Update bert distillation and search code (#376)

demo/bert/search_bert.py

-import paddle.fluid as fluid
-from paddleslim.teachers.bert.reader.cls import *
-from paddleslim.nas.darts.search_space import AdaBERTClassifier
-from paddleslim.nas.darts import DARTSearch
-
-
-def main():
-    place = fluid.CUDAPlace(0)
-
-    BERT_BASE_PATH = "./data/pretrained_models/uncased_L-12_H-768_A-12/"
-    bert_config_path = BERT_BASE_PATH + "/bert_config.json"
-    vocab_path = BERT_BASE_PATH + "/vocab.txt"
-    data_dir = "./data/glue_data/MNLI/"
-    max_seq_len = 512
-    do_lower_case = True
-    batch_size = 32
-    epoch = 30
-
-    processor = MnliProcessor(
-        data_dir=data_dir,
-        vocab_path=vocab_path,
-        max_seq_len=max_seq_len,
-        do_lower_case=do_lower_case,
-        in_tokens=False)
-
-    train_reader = processor.data_generator(
-        batch_size=batch_size,
-        phase='train',
-        epoch=epoch,
-        dev_count=1,
-        shuffle=True)
-
-    val_reader = processor.data_generator(
-        batch_size=batch_size,
-        phase='train',
-        epoch=epoch,
-        dev_count=1,
-        shuffle=True)
-
-    with fluid.dygraph.guard(place):
-        model = AdaBERTClassifier(
-            3,
-            teacher_model="/work/PaddleSlim/demo/bert_1/checkpoints/steps_23000"
-        )
-        searcher = DARTSearch(
-            model,
-            train_reader,
-            val_reader,
-            batchsize=batch_size,
-            num_epochs=epoch,
-            log_freq=10)
-        searcher.train()
-
-
-if __name__ == '__main__':
-    main()

demo/bert/train_cell_base.py → demo/bert/train_distill.py

@@ -3,192 +3,201 @@ from itertools import izip
 import paddle.fluid as fluid
 from paddleslim.teachers.bert.reader.cls import *
 from paddleslim.nas.darts.search_space import AdaBERTClassifier
-from paddleslim.nas.darts.architect_for_bert import Architect
+from paddle.fluid.dygraph.base import to_variable
+from tqdm import tqdm
+import os
+import pickle
 
 import logging
 from paddleslim.common import AvgrageMeter, get_logger
 logger = get_logger(__name__, level=logging.INFO)
 
 
-def count_parameters_in_MB(all_params):
-    parameters_number = 0
-    for param in all_params:
-        if param.trainable:
-            parameters_number += np.prod(param.shape)
-    return parameters_number / 1e6
-
-
-def model_loss(model, data_ids):
-    # src_ids = data_ids[0]
-    # position_ids = data_ids[1]
-    # sentence_ids = data_ids[2]
-    # input_mask = data_ids[3]
-    labels = data_ids[4]
-    labels.stop_gradient = True
+def valid_one_epoch(model, valid_loader, epoch, log_freq):
+    accs = AvgrageMeter()
+    ce_losses = AvgrageMeter()
+    model.student.eval()
 
-    enc_output = model(data_ids)
+    step_id = 0
+    for valid_data in valid_loader():
+        try:
+            loss, acc, ce_loss, _, _ = model._layers.loss(valid_data, epoch)
+        except:
+            loss, acc, ce_loss, _, _ = model.loss(valid_data, epoch)
 
-    ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
-        logits=enc_output, label=labels, return_softmax=True)
-    loss = fluid.layers.mean(x=ce_loss)
-    num_seqs = fluid.layers.create_tensor(dtype='int64')
-    accuracy = fluid.layers.accuracy(input=probs, label=labels, total=num_seqs)
-    return loss, accuracy
+        batch_size = valid_data[0].shape[0]
+        ce_losses.update(ce_loss.numpy(), batch_size)
+        accs.update(acc.numpy(), batch_size)
+        step_id += 1
+    return ce_losses.avg[0], accs.avg[0]
 
 
-def train_one_epoch(model, architect, train_loader, valid_loader, optimizer,
-                    epoch, use_data_parallel, log_freq):
-    ce_losses = AvgrageMeter()
+def train_one_epoch(model, train_loader, optimizer, epoch, use_data_parallel,
+                    log_freq):
+    total_losses = AvgrageMeter()
     accs = AvgrageMeter()
-    model.train()
+    ce_losses = AvgrageMeter()
+    kd_losses = AvgrageMeter()
+    model.student.train()
 
     step_id = 0
-    for train_data, valid_data in izip(train_loader(), valid_loader):
-        architect.step(train_data, valid_data)
-        loss, acc = model_loss(model, train_data)
+    for train_data in train_loader():
+        batch_size = train_data[0].shape[0]
 
         if use_data_parallel:
-            loss = model.scale_loss(loss)
-            loss.backward()
-            model.apply_collective_grads()
+            total_loss, acc, ce_loss, kd_loss, _ = model._layers.loss(
+                train_data, epoch)
         else:
-            loss.backward()
+            total_loss, acc, ce_loss, kd_loss, _ = model.loss(train_data,
+                                                              epoch)
 
