self):
+        s = ""
+        s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
+        s += ", question_text: %s" % (
+            tokenization.printable_text(self.question_text))
+        s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
+        if self.start_position:
+            s += ", start_position: %d" % (self.start_position)
+        if self.start_position:
+            s += ", end_position: %d" % (self.end_position)
+        if self.start_position:
+            s += ", is_impossible: %r" % (self.is_impossible)
+        return s
+
+
+class InputFeatures(object):
+    """A single set of features of data."""
+
+    def __init__(self,
+                 unique_id,
+                 example_index,
+                 doc_span_index,
+                 tokens,
+                 token_to_orig_map,
+                 token_is_max_context,
+                 input_ids,
+                 input_mask,
+                 segment_ids,
+                 start_position=None,
+                 end_position=None,
+                 is_impossible=None):
+        self.unique_id = unique_id
+        self.example_index = example_index
+        self.doc_span_index = doc_span_index
+        self.tokens = tokens
+        self.token_to_orig_map = token_to_orig_map
+        self.token_is_max_context = token_is_max_context
+        self.input_ids = input_ids
+        self.input_mask = input_mask
+        self.segment_ids = segment_ids
+        self.start_position = start_position
+        self.end_position = end_position
+        self.is_impossible = is_impossible
+
+
+def read_squad_examples(input_file, is_training,
+                        version_2_with_negative=False):
+    """Read a SQuAD json file into a list of SquadExample."""
+    with open(input_file, "r") as reader:
+        input_data = json.load(reader)["data"]
+
+    def is_whitespace(c):
+        if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
+            return True
+        return False
+
+    examples = []
+    for entry in input_data:
+        for paragraph in entry["paragraphs"]:
+            paragraph_text = paragraph["context"]
+            doc_tokens = []
+            char_to_word_offset = []
+            prev_is_whitespace = True
+            for c in paragraph_text:
+                if is_whitespace(c):
+                    prev_is_whitespace = True
+                else:
+                    if prev_is_whitespace:
+                        doc_tokens.append(c)
+                    else:
+                        doc_tokens[-1] += c
+                    prev_is_whitespace = False
+                char_to_word_offset.append(len(doc_tokens) - 1)
+
+            for qa in paragraph["qas"]:
+                qas_id = qa["id"]
+                question_text = qa["question"]
+                start_position = None
+                end_position = None
+                orig_answer_text = None
+                is_impossible = False
+                if is_training:
+
+                    if version_2_with_negative:
+                        is_impossible = qa["is_impossible"]
+                    if (len(qa["answers"]) != 1) and (not is_impossible):
+                        raise ValueError(
+                            "For training, each question should have exactly 1 answer."
+                        )
+                    if not is_impossible:
+                        answer = qa["answers"][0]
+                        orig_answer_text = answer["text"]
+                        answer_offset = answer["answer_start"]
+                        answer_length = len(orig_answer_text)
+                        start_position = char_to_word_offset[answer_offset]
+                        end_position = char_to_word_offset[answer_offset +
+                                                           answer_length - 1]
+                        # Only add answers where the text can be exactly recovered from the
+                        # document. If this CAN'T happen it's likely due to weird Unicode
+                        # stuff so we will just skip the example.
+                        #
+                        # Note that this means for training mode, every example is NOT
+                        # guaranteed to be preserved.
+                        actual_text = " ".join(doc_tokens[start_position:(
+                            end_position + 1)])
+                        cleaned_answer_text = " ".join(
+                            tokenization.whitespace_tokenize(orig_answer_text))
+                        if actual_text.find(cleaned_answer_text) == -1:
+                            print("Could not find answer: '%s' vs. '%s'",
+                                  actual_text, cleaned_answer_text)
+                            continue
+                    else:
+                        start_position = -1
+                        end_position = -1
+                        orig_answer_text = ""
+
+                example = SquadExample(
+                    qas_id=qas_id,
+                    question_text=question_text,
+                    doc_tokens=doc_tokens,
+                    orig_answer_text=orig_answer_text,
+                    start_position=start_position,
+                    end_position=end_position,
+                    is_impossible=is_impossible)
+                examples.append(example)
+
+    return examples
+
+
+def convert_examples_to_features(
+        examples,
+        tokenizer,
+        max_seq_length,
+        doc_stride,
+        max_query_length,
+        is_training,
+        #output_fn
+):
+    """Loads a data file into a list of `InputBatch`s."""
+
+    unique_id = 1000000000
+
+    for (example_index, example) in enumerate(examples):
+        query_tokens = tokenizer.tokenize(example.question_text)
+
+        if len(query_tokens) > max_query_length:
+            query_tokens = query_tokens[0:max_query_length]
+
+        tok_to_orig_index = []
+        orig_to_tok_index = []
+        all_doc_tokens = []
+        for (i, token) in enumerate(example.doc_tokens):
+            orig_to_tok_index.append(len(all_doc_tokens))
+            sub_tokens = tokenizer.tokenize(token)
+            for sub_token in sub_tokens:
+                tok_to_orig_index.append(i)
+                all_doc_tokens.append(sub_token)
+
+        tok_start_position = None
+        tok_end_position = None
+        if is_training and example.is_impossible:
+            tok_start_position = -1
+            tok_end_position = -1
+        if is_training and not example.is_impossible:
+            tok_start_position = orig_to_tok_index[example.start_position]
+            if example.end_position < len(example.doc_tokens) - 1:
+                tok_end_position = orig_to_tok_index[example.end_position +
+                                                     1] - 1
+            else:
+                tok_end_position = len(all_doc_tokens) - 1
+            (tok_start_position, tok_end_position) = _improve_answer_span(
+                all_doc_tokens, tok_start_position, tok_end_position,
+                tokenizer, example.orig_answer_text)
+
+        # The -3 accounts for [CLS], [SEP] and [SEP]
+        max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
+
+        # We can have documents that are longer than the maximum sequence length.
+        # To deal with this we do a sliding window approach, where we take chunks
+        # of the up to our max length with a stride of `doc_stride`.