-        optimizer.minimize(loss)
+        if use_data_parallel:
+            total_loss = model.scale_loss(total_loss)
+            total_loss.backward()
+            model.apply_collective_grads()
+        else:
+            total_loss.backward()
+        optimizer.minimize(total_loss)
         model.clear_gradients()
-
-        batch_size = train_data[0].shape[0]
-        ce_losses.update(loss.numpy(), batch_size)
+        total_losses.update(total_loss.numpy(), batch_size)
         accs.update(acc.numpy(), batch_size)
+        ce_losses.update(ce_loss.numpy(), batch_size)
+        kd_losses.update(kd_loss.numpy(), batch_size)
 
         if step_id % log_freq == 0:
             logger.info(
-                "Train Epoch {}, Step {}, Lr {:.6f} loss {:.6f}; acc: {:.6f};".
+                "Train Epoch {}, Step {}, Lr {:.6f} total_loss {:.6f}; ce_loss {:.6f}, kd_loss {:.6f}, train_acc {:.6f};".
                 format(epoch, step_id,
-                       optimizer.current_step_lr(), ce_losses.avg[0], accs.avg[
-                           0]))
-        step_id += 1
-
-
-def valid_one_epoch(model, valid_loader, epoch, log_freq):
-    ce_losses = AvgrageMeter()
-    accs = AvgrageMeter()
-    model.eval()
-
-    step_id = 0
-    for valid_data in valid_loader():
-        loss, acc = model_loss(model, valid_data)
-
-        batch_size = valid_data[0].shape[0]
-        ce_losses.update(loss.numpy(), batch_size)
-        accs.update(acc.numpy(), batch_size)
-
-        if step_id % log_freq == 0:
-            logger.info("Valid Epoch {}, Step {}, loss {:.6f}; acc: {:.6f};".
-                        format(epoch, step_id, ce_losses.avg[0], accs.avg[0]))
+                       optimizer.current_step_lr(), total_losses.avg[0],
+                       ce_losses.avg[0], kd_losses.avg[0], accs.avg[0]))
         step_id += 1
 
 
 def main():
+    # whether use multi-gpus
     use_data_parallel = False
     place = fluid.CUDAPlace(fluid.dygraph.parallel.Env(
     ).dev_id) if use_data_parallel else fluid.CUDAPlace(0)
 
     BERT_BASE_PATH = "./data/pretrained_models/uncased_L-12_H-768_A-12"
-    bert_config_path = BERT_BASE_PATH + "/bert_config.json"
     vocab_path = BERT_BASE_PATH + "/vocab.txt"
-    data_dir = "./data/glue_data/MNLI/"
-    teacher_model_dir = "./teacher_model/steps_23000"
-    num_samples = 392702
-    max_seq_len = 128
+
     do_lower_case = True
-    batch_size = 128
+    # augmented dataset nums
+    # num_samples = 8016987
+
+    max_seq_len = 128
+    batch_size = 192
     hidden_size = 768
     emb_size = 768
-    max_layer = 8
     epoch = 80
     log_freq = 10
-    use_fixed_gumbel = True
 
-    processor = MnliProcessor(
-        data_dir=data_dir,
-        vocab_path=vocab_path,
-        max_seq_len=max_seq_len,
-        do_lower_case=do_lower_case,
-        in_tokens=False)
+    task_name = 'mnli'
 
-    train_reader = processor.data_generator(
-        batch_size=batch_size,
-        phase='search_train',
-        epoch=1,
-        dev_count=1,
-        shuffle=True)
-
-    val_reader = processor.data_generator(
-        batch_size=batch_size,
-        phase='search_valid',
-        epoch=1,
-        dev_count=1,
-        shuffle=True)
+    if task_name == 'mrpc':
+        data_dir = "./data/glue_data/MRPC/"
+        teacher_model_dir = "./data/teacher_model/mrpc"
+        num_samples = 3668
+        max_layer = 4
+        num_labels = 2
+        processor_func = MrpcProcessor
+    elif task_name == 'mnli':
+        data_dir = "./data/glue_data/MNLI/"
+        teacher_model_dir = "./data/teacher_model/steps_23000"
+        num_samples = 392702
+        max_layer = 8
+        num_labels = 3
+        processor_func = MnliProcessor
 
-    if use_data_parallel:
-        train_reader = fluid.contrib.reader.distributed_batch_reader(
-            train_reader)
-        valid_reader = fluid.contrib.reader.distributed_batch_reader(
-            valid_reader)
+    device_num = fluid.dygraph.parallel.Env().nranks
+    use_fixed_gumbel = True
+    train_phase = "train"
+    val_phase = "dev"
+    step_per_epoch = int(num_samples / (batch_size * device_num))
 
     with fluid.dygraph.guard(place):
+        if use_fixed_gumbel:
+            # make sure gumbel arch is constant
+            np.random.seed(1)
+            fluid.default_main_program().random_seed = 1
         model = AdaBERTClassifier(
-            3,
+            num_labels,
             n_layer=max_layer,
             hidden_size=hidden_size,
+            task_name=task_name,
             emb_size=emb_size,
             teacher_model=teacher_model_dir,
             data_dir=data_dir,
             use_fixed_gumbel=use_fixed_gumbel)
 
-        if use_data_parallel:
-            strategy = fluid.dygraph.parallel.prepare_context()
-            model = fluid.dygraph.parallel.DataParallel(model, strategy)
-
-        device_num = fluid.dygraph.parallel.Env().nranks
-        step_per_epoch = int(num_samples / (batch_size * device_num))
         learning_rate = fluid.dygraph.CosineDecay(2e-2, step_per_epoch, epoch)
 
-        model_parameters = [
-            p for p in model.parameters()
-            if p.name not in [a.name for a in model.arch_parameters()]
-        ]
+        model_parameters = []
+        for p in model.parameters():
+            if (p.name not in [a.name for a in model.arch_parameters()] and
+                    p.name not in
+                [a.name for a in model.teacher.parameters()]):
+                model_parameters.append(p)
 
-        clip = fluid.clip.GradientClipByGlobalNorm(clip_norm=5.0)
         optimizer = fluid.optimizer.MomentumOptimizer(
             learning_rate,
             0.9,
             regularization=fluid.regularizer.L2DecayRegularizer(3e-4),
-            parameter_list=model_parameters,
-            grad_clip=clip)
+            parameter_list=model_parameters)
+
+        processor = processor_func(
+            data_dir=data_dir,
+            vocab_path=vocab_path,
+            max_seq_len=max_seq_len,
+            do_lower_case=do_lower_case,
+            in_tokens=False)
+
+        train_reader = processor.data_generator(
+            batch_size=batch_size,
+            phase=train_phase,
+            epoch=1,
+            dev_count=1,
+            shuffle=True)
+        dev_reader = processor.data_generator(
+            batch_size=batch_size,
+            phase=val_phase,
+            epoch=1,
+            dev_count=1,
+            shuffle=False)
+
+        if use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
 
         train_loader = fluid.io.DataLoader.from_generator(
-            capacity=1024,
+            capacity=128,
             use_double_buffer=True,
             iterable=True,
             return_list=True)
-        valid_loader = fluid.io.DataLoader.from_generator(
-            capacity=1024,
+        dev_loader = fluid.io.DataLoader.from_generator(
+            capacity=128,
             use_double_buffer=True,
             iterable=True,
             return_list=True)
+
         train_loader.set_batch_generator(train_reader, places=place)
-        valid_loader.set_batch_generator(val_reader, places=place)
+        dev_loader.set_batch_generator(dev_reader, places=place)
 
-        architect = Architect(model, learning_rate, 3e-4, place, False)
+        if use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+            model = fluid.dygraph.parallel.DataParallel(model, strategy)
 
+        best_valid_acc = 0
         for epoch_id in range(epoch):
-            train_one_epoch(model, architect, train_loader, valid_loader,
-                            optimizer, epoch_id, use_data_parallel, log_freq)
-            valid_one_epoch(model, valid_loader, epoch_id, log_freq)
-            print(model.student._encoder.alphas.numpy())
-            print("=" * 100)
+            train_one_epoch(model, train_loader, optimizer, epoch_id,
+                            use_data_parallel, log_freq)
+            loss, acc = valid_one_epoch(model, dev_loader, epoch_id, log_freq)
+            if acc > best_valid_acc:
+                best_valid_acc = acc
+            logger.info(
+                "dev set, ce_loss {:.6f}; acc {:.6f}, best_acc {:.6f};".format(
+                    loss, acc, best_valid_acc))
 
 
 if __name__ == '__main__':