+        _DocSpan = collections.namedtuple(  # pylint: disable=invalid-name
+            "DocSpan", ["start", "length"])
+        doc_spans = []
+        start_offset = 0
+        while start_offset < len(all_doc_tokens):
+            length = len(all_doc_tokens) - start_offset
+            if length > max_tokens_for_doc:
+                length = max_tokens_for_doc
+            doc_spans.append(_DocSpan(start=start_offset, length=length))
+            if start_offset + length == len(all_doc_tokens):
+                break
+            start_offset += min(length, doc_stride)
+
+        for (doc_span_index, doc_span) in enumerate(doc_spans):
+            tokens = []
+            token_to_orig_map = {}
+            token_is_max_context = {}
+            segment_ids = []
+            tokens.append("[CLS]")
+            segment_ids.append(0)
+            for token in query_tokens:
+                tokens.append(token)
+                segment_ids.append(0)
+            tokens.append("[SEP]")
+            segment_ids.append(0)
+
+            for i in range(doc_span.length):
+                split_token_index = doc_span.start + i
+                token_to_orig_map[len(tokens)] = tok_to_orig_index[
+                    split_token_index]
+
+                is_max_context = _check_is_max_context(
+                    doc_spans, doc_span_index, split_token_index)
+                token_is_max_context[len(tokens)] = is_max_context
+                tokens.append(all_doc_tokens[split_token_index])
+                segment_ids.append(1)
+            tokens.append("[SEP]")
+            segment_ids.append(1)
+
+            input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+            # The mask has 1 for real tokens and 0 for padding tokens. Only real
+            # tokens are attended to.
+            input_mask = [1] * len(input_ids)
+
+            # Zero-pad up to the sequence length.
+            #while len(input_ids) < max_seq_length:
+            #  input_ids.append(0)
+            #  input_mask.append(0)
+            #  segment_ids.append(0)
+
+            #assert len(input_ids) == max_seq_length
+            #assert len(input_mask) == max_seq_length
+            #assert len(segment_ids) == max_seq_length
+
+            start_position = None
+            end_position = None
+            if is_training and not example.is_impossible:
+                # For training, if our document chunk does not contain an annotation
+                # we throw it out, since there is nothing to predict.
+                doc_start = doc_span.start
+                doc_end = doc_span.start + doc_span.length - 1
+                out_of_span = False
+                if not (tok_start_position >= doc_start and
+                        tok_end_position <= doc_end):
+                    out_of_span = True
+                if out_of_span:
+                    start_position = 0
+                    end_position = 0
+                else:
+                    doc_offset = len(query_tokens) + 2
+                    start_position = tok_start_position - doc_start + doc_offset
+                    end_position = tok_end_position - doc_start + doc_offset
+
+            if is_training and example.is_impossible:
+                start_position = 0
+                end_position = 0
+            """
+            if example_index < 3:
+                print("*** Example ***")
+                print("unique_id: %s" % (unique_id))
+                print("example_index: %s" % (example_index))
+                print("doc_span_index: %s" % (doc_span_index))
+                print("tokens: %s" % " ".join(
+                    [tokenization.printable_text(x) for x in tokens]))
+                print("token_to_orig_map: %s" % " ".join([
+                    "%d:%d" % (x, y)
+                    for (x, y) in six.iteritems(token_to_orig_map)
+                ]))
+                print("token_is_max_context: %s" % " ".join([
+                    "%d:%s" % (x, y)
+                    for (x, y) in six.iteritems(token_is_max_context)
+                ]))
+                print("input_ids: %s" % " ".join([str(x) for x in input_ids]))
+                print("input_mask: %s" % " ".join([str(x) for x in input_mask]))
+                print("segment_ids: %s" %
+                      " ".join([str(x) for x in segment_ids]))
+                if is_training and example.is_impossible:
+                    print("impossible example")
+                if is_training and not example.is_impossible:
+                    answer_text = " ".join(tokens[start_position:(end_position +
+                                                                  1)])
+                    print("start_position: %d" % (start_position))
+                    print("end_position: %d" % (end_position))
+                    print("answer: %s" %
+                          (tokenization.printable_text(answer_text)))
+            """
+
+            feature = InputFeatures(
+                unique_id=unique_id,
+                example_index=example_index,
+                doc_span_index=doc_span_index,
+                tokens=tokens,
+                token_to_orig_map=token_to_orig_map,
+                token_is_max_context=token_is_max_context,
+                input_ids=input_ids,
+                input_mask=input_mask,
+                segment_ids=segment_ids,
+                start_position=start_position,
+                end_position=end_position,
+                is_impossible=example.is_impossible)
+
+            unique_id += 1
+
+            yield feature
+
+
+def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
+                         orig_answer_text):
+    """Returns tokenized answer spans that better match the annotated answer."""
+
+    # The SQuAD annotations are character based. We first project them to
+    # whitespace-tokenized words. But then after WordPiece tokenization, we can
+    # often find a "better match". For example:
+    #
+    #   Question: What year was John Smith born?
+    #   Context: The leader was John Smith (1895-1943).
+    #   Answer: 1895
+    #
+    # The original whitespace-tokenized answer will be "(1895-1943).". However
+    # after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
+    # the exact answer, 1895.
+    #
+    # However, this is not always possible. Consider the following:
+    #
+    #   Question: What country is the top exporter of electornics?
+    #   Context: The Japanese electronics industry is the lagest in the world.
+    #   Answer: Japan
+    #
+    # In this case, the annotator chose "Japan" as a character sub-span of
+    # the word "Japanese". Since our WordPiece tokenizer does not split
+    # "Japanese", we just use "Japanese" as the annotation. This is fairly rare
+    # in SQuAD, but does happen.
+    tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
+
+    for new_start in range(input_start, input_end + 1):
+        for new_end in range(input_end, new_start - 1, -1):
+            text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
+            if text_span == tok_answer_text:
+                return (new_start, new_end)
+
+    return (input_start, input_end)
+
+
+def _check_is_max_context(doc_spans, cur_span_index, position):
+    """Check if this is the 'max context' doc span for the token."""
+
+    # Because of the sliding window approach taken to scoring documents, a single
+    # token can appear in multiple documents. E.g.
+    #  Doc: the man went to the store and bought a gallon of milk
+    #  Span A: the man went to the
+    #  Span B: to the store and bought
+    #  Span C: and bought a gallon of
+    #  ...
+    #
+    # Now the word 'bought' will have two scores from spans B and C. We only
+    # want to consider the score with "maximum context", which we define as
+    # the *minimum* of its left and right context (the *sum* of left and
+    # right context will always be the same, of course).
+    #
+    # In the example the maximum context for 'bought' would be span C since
+    # it has 1 left context and 3 right context, while span B has 4 left context
+    # and 0 right context.