demo/bert/train_search.py

+import numpy as np
+from itertools import izip
+import paddle.fluid as fluid
+from paddleslim.teachers.bert.reader.cls import *
+from paddleslim.nas.darts.search_space import AdaBERTClassifier
+from paddle.fluid.dygraph.base import to_variable
+from tqdm import tqdm
+import os
+import pickle
+
+import logging
+from paddleslim.common import AvgrageMeter, get_logger
+logger = get_logger(__name__, level=logging.INFO)
+
+
+def valid_one_epoch(model, valid_loader, epoch, log_freq):
+    accs = AvgrageMeter()
+    ce_losses = AvgrageMeter()
+    model.student.eval()
+
+    step_id = 0
+    for valid_data in valid_loader():
+        try:
+            loss, acc, ce_loss, _, _ = model._layers.loss(valid_data, epoch)
+        except:
+            loss, acc, ce_loss, _, _ = model.loss(valid_data, epoch)
+
+        batch_size = valid_data[0].shape[0]
+        ce_losses.update(ce_loss.numpy(), batch_size)
+        accs.update(acc.numpy(), batch_size)
+        step_id += 1
+    return ce_losses.avg[0], accs.avg[0]
+
+
+def train_one_epoch(model, train_loader, valid_loader, optimizer,
+                    arch_optimizer, epoch, use_data_parallel, log_freq):
+    total_losses = AvgrageMeter()
+    accs = AvgrageMeter()
+    ce_losses = AvgrageMeter()
+    kd_losses = AvgrageMeter()
+    val_accs = AvgrageMeter()
+    model.student.train()
+
+    step_id = 0
+    for train_data, valid_data in izip(train_loader(), valid_loader()):
+        batch_size = train_data[0].shape[0]
+        # make sure arch on every gpu is same, otherwise an error will occurs
+        np.random.seed(step_id * 2 * (epoch + 1))
+        if use_data_parallel:
+            total_loss, acc, ce_loss, kd_loss, _ = model._layers.loss(
+                train_data, epoch)
+        else:
+            total_loss, acc, ce_loss, kd_loss, _ = model.loss(train_data,
+                                                              epoch)
+
+        if use_data_parallel:
+            total_loss = model.scale_loss(total_loss)
+            total_loss.backward()
+            model.apply_collective_grads()
+        else:
+            total_loss.backward()
+        optimizer.minimize(total_loss)
+        model.clear_gradients()
+        total_losses.update(total_loss.numpy(), batch_size)
+        accs.update(acc.numpy(), batch_size)
+        ce_losses.update(ce_loss.numpy(), batch_size)
+        kd_losses.update(kd_loss.numpy(), batch_size)
+
+        # make sure arch on every gpu is same, otherwise an error will occurs
+        np.random.seed(step_id * 2 * (epoch + 1) + 1)
+        if use_data_parallel:
+            arch_loss, _, _, _, arch_logits = model._layers.loss(valid_data,
+                                                                 epoch)
+        else:
+            arch_loss, _, _, _, arch_logits = model.loss(valid_data, epoch)
+
+        if use_data_parallel:
+            arch_loss = model.scale_loss(arch_loss)
+            arch_loss.backward()
+            model.apply_collective_grads()
+        else:
+            arch_loss.backward()
+        arch_optimizer.minimize(arch_loss)
+        model.clear_gradients()
+        probs = fluid.layers.softmax(arch_logits[-1])
+        val_acc = fluid.layers.accuracy(input=probs, label=valid_data[4])
+        val_accs.update(val_acc.numpy(), batch_size)
+
+        if step_id % log_freq == 0:
+            logger.info(
+                "Train Epoch {}, Step {}, Lr {:.6f} total_loss {:.6f}; ce_loss {:.6f}, kd_loss {:.6f}, train_acc {:.6f}, search_valid_acc {:.6f};".
+                format(epoch, step_id,
+                       optimizer.current_step_lr(), total_losses.avg[
+                           0], ce_losses.avg[0], kd_losses.avg[0], accs.avg[0],
+                       val_accs.avg[0]))
+
+        step_id += 1
+
+
+def main():
+    # whether use multi-gpus
+    use_data_parallel = False
+    place = fluid.CUDAPlace(fluid.dygraph.parallel.Env(
+    ).dev_id) if use_data_parallel else fluid.CUDAPlace(0)
+
+    BERT_BASE_PATH = "./data/pretrained_models/uncased_L-12_H-768_A-12"
+    vocab_path = BERT_BASE_PATH + "/vocab.txt"
+    data_dir = "./data/glue_data/MNLI/"
+    teacher_model_dir = "./data/teacher_model/steps_23000"
+    do_lower_case = True
+    num_samples = 392702
+    # augmented dataset nums
+    # num_samples = 8016987
+    max_seq_len = 128
+    batch_size = 128
+    hidden_size = 768
+    emb_size = 768
+    max_layer = 8
+    epoch = 80
+    log_freq = 10
+    device_num = fluid.dygraph.parallel.Env().nranks
+
+    use_fixed_gumbel = False
+    train_phase = "search_train"
+    val_phase = "search_valid"
+    step_per_epoch = int(num_samples * 0.5 / ((batch_size) * device_num))
+
+    with fluid.dygraph.guard(place):
+        model = AdaBERTClassifier(
+            3,
+            n_layer=max_layer,
+            hidden_size=hidden_size,
+            emb_size=emb_size,
+            teacher_model=teacher_model_dir,
+            data_dir=data_dir,
+            use_fixed_gumbel=use_fixed_gumbel)
+
+        learning_rate = fluid.dygraph.CosineDecay(2e-2, step_per_epoch, epoch)
+
+        model_parameters = []
+        for p in model.parameters():
+            if (p.name not in [a.name for a in model.arch_parameters()] and
+                    p.name not in
+                [a.name for a in model.teacher.parameters()]):
+                model_parameters.append(p)
+
+        optimizer = fluid.optimizer.MomentumOptimizer(
+            learning_rate,
+            0.9,
+            regularization=fluid.regularizer.L2DecayRegularizer(3e-4),
+            parameter_list=model_parameters)
+
+        arch_optimizer = fluid.optimizer.Adam(
+            3e-4,
+            0.5,
+            0.999,
+            regularization=fluid.regularizer.L2Decay(1e-3),
+            parameter_list=model.arch_parameters())
+
+        processor = MnliProcessor(
+            data_dir=data_dir,
+            vocab_path=vocab_path,
+            max_seq_len=max_seq_len,
+            do_lower_case=do_lower_case,
+            in_tokens=False)
+
+        train_reader = processor.data_generator(
+            batch_size=batch_size,
+            phase=train_phase,
+            epoch=1,
+            dev_count=1,
+            shuffle=True)
+        valid_reader = processor.data_generator(
+            batch_size=batch_size,
+            phase=val_phase,
+            epoch=1,
+            dev_count=1,
+            shuffle=True)
+        dev_reader = processor.data_generator(
+            batch_size=batch_size,
+            phase="dev",
+            epoch=1,
+            dev_count=1,
+            shuffle=False)
+
+        if use_data_parallel:
+            train_reader = fluid.contrib.reader.distributed_batch_reader(
+                train_reader)
+            valid_reader = fluid.contrib.reader.distributed_batch_reader(
+                valid_reader)
+
+        train_loader = fluid.io.DataLoader.from_generator(
+            capacity=128,
+            use_double_buffer=True,
+            iterable=True,
+            return_list=True)
+        valid_loader = fluid.io.DataLoader.from_generator(
+            capacity=128,
+            use_double_buffer=True,
+            iterable=True,
+            return_list=True)
+        dev_loader = fluid.io.DataLoader.from_generator(
+            capacity=128,
+            use_double_buffer=True,
+            iterable=True,
+            return_list=True)
+
+        train_loader.set_batch_generator(train_reader, places=place)
+        valid_loader.set_batch_generator(valid_reader, places=place)
+        dev_loader.set_batch_generator(dev_reader, places=place)
+
+        if use_data_parallel:
+            strategy = fluid.dygraph.parallel.prepare_context()
+            model = fluid.dygraph.parallel.DataParallel(model, strategy)
+
+        for epoch_id in range(epoch):
+            train_one_epoch(model, train_loader, valid_loader, optimizer,
+                            arch_optimizer, epoch_id, use_data_parallel,
+                            log_freq)
+            loss, acc = valid_one_epoch(model, dev_loader, epoch_id, log_freq)
+            logger.info("dev set, ce_loss {:.6f}; acc: {:.6f};".format(loss,
+                                                                       acc))
+
+            if use_data_parallel:
+                print(model._layers.student._encoder.alphas.numpy())
+            else:
+                print(model.student._encoder.alphas.numpy())
+            print("=" * 100)
+
+
+if __name__ == '__main__':
+    main()

paddleslim/nas/darts/architect_for_bert.py

@@ -49,17 +49,17 @@ class Architect(object):
                     self.network_weight_decay),
                 parameter_list=self.unrolled_model_params)
 
-    def step(self, train_data, valid_data):
+    def step(self, train_data, valid_data, epoch):
         if self.unrolled:
             params_grads = self._backward_step_unrolled(train_data, valid_data)
             self.optimizer.apply_gradients(params_grads)
         else:
-            loss = self._backward_step(valid_data)
+            loss = self._backward_step(valid_data, epoch)
             self.optimizer.minimize(loss)
         self.optimizer.clear_gradients()
 