+    best_score = None
+    best_span_index = None
+    for (span_index, doc_span) in enumerate(doc_spans):
+        end = doc_span.start + doc_span.length - 1
+        if position < doc_span.start:
+            continue
+        if position > end:
+            continue
+        num_left_context = position - doc_span.start
+        num_right_context = end - position
+        score = min(num_left_context,
+                    num_right_context) + 0.01 * doc_span.length
+        if best_score is None or score > best_score:
+            best_score = score
+            best_span_index = span_index
+
+    return cur_span_index == best_span_index
+
+
+class DataProcessor(object):
+    def __init__(self, vocab_path, do_lower_case, max_seq_length, in_tokens,
+                 doc_stride, max_query_length):
+        self._tokenizer = tokenization.FullTokenizer(
+            vocab_file=vocab_path, do_lower_case=do_lower_case)
+        self._max_seq_length = max_seq_length
+        self._doc_stride = doc_stride
+        self._max_query_length = max_query_length
+        self._in_tokens = in_tokens
+
+        self.vocab = self._tokenizer.vocab
+        self.vocab_size = len(self.vocab)
+        self.pad_id = self.vocab["[PAD]"]
+        self.cls_id = self.vocab["[CLS]"]
+        self.sep_id = self.vocab["[SEP]"]
+        self.mask_id = self.vocab["[MASK]"]
+
+        self.current_train_example = -1
+        self.num_train_examples = -1
+        self.current_train_epoch = -1
+
+        self.train_examples = None
+        self.predict_examples = None
+        self.num_examples = {'train': -1, 'predict': -1}
+
+    def get_train_progress(self):
+        """Gets progress for training phase."""
+        return self.current_train_example, self.current_train_epoch
+
+    def get_examples(self,
+                     data_path,
+                     is_training,
+                     version_2_with_negative=False):
+        examples = read_squad_examples(
+            input_file=data_path,
+            is_training=is_training,
+            version_2_with_negative=version_2_with_negative)
+        return examples
+
+    def get_num_examples(self, phase):
+        if phase not in ['train', 'predict']:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'predict'].")
+        return self.num_examples[phase]
+
+    def get_features(self, examples, is_training):
+        features = convert_examples_to_features(
+            examples=examples,
+            tokenizer=self._tokenizer,
+            max_seq_length=self._max_seq_length,
+            doc_stride=self._doc_stride,
+            max_query_length=self._max_query_length,
+            is_training=is_training)
+        return features
+
+    def data_generator(self,
+                       data_path,
+                       batch_size,
+                       phase='train',
+                       shuffle=False,
+                       dev_count=1,
+                       version_2_with_negative=False,
+                       epoch=1):
+        if phase == 'train':
+            self.train_examples = self.get_examples(
+                data_path,
+                is_training=True,
+                version_2_with_negative=version_2_with_negative)
+            examples = self.train_examples
+            self.num_examples['train'] = len(self.train_examples)
+        elif phase == 'predict':
+            self.predict_examples = self.get_examples(
+                data_path,
+                is_training=False,
+                version_2_with_negative=version_2_with_negative)
+            examples = self.predict_examples
+            self.num_examples['predict'] = len(self.predict_examples)
+        else:
+            raise ValueError(
+                "Unknown phase, which should be in ['train', 'predict'].")
+
+        def batch_reader(features, batch_size, in_tokens):
+            batch, total_token_num, max_len = [], 0, 0
+            for (index, feature) in enumerate(features):
+                if phase == 'train':
+                    self.current_train_example = index + 1
+                seq_len = len(feature.input_ids)
+                labels = [feature.unique_id
+                          ] if feature.start_position is None else [
+                              feature.start_position, feature.end_position
+                          ]
+                example = [
+                    feature.input_ids, feature.segment_ids, range(seq_len)
+                ] + labels
+                max_len = max(max_len, seq_len)
+
+                #max_len = max(max_len, len(token_ids))
+                if in_tokens:
+                    to_append = (len(batch) + 1) * max_len <= batch_size
+                else:
+                    to_append = len(batch) < batch_size
+
+                if to_append:
+                    batch.append(example)
+                    total_token_num += seq_len
+                else:
+                    yield batch, total_token_num
+                    batch, total_token_num, max_len = [example
+                                                       ], seq_len, seq_len
+            if len(batch) > 0:
+                yield batch, total_token_num
+
+        def wrapper():
+            for epoch_index in range(epoch):
+                if shuffle:
+                    random.shuffle(examples)
+                if phase == 'train':
+                    self.current_train_epoch = epoch_index
+                    features = self.get_features(examples, is_training=True)
+                else:
+                    features = self.get_features(examples, is_training=False)
+
+                all_dev_batches = []
+                for batch_data, total_token_num in batch_reader(
+                        features, batch_size, self._in_tokens):
+                    batch_data = prepare_batch_data(
+                        batch_data,
+                        total_token_num,
+                        voc_size=-1,
+                        pad_id=self.pad_id,
+                        cls_id=self.cls_id,
+                        sep_id=self.sep_id,
+                        mask_id=-1,
+                        return_input_mask=True,
+                        return_max_len=False,
+                        return_num_token=False)
+                    if len(all_dev_batches) < dev_count:
+                        all_dev_batches.append(batch_data)
+
+                    if len(all_dev_batches) == dev_count:
+                        for batch in all_dev_batches:
+                            yield batch
+                        all_dev_batches = []
+
+        return wrapper
+
+
+def write_predictions(all_examples, all_features, all_results, n_best_size,
+                      max_answer_length, do_lower_case, output_prediction_file,
+                      output_nbest_file, output_null_log_odds_file,
+                      version_2_with_negative, null_score_diff_threshold,
+                      verbose):
+    """Write final predictions to the json file and log-odds of null if needed."""
+    print("Writing predictions to: %s" % (output_prediction_file))
+    print("Writing nbest to: %s" % (output_nbest_file))
+
+    example_index_to_features = collections.defaultdict(list)
+    for feature in all_features:
+        example_index_to_features[feature.example_index].append(feature)
+
+    unique_id_to_result = {}
+    for result in all_results:
+        unique_id_to_result[result.unique_id] = result
+
+    _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+        "PrelimPrediction", [
+            "feature_index", "start_index", "end_index", "start_logit",
+            "end_logit"
+        ])
+
+    all_predictions = collections.OrderedDict()
+    all_nbest_json = collections.OrderedDict()
+    scores_diff_json = collections.OrderedDict()
+
+    for (example_index, example) in enumerate(all_examples):
+        features = example_index_to_features[example_index]
+
+        prelim_predictions = []
+        # keep track of the minimum score of null start+end of position 0
+        score_null = 1000000  # large and positive
+        min_null_feature_index = 0  # the paragraph slice with min mull score
+        null_start_logit = 0  # the start logit at the slice with min null score
+        null_end_logit = 0  # the end logit at the slice with min null score
+        for (feature_index, feature) in enumerate(features):
+            result = unique_id_to_result[feature.unique_id]
+            start_indexes = _get_best_indexes(result.start_logits, n_best_size)
+            end_indexes = _get_best_indexes(result.end_logits, n_best_size)
+            # if we could have irrelevant answers, get the min score of irrelevant
+            if version_2_with_negative:
+                feature_null_score = result.start_logits[
+                    0] + result.end_logits[0]
+                if feature_null_score < score_null:
+                    score_null = feature_null_score
+                    min_null_feature_index = feature_index
+                    null_start_logit = result.start_logits[0]
+                    null_end_logit = result.end_logits[0]
+            for start_index in start_indexes:
+                for end_index in end_indexes:
+                    # We could hypothetically create invalid predictions, e.g., predict
+                    # that the start of the span is in the question. We throw out all
+                    # invalid predictions.