-    def _backward_step(self, valid_data):
-        loss = self.model.loss(valid_data)
+    def _backward_step(self, valid_data, epoch):
+        loss = self.model.loss(valid_data, epoch)
         loss[0].backward()
         return loss[0]
 

paddleslim/nas/darts/search_space/conv_bert/cls.py

@@ -31,6 +31,7 @@ import multiprocessing
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.dygraph import to_variable, Layer, Linear
+from paddle.fluid.dygraph.base import to_variable
 from .reader.cls import *
 from .model.bert import BertModelLayer
 from .optimization import Optimizer
@@ -48,6 +49,7 @@ class AdaBERTClassifier(Layer):
                  hidden_size=768,
                  gamma=0.8,
                  beta=4,
+                 task_name='mnli',
                  conv_type="conv_bn",
                  search_layer=False,
                  teacher_model=None,
@@ -68,17 +70,21 @@ class AdaBERTClassifier(Layer):
         self._teacher_model = teacher_model
         self._data_dir = data_dir
         self.use_fixed_gumbel = use_fixed_gumbel
+
         self.T = t
         print(
             "----------------------load teacher model and test----------------------------------------"
         )
         self.teacher = BERTClassifier(
-            num_labels, model_path=self._teacher_model)
+            num_labels, task_name=task_name, model_path=self._teacher_model)
+        # global setting, will be overwritten when training(about 1% acc loss)
+        self.teacher.eval()
         self.teacher.test(self._data_dir)
         print(
             "----------------------finish load teacher model and test----------------------------------------"
         )
         self.student = BertModelLayer(
+            num_labels=num_labels,
             n_layer=self._n_layer,
             emb_size=self._emb_size,
             hidden_size=self._hidden_size,
@@ -87,6 +93,7 @@ class AdaBERTClassifier(Layer):
             use_fixed_gumbel=self.use_fixed_gumbel,
             gumbel_alphas=gumbel_alphas)
 
+        fix_emb = False
         for s_emb, t_emb in zip(self.student.emb_names(),
                                 self.teacher.emb_names()):
             t_emb.stop_gradient = True
@@ -100,91 +107,58 @@ class AdaBERTClassifier(Layer):
                 "Assigned embedding[{}] from teacher to embedding[{}] in student.".
                 format(t_emb.name, s_emb.name))
 
-        self.cls_fc = list()
-        for i in range(self._n_layer):
-            fc = Linear(
-                input_dim=self._hidden_size,
-                output_dim=self._num_labels,
-                param_attr=fluid.ParamAttr(
-                    name="s_cls_out_%d_w" % i,
-                    initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
-                bias_attr=fluid.ParamAttr(
-                    name="s_cls_out_%d_b" % i,
-                    initializer=fluid.initializer.Constant(0.)))
-            fc = self.add_sublayer("cls_fc_%d" % i, fc)
-            self.cls_fc.append(fc)
-
-    def forward(self, data_ids):
-        src_ids = data_ids[0]
-        position_ids = data_ids[1]
-        sentence_ids = data_ids[2]
-        return self.student(src_ids, position_ids, sentence_ids)
+    def forward(self, data_ids, epoch):
+        return self.student(data_ids, epoch)
 
     def arch_parameters(self):
         return self.student.arch_parameters()
 
-    def genotype(self):
-        return self.arch_parameters()
-
-    def ce(self, logits):
-        logits = np.exp(logits - np.max(logits))
-        logits = logits / logits.sum(axis=0)
-        return logits
-
-    def loss(self, data_ids):
-        src_ids = data_ids[0]
-        position_ids = data_ids[1]
-        sentence_ids = data_ids[2]
-        input_mask = data_ids[3]
+    def loss(self, data_ids, epoch):
         labels = data_ids[4]
 
-        s_logits = self.student(
-            src_ids, position_ids, sentence_ids, flops=[], model_size=[])
+        s_logits = self.student(data_ids, epoch)
 
-        self.teacher.eval()
-        total_loss, t_logits, t_losses, accuracys, num_seqs = self.teacher(
-            data_ids)
-
-        # define kd loss
-        kd_losses = []
+        t_enc_outputs, t_logits, t_losses, t_accs, _ = self.teacher(data_ids)
 
+        #define kd loss
         kd_weights = []
         for i in range(len(s_logits)):
             j = int(np.ceil(i * (float(len(t_logits)) / len(s_logits))))
             kd_weights.append(t_losses[j].numpy())
 
         kd_weights = np.array(kd_weights)
-        kd_weights = self.ce(-kd_weights)
-        s_probs = None
+        kd_weights = np.squeeze(kd_weights)
+        kd_weights = to_variable(kd_weights)
+        kd_weights = fluid.layers.softmax(-kd_weights)
+
+        kd_losses = []
         for i in range(len(s_logits)):
             j = int(np.ceil(i * (float(len(t_logits)) / len(s_logits))))
             t_logit = t_logits[j]
             s_logit = s_logits[i]
             t_logit.stop_gradient = True
-            t_probs = fluid.layers.softmax(t_logit / self.T)
-            s_probs = fluid.layers.softmax(s_logit)
+            t_probs = fluid.layers.softmax(t_logit)  # P_j^T
+            s_probs = fluid.layers.softmax(s_logit / self.T)  #P_j^S
             #kd_loss = -t_probs * fluid.layers.log(s_probs)
             kd_loss = fluid.layers.cross_entropy(
                 input=s_probs, label=t_probs, soft_label=True)
-            kd_loss = fluid.layers.reduce_sum(kd_loss, dim=1)
-            kd_loss = fluid.layers.mean(kd_loss)
-            #            print("kd_loss[{}] = {}; kd_weights[{}] = {}".format(i, kd_loss.numpy(), i, kd_weights[i]))
-            #            tmp = kd_loss * kd_weights[i]
-            tmp = fluid.layers.scale(kd_loss, scale=kd_weights[i])
-            #            print("kd_loss[{}] = {}".format(i, tmp.numpy()))
-            kd_losses.append(tmp)
-
+            kd_loss = fluid.layers.reduce_mean(kd_loss)
+            kd_loss = fluid.layers.scale(kd_loss, scale=kd_weights[i])
+            kd_losses.append(kd_loss)
         kd_loss = fluid.layers.sum(kd_losses)
 