+                    if start_index >= len(feature.tokens):
+                        continue
+                    if end_index >= len(feature.tokens):
+                        continue
+                    if start_index not in feature.token_to_orig_map:
+                        continue
+                    if end_index not in feature.token_to_orig_map:
+                        continue
+                    if not feature.token_is_max_context.get(start_index,
+                                                            False):
+                        continue
+                    if end_index < start_index:
+                        continue
+                    length = end_index - start_index + 1
+                    if length > max_answer_length:
+                        continue
+                    prelim_predictions.append(
+                        _PrelimPrediction(
+                            feature_index=feature_index,
+                            start_index=start_index,
+                            end_index=end_index,
+                            start_logit=result.start_logits[start_index],
+                            end_logit=result.end_logits[end_index]))
+
+        if version_2_with_negative:
+            prelim_predictions.append(
+                _PrelimPrediction(
+                    feature_index=min_null_feature_index,
+                    start_index=0,
+                    end_index=0,
+                    start_logit=null_start_logit,
+                    end_logit=null_end_logit))
+        prelim_predictions = sorted(
+            prelim_predictions,
+            key=lambda x: (x.start_logit + x.end_logit),
+            reverse=True)
+
+        _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
+            "NbestPrediction", ["text", "start_logit", "end_logit"])
+
+        seen_predictions = {}
+        nbest = []
+        for pred in prelim_predictions:
+            if len(nbest) >= n_best_size:
+                break
+            feature = features[pred.feature_index]
+            if pred.start_index > 0:  # this is a non-null prediction
+                tok_tokens = feature.tokens[pred.start_index:(pred.end_index +
+                                                              1)]
+                orig_doc_start = feature.token_to_orig_map[pred.start_index]
+                orig_doc_end = feature.token_to_orig_map[pred.end_index]
+                orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end +
+                                                                 1)]
+                tok_text = " ".join(tok_tokens)
+
+                # De-tokenize WordPieces that have been split off.
+                tok_text = tok_text.replace(" ##", "")
+                tok_text = tok_text.replace("##", "")
+
+                # Clean whitespace
+                tok_text = tok_text.strip()
+                tok_text = " ".join(tok_text.split())
+                orig_text = " ".join(orig_tokens)
+
+                final_text = get_final_text(tok_text, orig_text, do_lower_case,
+                                            verbose)
+                if final_text in seen_predictions:
+                    continue
+
+                seen_predictions[final_text] = True
+            else:
+                final_text = ""
+                seen_predictions[final_text] = True
+
+            nbest.append(
+                _NbestPrediction(
+                    text=final_text,
+                    start_logit=pred.start_logit,
+                    end_logit=pred.end_logit))
+
+        # if we didn't inlude the empty option in the n-best, inlcude it
+        if version_2_with_negative:
+            if "" not in seen_predictions:
+                nbest.append(
+                    _NbestPrediction(
+                        text="",
+                        start_logit=null_start_logit,
+                        end_logit=null_end_logit))
+        # In very rare edge cases we could have no valid predictions. So we
+        # just create a nonce prediction in this case to avoid failure.
+        if not nbest:
+            nbest.append(
+                _NbestPrediction(
+                    text="empty", start_logit=0.0, end_logit=0.0))
+
+        assert len(nbest) >= 1
+
+        total_scores = []
+        best_non_null_entry = None
+        for entry in nbest:
+            total_scores.append(entry.start_logit + entry.end_logit)
+            if not best_non_null_entry:
+                if entry.text:
+                    best_non_null_entry = entry
+        # debug
+        if best_non_null_entry is None:
+            print("Emmm..., sth wrong")
+
+        probs = _compute_softmax(total_scores)
+
+        nbest_json = []
+        for (i, entry) in enumerate(nbest):
+            output = collections.OrderedDict()
+            output["text"] = entry.text
+            output["probability"] = probs[i]
+            output["start_logit"] = entry.start_logit
+            output["end_logit"] = entry.end_logit
+            nbest_json.append(output)
+
+        assert len(nbest_json) >= 1
+
+        if not version_2_with_negative:
+            all_predictions[example.qas_id] = nbest_json[0]["text"]
+        else:
+            # predict "" iff the null score - the score of best non-null > threshold
+            score_diff = score_null - best_non_null_entry.start_logit - (
+                best_non_null_entry.end_logit)
+            scores_diff_json[example.qas_id] = score_diff
+            if score_diff > null_score_diff_threshold:
+                all_predictions[example.qas_id] = ""
+            else:
+                all_predictions[example.qas_id] = best_non_null_entry.text
+
+        all_nbest_json[example.qas_id] = nbest_json
+
+    with open(output_prediction_file, "w") as writer:
+        writer.write(json.dumps(all_predictions, indent=4) + "\n")
+
+    with open(output_nbest_file, "w") as writer:
+        writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
+
+    if version_2_with_negative:
+        with open(output_null_log_odds_file, "w") as writer:
+            writer.write(json.dumps(scores_diff_json, indent=4) + "\n")
+
+
+def get_final_text(pred_text, orig_text, do_lower_case, verbose):
+    """Project the tokenized prediction back to the original text."""
+
+    # When we created the data, we kept track of the alignment between original
+    # (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
+    # now `orig_text` contains the span of our original text corresponding to the
+    # span that we predicted.
+    #
+    # However, `orig_text` may contain extra characters that we don't want in
+    # our prediction.
+    #
+    # For example, let's say:
+    #   pred_text = steve smith
+    #   orig_text = Steve Smith's
+    #
+    # We don't want to return `orig_text` because it contains the extra "'s".
+    #
+    # We don't want to return `pred_text` because it's already been normalized
+    # (the SQuAD eval script also does punctuation stripping/lower casing but
+    # our tokenizer does additional normalization like stripping accent
+    # characters).
+    #
+    # What we really want to return is "Steve Smith".
+    #
+    # Therefore, we have to apply a semi-complicated alignment heruistic between
+    # `pred_text` and `orig_text` to get a character-to-charcter alignment. This
+    # can fail in certain cases in which case we just return `orig_text`.
+
+    def _strip_spaces(text):
+        ns_chars = []
+        ns_to_s_map = collections.OrderedDict()
+        for (i, c) in enumerate(text):
+            if c == " ":
+                continue
+            ns_to_s_map[len(ns_chars)] = i
+            ns_chars.append(c)
+        ns_text = "".join(ns_chars)
+        return (ns_text, ns_to_s_map)
+
+    # We first tokenize `orig_text`, strip whitespace from the result
+    # and `pred_text`, and check if they are the same length. If they are
+    # NOT the same length, the heuristic has failed. If they are the same
+    # length, we assume the characters are one-to-one aligned.
+    tokenizer = tokenization.BasicTokenizer(do_lower_case=do_lower_case)
+
+    tok_text = " ".join(tokenizer.tokenize(orig_text))
+
+    start_position = tok_text.find(pred_text)
+    if start_position == -1:
+        if verbose:
+            print("Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
+        return orig_text
+    end_position = start_position + len(pred_text) - 1
+
+    (orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
+    (tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
+
+    if len(orig_ns_text) != len(tok_ns_text):
+        if verbose:
+            print("Length not equal after stripping spaces: '%s' vs '%s'",
+                  orig_ns_text, tok_ns_text)
+        return orig_text
+
+    # We then project the characters in `pred_text` back to `orig_text` using
+    # the character-to-character alignment.
+    tok_s_to_ns_map = {}
+    for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
+        tok_s_to_ns_map[tok_index] = i
+
+    orig_start_position = None
+    if start_position in tok_s_to_ns_map:
+        ns_start_position = tok_s_to_ns_map[start_position]
+        if ns_start_position in orig_ns_to_s_map:
+            orig_start_position = orig_ns_to_s_map[ns_start_position]
+
+    if orig_start_position is None:
+        if verbose:
+            print("Couldn't map start position")
+        return orig_text
+
+    orig_end_position = None
+    if end_position in tok_s_to_ns_map:
+        ns_end_position = tok_s_to_ns_map[end_position]
+        if ns_end_position in orig_ns_to_s_map:
+            orig_end_position = orig_ns_to_s_map[ns_end_position]
+
+    if orig_end_position is None:
+        if verbose:
+            print("Couldn't map end position")
+        return orig_text
+
+    output_text = orig_text[orig_start_position:(orig_end_position + 1)]
+    return output_text
+
+
+def _get_best_indexes(logits, n_best_size):
+    """Get the n-best logits from a list."""
+    index_and_score = sorted(
+        enumerate(logits), key=lambda x: x[1], reverse=True)
+
+    best_indexes = []
+    for i in range(len(index_and_score)):
+        if i >= n_best_size:
+            break
+        best_indexes.append(index_and_score[i][0])
+    return best_indexes
+
+
+def _compute_softmax(scores):
+    """Compute softmax probability over raw logits."""
+    if not scores:
+        return []
+
+    max_score = None
+    for score in scores:
+        if max_score is None or score > max_score:
+            max_score = score
+
+    exp_scores = []
+    total_sum = 0.0
+    for score in scores:
+        x = math.exp(score - max_score)
+        exp_scores.append(x)
+        total_sum += x
+
+    probs = []
+    for score in exp_scores:
+        probs.append(score / total_sum)
+    return probs
+
+
+if __name__ == '__main__':
+    train_file = 'squad/train-v1.1.json'
+    vocab_file = 'uncased_L-12_H-768_A-12/vocab.txt'
+    do_lower_case = True
+    tokenizer = tokenization.FullTokenizer(
+        vocab_file=vocab_file, do_lower_case=do_lower_case)
+    train_examples = read_squad_examples(
+        input_file=train_file, is_training=True)
+    print("begin converting")
+    for (index, feature) in enumerate(
+            convert_examples_to_features(
+                examples=train_examples,
+                tokenizer=tokenizer,
+                max_seq_length=384,
+                doc_stride=128,
+                max_query_length=64,
+                is_training=True,
+                #output_fn=train_writer.process_feature
+            )):
+        if index < 10:
+            print(index, feature.input_ids, feature.input_mask,
+                  feature.segment_ids)
+    #for (index, example) in enumerate(train_examples):
+    #    if index < 5:
+    #        print(example)

paddleslim/teachers/bert/tokenization.py

+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# 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.
+"""Tokenization classes."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import unicodedata
+import six
+import io
+
+
+def convert_to_unicode(text):
+    """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
+    if six.PY3:
+        if isinstance(text, str):
+            return text
+        elif isinstance(text, bytes):
+            return text.decode("utf-8", "ignore")
+        else:
+            raise ValueError("Unsupported string type: %s" % (type(text)))
+    elif six.PY2:
+        if isinstance(text, str):
+            return text.decode("utf-8", "ignore")
+        elif isinstance(text, unicode):
+            return text
+        else:
+            raise ValueError("Unsupported string type: %s" % (type(text)))
+    else:
+        raise ValueError("Not running on Python2 or Python 3?")
+
+
+def printable_text(text):
+    """Returns text encoded in a way suitable for print or `tf.logging`."""
+
+    # These functions want `str` for both Python2 and Python3, but in one case
+    # it's a Unicode string and in the other it's a byte string.
+    if six.PY3:
+        if isinstance(text, str):
+            return text
+        elif isinstance(text, bytes):
+            return text.decode("utf-8", "ignore")
+        else:
+            raise ValueError("Unsupported string type: %s" % (type(text)))
+    elif six.PY2:
+        if isinstance(text, str):
+            return text
+        elif isinstance(text, unicode):
+            return text.encode("utf-8")
+        else:
+            raise ValueError("Unsupported string type: %s" % (type(text)))
+    else:
+        raise ValueError("Not running on Python2 or Python 3?")
+
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    vocab = collections.OrderedDict()
+    fin = io.open(vocab_file, encoding="utf8")
+    for num, line in enumerate(fin):
+        items = convert_to_unicode(line.strip()).split("\t")
+        if len(items) > 2:
+            break
+        token = items[0]
+        index = items[1] if len(items) == 2 else num
+        token = token.strip()
+        vocab[token] = int(index)
+    return vocab
+
+
+def convert_by_vocab(vocab, items):
+    """Converts a sequence of [tokens|ids] using the vocab."""
+    output = []
+    for item in items:
+        output.append(vocab[item])
+    return output
+
+
+def convert_tokens_to_ids(vocab, tokens):
+    return convert_by_vocab(vocab, tokens)
+
+
+def convert_ids_to_tokens(inv_vocab, ids):
+    return convert_by_vocab(inv_vocab, ids)
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a peice of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
+
+
+class FullTokenizer(object):
+    """Runs end-to-end tokenziation."""