-        #        print("kd_loss = {}".format(kd_loss.numpy()))
-
+        losses = []
+        for logit in s_logits:
             ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
-            logits=s_logits[-1], label=labels, return_softmax=True)
-        ce_loss = fluid.layers.mean(x=ce_loss)
+                logits=logit, label=labels, return_softmax=True)
+            loss = fluid.layers.mean(x=ce_loss)
+            losses.append(loss)
+
             num_seqs = fluid.layers.create_tensor(dtype='int64')
             accuracy = fluid.layers.accuracy(
                 input=probs, label=labels, total=num_seqs)
+        ce_loss = fluid.layers.sum(losses)
+
+        total_loss = (1 - self._gamma) * ce_loss + self._gamma * kd_loss
 
-        loss = (1 - self._gamma) * ce_loss + self._gamma * kd_loss
-        #        return ce_loss, accuracy, None, None
-        return loss, accuracy, ce_loss, kd_loss
+        return total_loss, accuracy, ce_loss, kd_loss, s_logits

paddleslim/nas/darts/search_space/conv_bert/model/bert.py

@@ -32,6 +32,7 @@ from .transformer_encoder import EncoderLayer
 
 class BertModelLayer(Layer):
     def __init__(self,
+                 num_labels,
                  emb_size=128,
                  hidden_size=768,
                  n_layer=12,
@@ -91,6 +92,7 @@ class BertModelLayer(Layer):
             param_attr=fluid.ParamAttr(name="s_emb_factorization"))
 
         self._encoder = EncoderLayer(
+            num_labels=num_labels,
             n_layer=self._n_layer,
             hidden_size=self._hidden_size,
             search_layer=self._search_layer,
@@ -101,6 +103,10 @@ class BertModelLayer(Layer):
         return self._src_emb.parameters() + self._pos_emb.parameters(
         ) + self._sent_emb.parameters()
 
+    def emb_names(self):
+        return self._src_emb.parameters() + self._pos_emb.parameters(
+        ) + self._sent_emb.parameters()
+
     def max_flops(self):
         return self._encoder.max_flops
 
@@ -110,54 +116,19 @@ class BertModelLayer(Layer):
     def arch_parameters(self):
         return [self._encoder.alphas]  #, self._encoder.k]
 
-    def forward(self,
-                src_ids,
-                position_ids,
-                sentence_ids,
-                flops=[],
-                model_size=[]):
+    def forward(self, data_ids, epoch):
         """
         forward
         """
-        ids = np.squeeze(src_ids.numpy())
-        sids = np.squeeze(sentence_ids.numpy())
-        batchsize = ids.shape[0]
-
-        ids_0 = ids[((sids == 0) & (ids != 0))]
-        seqlen_0 = ((sids == 0) & (ids != 0)).astype(np.int64).sum(1)
-        y_0 = np.concatenate([np.arange(s) for s in seqlen_0])
-        x_0 = np.concatenate([
-            np.ones(
-                [s], dtype=np.int64) * i for i, s in enumerate(seqlen_0)
-        ])
-        ids0 = np.zeros([batchsize, seqlen_0.max()], dtype=np.int64)
-        ids0[(x_0, y_0)] = ids_0
-
-        ids_1 = ids[(sids == 1) & (ids != 0)]
-        seqlen_1 = ((sids == 1) & (ids != 0)).astype(np.int64).sum(1)
-        y_1 = np.concatenate([np.arange(s) for s in seqlen_1])
-        x_1 = np.concatenate([
-            np.ones(
-                [s], dtype=np.int64) * i for i, s in enumerate(seqlen_1)
-        ])
-        ids1 = np.zeros([batchsize, seqlen_1.max()], dtype=np.int64)
-        ids1[(x_1, y_1)] = ids_1
-
-        msl = max(seqlen_0.max(), seqlen_1.max())
-        ids0 = np.pad(ids0, [[0, 0], [0, msl - seqlen_0.max()]],
-                      mode='constant')
-        ids1 = np.pad(ids1, [[0, 0], [0, msl - seqlen_1.max()]],
-                      mode='constant')
-
-        ids0 = fluid.dygraph.to_variable(ids0)
-        ids1 = fluid.dygraph.to_variable(ids1)
+        ids0 = data_ids[5]
+        ids1 = data_ids[6]
 
         src_emb_0 = self._src_emb(ids0)
         src_emb_1 = self._src_emb(ids1)
         emb_out_0 = self._emb_fac(src_emb_0)
         emb_out_1 = self._emb_fac(src_emb_1)
-        # (bs, seq_len, 768)
+        # (bs, seq_len, hidden_size)
+
+        enc_outputs = self._encoder(emb_out_0, emb_out_1, epoch)
 
-        enc_outputs = self._encoder(
-            emb_out, flops=flops, model_size=model_size)
         return enc_outputs

paddleslim/nas/darts/search_space/conv_bert/model/transformer_encoder.py

@@ -23,14 +23,15 @@ from collections import Iterable
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.dygraph import Embedding, LayerNorm, Linear, Layer, Conv2D, BatchNorm, Pool2D, to_variable
+from paddle.fluid.dygraph import to_variable
 from paddle.fluid.initializer import NormalInitializer
 from paddle.fluid import ParamAttr
 from paddle.fluid.initializer import MSRA, ConstantInitializer
 
 ConvBN_PRIMITIVES = [
     'std_conv_bn_3', 'std_conv_bn_5', 'std_conv_bn_7', 'dil_conv_bn_3',
-    'dil_conv_bn_5', 'dil_conv_bn_7', 'avg_pool_3', 'max_pool_3', 'none',
-    'skip_connect'
+    'dil_conv_bn_5', 'dil_conv_bn_7', 'avg_pool_3', 'max_pool_3',
+    'skip_connect', 'none'
 ]
 