+
+    def __init__(self, vocab_file, do_lower_case=True):
+        self.vocab = load_vocab(vocab_file)
+        self.inv_vocab = {v: k for k, v in self.vocab.items()}
+        self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+
+    def tokenize(self, text):
+        split_tokens = []
+        for token in self.basic_tokenizer.tokenize(text):
+            for sub_token in self.wordpiece_tokenizer.tokenize(token):
+                split_tokens.append(sub_token)
+
+        return split_tokens
+
+    def convert_tokens_to_ids(self, tokens):
+        return convert_by_vocab(self.vocab, tokens)
+
+    def convert_ids_to_tokens(self, ids):
+        return convert_by_vocab(self.inv_vocab, ids)
+
+
+class CharTokenizer(object):
+    """Runs end-to-end tokenziation."""
+
+    def __init__(self, vocab_file, do_lower_case=True):
+        self.vocab = load_vocab(vocab_file)
+        self.inv_vocab = {v: k for k, v in self.vocab.items()}
+        self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+
+    def tokenize(self, text):
+        split_tokens = []
+        for token in text.lower().split(" "):
+            for sub_token in self.wordpiece_tokenizer.tokenize(token):
+                split_tokens.append(sub_token)
+
+        return split_tokens
+
+    def convert_tokens_to_ids(self, tokens):
+        return convert_by_vocab(self.vocab, tokens)
+
+    def convert_ids_to_tokens(self, ids):
+        return convert_by_vocab(self.inv_vocab, ids)
+
+
+class BasicTokenizer(object):
+    """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
+
+    def __init__(self, do_lower_case=True):
+        """Constructs a BasicTokenizer.
+
+        Args:
+            do_lower_case: Whether to lower case the input.
+        """
+        self.do_lower_case = do_lower_case
+
+    def tokenize(self, text):
+        """Tokenizes a piece of text."""
+        text = convert_to_unicode(text)
+        text = self._clean_text(text)
+
+        # This was added on November 1st, 2018 for the multilingual and Chinese
+        # models. This is also applied to the English models now, but it doesn't
+        # matter since the English models were not trained on any Chinese data
+        # and generally don't have any Chinese data in them (there are Chinese
+        # characters in the vocabulary because Wikipedia does have some Chinese
+        # words in the English Wikipedia.).
+        text = self._tokenize_chinese_chars(text)
+
+        orig_tokens = whitespace_tokenize(text)
+        split_tokens = []
+        for token in orig_tokens:
+            if self.do_lower_case:
+                token = token.lower()
+                token = self._run_strip_accents(token)
+            split_tokens.extend(self._run_split_on_punc(token))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+    def _run_strip_accents(self, text):
+        """Strips accents from a piece of text."""
+        text = unicodedata.normalize("NFD", text)
+        output = []
+        for char in text:
+            cat = unicodedata.category(char)
+            if cat == "Mn":
+                continue
+            output.append(char)
+        return "".join(output)
+
+    def _run_split_on_punc(self, text):
+        """Splits punctuation on a piece of text."""
+        chars = list(text)
+        i = 0
+        start_new_word = True
+        output = []
+        while i < len(chars):
+            char = chars[i]
+            if _is_punctuation(char):
+                output.append([char])
+                start_new_word = True
+            else:
+                if start_new_word:
+                    output.append([])
+                start_new_word = False
+                output[-1].append(char)
+            i += 1
+
+        return ["".join(x) for x in output]
+
+    def _tokenize_chinese_chars(self, text):
+        """Adds whitespace around any CJK character."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if self._is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+    def _is_chinese_char(self, cp):
+        """Checks whether CP is the codepoint of a CJK character."""
+        # This defines a "chinese character" as anything in the CJK Unicode block:
+        #     https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+        #
+        # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+        # despite its name. The modern Korean Hangul alphabet is a different block,
+        # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+        # space-separated words, so they are not treated specially and handled
+        # like the all of the other languages.
+        if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
+            (cp >= 0x3400 and cp <= 0x4DBF) or  #
+            (cp >= 0x20000 and cp <= 0x2A6DF) or  #
+            (cp >= 0x2A700 and cp <= 0x2B73F) or  #
+            (cp >= 0x2B740 and cp <= 0x2B81F) or  #
+            (cp >= 0x2B820 and cp <= 0x2CEAF) or
+            (cp >= 0xF900 and cp <= 0xFAFF) or  #
+            (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
+            return True
+
+        return False
+
+    def _clean_text(self, text):
+        """Performs invalid character removal and whitespace cleanup on text."""
+        output = []
+        for char in text:
+            cp = ord(char)
+            if cp == 0 or cp == 0xfffd or _is_control(char):
+                continue
+            if _is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+class WordpieceTokenizer(object):
+    """Runs WordPiece tokenziation."""
+
+    def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100):
+        self.vocab = vocab
+        self.unk_token = unk_token
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def tokenize(self, text):
+        """Tokenizes a piece of text into its word pieces.
+
+        This uses a greedy longest-match-first algorithm to perform tokenization
+        using the given vocabulary.
+
+        For example:
+            input = "unaffable"
+            output = ["un", "##aff", "##able"]
+
+        Args:
+            text: A single token or whitespace separated tokens. This should have
+                already been passed through `BasicTokenizer.
+
+        Returns:
+            A list of wordpiece tokens.
+        """
+
+        text = convert_to_unicode(text)
+
+        output_tokens = []
+        for token in whitespace_tokenize(text):
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = "".join(chars[start:end])
+                    if start > 0:
+                        substr = "##" + substr
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr)
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens
+
+
+def _is_whitespace(char):
+    """Checks whether `chars` is a whitespace character."""
+    # \t, \n, and \r are technically contorl characters but we treat them
+    # as whitespace since they are generally considered as such.
+    if char == " " or char == "\t" or char == "\n" or char == "\r":
+        return True
+    cat = unicodedata.category(char)
+    if cat == "Zs":
+        return True
+    return False
+
+
+def _is_control(char):
+    """Checks whether `chars` is a control character."""
+    # These are technically control characters but we count them as whitespace
+    # characters.
+    if char == "\t" or char == "\n" or char == "\r":
+        return False
+    cat = unicodedata.category(char)
+    if cat.startswith("C"):
+        return True
+    return False
+
+
+def _is_punctuation(char):
+    """Checks whether `chars` is a punctuation character."""
+    cp = ord(char)
+    # We treat all non-letter/number ASCII as punctuation.
+    # Characters such as "^", "$", and "`" are not in the Unicode
+    # Punctuation class but we treat them as punctuation anyways, for
+    # consistency.