 
@@ -53,11 +54,6 @@ class MixedOp(fluid.dygraph.Layer):
     def __init__(self, n_channel, name=None):
         super(MixedOp, self).__init__()
         PRIMITIVES = ConvBN_PRIMITIVES
-        # ops = [
-        #     OPS[primitive](n_channel, name
-        #                    if name is None else name + "/" + primitive)
-        #     for primitive in PRIMITIVES
-        # ]
         ops = []
         for primitive in PRIMITIVES:
             op = OPS[primitive](n_channel, name
@@ -76,26 +72,17 @@ class MixedOp(fluid.dygraph.Layer):
         self._ops = fluid.dygraph.LayerList(ops)
 
     def forward(self, x, weights):
-        #out = weights[0] * self._ops[0](x)
         # out = fluid.layers.sums(
         #     [weights[i] * op(x) for i, op in enumerate(self._ops)])
         # return out
 
-        for i in range(len(self._ops)):
-
-            if isinstance(weights, Iterable):
-                weights_i = weights[i]
-            else:
-                weights_i = weights[i].numpy()
-
-            if weights_i != 0:
+        for i in range(len(weights.numpy())):
+            if weights[i].numpy() != 0:
                 return self._ops[i](x) * weights[i]
 
 
-def gumbel_softmax(logits, temperature=1, hard=True, eps=1e-10):
-    #U = np.random.uniform(0, 1, logits.shape)
-    #U = - to_variable(
-    #    np.log(-np.log(U + eps) + eps).astype("float32"))
+def gumbel_softmax(logits, epoch, temperature=1.0, hard=True, eps=1e-10):
+    temperature = temperature * (0.98**epoch)
     U = np.random.gumbel(0, 1, logits.shape).astype("float32")
 
     logits = logits + to_variable(U)
@@ -105,12 +92,12 @@ def gumbel_softmax(logits, temperature=1, hard=True, eps=1e-10):
     if hard:
         maxes = fluid.layers.reduce_max(logits, dim=1, keep_dim=True)
         hard = fluid.layers.cast((logits == maxes), logits.dtype)
-        # out = hard - logits.detach() + logits
-        tmp = hard - logits
-        tmp.stop_gradient = True
-        out = tmp + logits
+        out = hard - logits.detach() + logits
+        # tmp.stop_gradient = True
+        # out = tmp + logits
     else:
         out = logits
+
     return out
 
 
@@ -142,8 +129,6 @@ class ReluConvBN(fluid.dygraph.Layer):
                  use_cudnn=True,
                  name=None):
         super(ReluConvBN, self).__init__()
-        #conv_std = (2.0 /
-        #            (filter_size[0] * filter_size[1] * out_c * in_c))**0.5
         conv_param = fluid.ParamAttr(
             name=name if name is None else (name + "_conv.weights"),
             initializer=fluid.initializer.MSRA())
@@ -215,6 +200,7 @@ class EncoderLayer(Layer):
     """
 
     def __init__(self,
+                 num_labels,
                  n_layer,
                  hidden_size=768,
                  name="encoder",
@@ -224,12 +210,27 @@ class EncoderLayer(Layer):
         super(EncoderLayer, self).__init__()
         self._n_layer = n_layer
         self._hidden_size = hidden_size
-        self._n_channel = 256
+        self._n_channel = 128
         self._steps = 3
         self._n_ops = len(ConvBN_PRIMITIVES)
         self.use_fixed_gumbel = use_fixed_gumbel
 
-        self.stem = fluid.dygraph.Sequential(
+        self.stem0 = fluid.dygraph.Sequential(
+            Conv2D(
+                num_channels=1,
+                num_filters=self._n_channel,
+                filter_size=[3, self._hidden_size],
+                padding=[1, 0],
+                param_attr=fluid.ParamAttr(initializer=MSRA()),
+                bias_attr=False),
+            BatchNorm(
+                num_channels=self._n_channel,
+                param_attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.Constant(value=1)),
+                bias_attr=fluid.ParamAttr(
+                    initializer=fluid.initializer.Constant(value=0))))
+
+        self.stem1 = fluid.dygraph.Sequential(
             Conv2D(
                 num_channels=1,
                 num_filters=self._n_channel,
@@ -262,16 +263,10 @@ class EncoderLayer(Layer):
             default_initializer=NormalInitializer(
                 loc=0.0, scale=1e-3))
 
-        # self.k = fluid.layers.create_parameter(
-        #     shape=[1, self._n_layer],
-        #     dtype="float32",
-        #     default_initializer=NormalInitializer(
-        #         loc=0.0, scale=1e-3))
         self.pool2d_avg = Pool2D(pool_type='avg', global_pooling=True)
         self.bns = []
         self.outs = []
         for i in range(self._n_layer):
-
             bn = BatchNorm(
                 num_channels=self._n_channel,
                 param_attr=fluid.ParamAttr(
@@ -280,52 +275,53 @@ class EncoderLayer(Layer):
                 bias_attr=fluid.ParamAttr(
                     initializer=fluid.initializer.Constant(value=0),
                     trainable=False))
-            self.bns.append(bn)
-
             out = Linear(
                 self._n_channel,
-                3,
+                num_labels,
                 param_attr=ParamAttr(initializer=MSRA()),
                 bias_attr=ParamAttr(initializer=MSRA()))
+            self.bns.append(bn)
             self.outs.append(out)
+        self._bns = fluid.dygraph.LayerList(self.bns)
+        self._outs = fluid.dygraph.LayerList(self.outs)
 
         self.use_fixed_gumbel = use_fixed_gumbel
-        self.gumbel_alphas = gumbel_softmax(self.alphas)
-        if gumbel_alphas is not None:
-            self.gumbel_alphas = np.array(gumbel_alphas).reshape(
-                self.alphas.shape)
-        else:
-            self.gumbel_alphas = gumbel_softmax(self.alphas)
+        #self.gumbel_alphas = gumbel_softmax(self.alphas, 0).detach()
+
+        mrpc_arch = [
+            [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],  # std_conv7 0     # node 0
+            [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],  # dil_conv5 1
+            [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],  # std_conv7 0     # node 1
+            [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],  # dil_conv5 1
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],  # zero 2
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],  # zero 0          # node2
+            [1, 0, 0, 0, 0, 0, 0, 0, 0, 0],  # std_conv3 1
+            [0, 0, 0, 0, 0, 0, 0, 0, 0, 1],  # zero 2
+            [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]  # dil_conv3 3
+        ]
+        self.gumbel_alphas = to_variable(
+            np.array(mrpc_arch).astype(np.float32))
         self.gumbel_alphas.stop_gradient = True
+        print("gumbel_alphas: \n", self.gumbel_alphas.numpy())
 