+    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
+        (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
+        return True
+    cat = unicodedata.category(char)
+    if cat.startswith("P"):
+        return True
+    return False

paddleslim/teachers/bert/utils/convert_static_to_dygraph.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.
+import shutil
+import sys
+import os
+
+
+def usage():
+    """
+    usage information
+    """
+    print
+    print("please use command: ")
+    print(
+        "python convert_static_to_dygraph.py input_params_dir output_params_dir"
+    )
+    print
+
+
+def convert_static_to_dygraph(static_model_path, dygraph_model_path):
+    """
+    convert paddle static bert model to dygraph model 
+    """
+
+    def mkdir(path):
+        if not os.path.isdir(path):
+            if os.path.split(path)[0]:
+                mkdir(os.path.split(path)[0])
+        else:
+            return
+        os.mkdir(path)
+
+    if os.path.exists(dygraph_model_path):
+        shutil.rmtree(dygraph_model_path)
+    mkdir(dygraph_model_path)
+
+    if not os.path.exists(static_model_path):
+        print("paddle static model path doesn't exist.....")
+        return -1
+
+    file_list = []
+    for root, dirs, files in os.walk(static_model_path):
+        file_list.extend(files)
+
+    os.makedirs(os.path.join(dygraph_model_path, "PretrainModelLayer_0"))
+    os.makedirs(
+        os.path.join(dygraph_model_path,
+                     "PretrainModelLayer_0/BertModelLayer_0"))
+    os.makedirs(
+        os.path.join(dygraph_model_path,
+                     "PretrainModelLayer_0/PrePostProcessLayer_0"))
+    os.makedirs(
+        os.path.join(
+            dygraph_model_path,
+            "PretrainModelLayer_0/BertModelLayer_0/PrePostProcessLayer_0"))
+
+    #os.chdir(static_model_path)
+    #convert embedding file
+    embedding_type = ["word", "pos", "sent"]
+    for i in range(3):
+        src_name = embedding_type[i] + "_embedding"
+        trg_name = "Embedding_" + str(i) + "." + src_name
+        shutil.copyfile(
+            os.path.join(static_model_path, src_name),
+            os.path.join(dygraph_model_path,
+                         "PretrainModelLayer_0/BertModelLayer_0/" + trg_name))
+
+    #convert pre_encoder file
+    shutil.copyfile(
+        os.path.join(static_model_path, "pre_encoder_layer_norm_scale"),
+        os.path.join(
+            dygraph_model_path,
+            "PretrainModelLayer_0/BertModelLayer_0/PrePostProcessLayer_0/LayerNorm_0._layer_norm_scale"
+        ))
+    shutil.copyfile(
+        os.path.join(static_model_path, "pre_encoder_layer_norm_bias"),
+        os.path.join(
+            dygraph_model_path,
+            "PretrainModelLayer_0/BertModelLayer_0/PrePostProcessLayer_0/LayerNorm_0._layer_norm_bias"
+        ))
+
+    #convert mask lm params file
+    shutil.copyfile(
+        os.path.join(static_model_path, "mask_lm_out_fc.b_0"),
+        os.path.join(dygraph_model_path,
+                     "PretrainModelLayer_0/Layer_0.mask_lm_out_fc.b_0"))
+    shutil.copyfile(
+        os.path.join(static_model_path, "mask_lm_trans_fc.b_0"),
+        os.path.join(dygraph_model_path,
+                     "PretrainModelLayer_0/FC_0.mask_lm_trans_fc.b_0"))
+    shutil.copyfile(
+        os.path.join(static_model_path, "mask_lm_trans_fc.w_0"),
+        os.path.join(dygraph_model_path,
+                     "PretrainModelLayer_0/FC_0.mask_lm_trans_fc.w_0"))
+    shutil.copyfile(
+        os.path.join(static_model_path, "mask_lm_trans_layer_norm_bias"),
+        os.path.join(
+            dygraph_model_path,
+            "PretrainModelLayer_0/PrePostProcessLayer_0/LayerNorm_0._layer_norm_bias"
+        ))
+    shutil.copyfile(
+        os.path.join(static_model_path, "mask_lm_trans_layer_norm_scale"),
+        os.path.join(
+            dygraph_model_path,
+            "PretrainModelLayer_0/PrePostProcessLayer_0/LayerNorm_0._layer_norm_scale"
+        ))
+    shutil.copyfile(
+        os.path.join(static_model_path, "next_sent_fc.b_0"),
+        os.path.join(dygraph_model_path,
+                     "PretrainModelLayer_0/FC_1.next_sent_fc.b_0"))
+    shutil.copyfile(
+        os.path.join(static_model_path, "next_sent_fc.w_0"),
+        os.path.join(dygraph_model_path,
+                     "PretrainModelLayer_0/FC_1.next_sent_fc.w_0"))
+    shutil.copyfile(
+        os.path.join(static_model_path, "pooled_fc.b_0"),
+        os.path.join(
+            dygraph_model_path,
+            "PretrainModelLayer_0/BertModelLayer_0/FC_0.pooled_fc.b_0"))
+    shutil.copyfile(
+        os.path.join(static_model_path, "pooled_fc.w_0"),
+        os.path.join(
+            dygraph_model_path,
+            "PretrainModelLayer_0/BertModelLayer_0/FC_0.pooled_fc.w_0"))
+
+    encoder_num = 0
+    for f in file_list:
+        if not f.startswith("encoder_layer"):
+            continue
+        layer_num = f.split('_')[2]
+        if int(layer_num) > encoder_num:
+            encoder_num = int(layer_num)
+
+    encoder_num += 1
+    for i in range(encoder_num):
+        encoder_dir = "EncoderSubLayer_" + str(i)
+        os.makedirs(
+            os.path.join(dygraph_model_path,
+                         "PretrainModelLayer_0/BertModelLayer_0/" +
+                         "EncoderLayer_0/", encoder_dir))
+        os.makedirs(
+            os.path.join(dygraph_model_path,
+                         "PretrainModelLayer_0/BertModelLayer_0/" +
+                         "EncoderLayer_0/", encoder_dir +
+                         "/PositionwiseFeedForwardLayer_0"))
+        os.makedirs(
+            os.path.join(
+                dygraph_model_path, "PretrainModelLayer_0/BertModelLayer_0/" +
+                "EncoderLayer_0/", encoder_dir + "/MultiHeadAttentionLayer_0"))
+        os.makedirs(
+            os.path.join(
+                dygraph_model_path, "PretrainModelLayer_0/BertModelLayer_0/" +
+                "EncoderLayer_0/", encoder_dir + "/PrePostProcessLayer_1"))
+        os.makedirs(
+            os.path.join(
+                dygraph_model_path, "PretrainModelLayer_0/BertModelLayer_0/" +
+                "EncoderLayer_0/", encoder_dir + "/PrePostProcessLayer_3"))
+
+    encoder_map_dict = {
+        "ffn_fc_0.b_0":
+        ("PositionwiseFeedForwardLayer_0", "FC_0.ffn_fc_0.b_0"),
+        "ffn_fc_0.w_0":
+        ("PositionwiseFeedForwardLayer_0", "FC_0.ffn_fc_0.w_0"),
+        "ffn_fc_1.b_0":
+        ("PositionwiseFeedForwardLayer_0", "FC_1.ffn_fc_1.b_0"),
+        "ffn_fc_1.w_0":
+        ("PositionwiseFeedForwardLayer_0", "FC_1.ffn_fc_1.w_0"),
+        "multi_head_att_key_fc.b_0":
+        ("MultiHeadAttentionLayer_0", "FC_1.key_fc.b_0"),
+        "multi_head_att_key_fc.w_0":
+        ("MultiHeadAttentionLayer_0", "FC_1.key_fc.w_0"),
+        "multi_head_att_output_fc.b_0":
+        ("MultiHeadAttentionLayer_0", "FC_3.output_fc.b_0"),
+        "multi_head_att_output_fc.w_0":
+        ("MultiHeadAttentionLayer_0", "FC_3.output_fc.w_0"),
+        "multi_head_att_query_fc.b_0":
+        ("MultiHeadAttentionLayer_0", "FC_0.query_fc.b_0"),
+        "multi_head_att_query_fc.w_0":
+        ("MultiHeadAttentionLayer_0", "FC_0.query_fc.w_0"),
+        "multi_head_att_value_fc.b_0":
+        ("MultiHeadAttentionLayer_0", "FC_2.value_fc.b_0"),
+        "multi_head_att_value_fc.w_0":
+        ("MultiHeadAttentionLayer_0", "FC_2.value_fc.w_0"),
+        "post_att_layer_norm_bias":
+        ("PrePostProcessLayer_1", "LayerNorm_0.post_att_layer_norm_bias"),
+        "post_att_layer_norm_scale":
+        ("PrePostProcessLayer_1", "LayerNorm_0.post_att_layer_norm_scale"),
+        "post_ffn_layer_norm_bias":
+        ("PrePostProcessLayer_3", "LayerNorm_0.post_ffn_layer_norm_bias"),
+        "post_ffn_layer_norm_scale":
+        ("PrePostProcessLayer_3", "LayerNorm_0.post_ffn_layer_norm_scale")
+    }
+
+    for f in file_list:
+        if not f.startswith("encoder_layer"):
+            continue
+        layer_num = f.split('_')[2]
+        suffix_name = "_".join(f.split('_')[3:])
+        in_dir = encoder_map_dict[suffix_name][0]
+        rename = encoder_map_dict[suffix_name][1]
+        encoder_layer = "EncoderSubLayer_" + layer_num
+        shutil.copyfile(
+            os.path.join(static_model_path, f),
+            os.path.join(
+                dygraph_model_path,
+                "PretrainModelLayer_0/BertModelLayer_0/EncoderLayer_0/" +
+                encoder_layer + "/" + in_dir + "/" + rename))
+
+
+if __name__ == "__main__":
+
+    if len(sys.argv) < 3:
+        usage()
+        exit(1)
+    static_model_path = sys.argv[1]
+    dygraph_model_path = sys.argv[2]
+    convert_static_to_dygraph(static_model_path, dygraph_model_path)

paddleslim/teachers/bert/utils/fp16.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.
+
+from __future__ import print_function
+import paddle
+import paddle.fluid as fluid
+
+
+def cast_fp16_to_fp32(i, o, prog):
+    prog.global_block().append_op(
+        type="cast",
+        inputs={"X": i},
+        outputs={"Out": o},
+        attrs={
+            "in_dtype": fluid.core.VarDesc.VarType.FP16,
+            "out_dtype": fluid.core.VarDesc.VarType.FP32
+        })
+
+
+def cast_fp32_to_fp16(i, o, prog):
+    prog.global_block().append_op(
+        type="cast",
+        inputs={"X": i},
+        outputs={"Out": o},
+        attrs={
+            "in_dtype": fluid.core.VarDesc.VarType.FP32,
+            "out_dtype": fluid.core.VarDesc.VarType.FP16
+        })
+
+
+def copy_to_master_param(p, block):
+    v = block.vars.get(p.name, None)
+    if v is None:
+        raise ValueError("no param name %s found!" % p.name)
+    new_p = fluid.framework.Parameter(
+        block=block,
+        shape=v.shape,
+        dtype=fluid.core.VarDesc.VarType.FP32,
+        type=v.type,
+        lod_level=v.lod_level,
+        stop_gradient=p.stop_gradient,
+        trainable=p.trainable,
+        optimize_attr=p.optimize_attr,
+        regularizer=p.regularizer,
+        gradient_clip_attr=p.gradient_clip_attr,
+        error_clip=p.error_clip,
+        name=v.name + ".master")
+    return new_p
+
+
+def create_master_params_grads(params_grads, main_prog, startup_prog,
+                               loss_scaling):
+    master_params_grads = []
+    tmp_role = main_prog._current_role
+    OpRole = fluid.core.op_proto_and_checker_maker.OpRole
+    main_prog._current_role = OpRole.Backward
+    for p, g in params_grads:
+        # create master parameters
+        master_param = copy_to_master_param(p, main_prog.global_block())
+        startup_master_param = startup_prog.global_block()._clone_variable(
+            master_param)
+        startup_p = startup_prog.global_block().var(p.name)
+        cast_fp16_to_fp32(startup_p, startup_master_param, startup_prog)
+        # cast fp16 gradients to fp32 before apply gradients
+        if g.name.find("layer_norm") > -1:
+            if loss_scaling > 1:
+                scaled_g = g / float(loss_scaling)
+            else:
+                scaled_g = g
+            master_params_grads.append([p, scaled_g])
+            continue
+        master_grad = fluid.layers.cast(g, "float32")
+        if loss_scaling > 1:
+            master_grad = master_grad / float(loss_scaling)
+        master_params_grads.append([master_param, master_grad])
+    main_prog._current_role = tmp_role
+    return master_params_grads
+
+
+def master_param_to_train_param(master_params_grads, params_grads, main_prog):
+    for idx, m_p_g in enumerate(master_params_grads):
+        train_p, _ = params_grads[idx]
+        if train_p.name.find("layer_norm") > -1:
+            continue
+        with main_prog._optimized_guard([m_p_g[0], m_p_g[1]]):
+            cast_fp32_to_fp16(m_p_g[0], train_p, main_prog)