-        print("gumbel_alphas: {}".format(self.gumbel_alphas))
-
-    def forward(self, enc_input_0, enc_input_1, flops=[], model_size=[]):
+    def forward(self, enc_input_0, enc_input_1, epoch, flops=[],
+                model_size=[]):
         alphas = self.gumbel_alphas if self.use_fixed_gumbel else gumbel_softmax(
-            self.alphas)
-
-        s0 = fluid.layers.reshape(
-            enc_input_0, [-1, 1, enc_input_0.shape[1], enc_input_0.shape[2]])
-        s1 = fluid.layers.reshape(
-            enc_input_1, [-1, 1, enc_input_1.shape[1], enc_input_1.shape[2]])
-        # (bs, 1, seq_len, hidden_size)
+            self.alphas, epoch)
 
-        s0 = self.stem(s0)
-        s1 = self.stem(s1)
-        # (bs, n_channel, seq_len, 1)
-        if self.use_fixed_gumbel:
-            alphas = self.gumbel_alphas
-        else:
-            alphas = gumbel_softmax(self.alphas)
+        s0 = fluid.layers.unsqueeze(enc_input_0, [1])
+        s1 = fluid.layers.unsqueeze(enc_input_1, [1])
+        s0 = self.stem0(s0)
+        s1 = self.stem1(s1)
 
-        s0 = s1 = tmp
-        outputs = []
+        enc_outputs = []
         for i in range(self._n_layer):
             s0, s1 = s1, self._cells[i](s0, s1, alphas)
-            tmp = self.bns[i](s1)
-            tmp = self.pool2d_avg(tmp)
             # (bs, n_channel, seq_len, 1)
+            tmp = self._bns[i](s1)
+            tmp = self.pool2d_avg(tmp)
             tmp = fluid.layers.reshape(tmp, shape=[-1, 0])
-            tmp = self.outs[i](tmp)
-            outputs.append(tmp)
-        return outputs
+            tmp = self._outs[i](tmp)
+            enc_outputs.append(tmp)
+
+        return enc_outputs

paddleslim/teachers/bert/cls.py

@@ -58,7 +58,8 @@ class BERTClassifier(Layer):
                  num_labels,
                  task_name="mnli",
                  model_path=None,
-                 use_cuda=True):
+                 use_cuda=True,
+                 return_pooled_out=True):
         super(BERTClassifier, self).__init__()
         self.task_name = task_name.lower()
         BERT_BASE_PATH = "./data/pretrained_models/uncased_L-12_H-768_A-12/"
@@ -84,7 +85,7 @@ class BERTClassifier(Layer):
         }
 
         self.cls_model = ClsModelLayer(
-            self.bert_config, num_labels, return_pooled_out=True)
+            self.bert_config, num_labels, return_pooled_out=return_pooled_out)
 
         if model_path is not None:
             #restore the model

paddleslim/teachers/bert/model/cls.py

@@ -46,6 +46,7 @@ class ClsModelLayer(Layer):
         self.use_fp16 = use_fp16
         self.loss_scaling = loss_scaling
         self.n_layers = config['num_hidden_layers']
+        self.return_pooled_out = return_pooled_out
 
         self.bert_layer = BertModelLayer(
             config=self.config, return_pooled_out=True, use_fp16=self.use_fp16)
@@ -79,11 +80,23 @@ class ClsModelLayer(Layer):
 
         enc_outputs, next_sent_feats = self.bert_layer(
             src_ids, position_ids, sentence_ids, input_mask)
+
+        if not self.return_pooled_out:
+            cls_feat = fluid.layers.dropout(
+                x=next_sent_feats[-1],
+                dropout_prob=0.1,
+                dropout_implementation="upscale_in_train")
+            logits = self.cls_fc[-1](cls_feat)
+            probs = fluid.layers.softmax(logits)
+            num_seqs = fluid.layers.create_tensor(dtype='int64')
+            accuracy = fluid.layers.accuracy(
+                input=probs, label=labels, total=num_seqs)
+            return enc_outputs, logits, accuracy, num_seqs
+
         logits = []
         losses = []
         accuracys = []
         for next_sent_feat, fc in zip(next_sent_feats, self.cls_fc):
-
             cls_feat = fluid.layers.dropout(
                 x=next_sent_feat,
                 dropout_prob=0.1,

paddleslim/teachers/bert/reader/cls.py

@@ -16,6 +16,7 @@ import io
 import os
 import types
 import csv
+import random
 import numpy as np
 from . import tokenization
 from .batching import prepare_batch_data
@@ -139,6 +140,8 @@ class DataProcessor(object):
           epoch: int. Total epoches to generate data.
           shuffle: bool. Whether to shuffle examples.
         """
+        search_examples = self.get_train_examples(self.data_dir)
+        random.shuffle(search_examples)
         if phase == 'train':
             examples = self.get_train_examples(self.data_dir)
             self.num_examples['train'] = len(examples)
@@ -152,13 +155,13 @@ class DataProcessor(object):
             examples = self.get_test_examples(self.data_dir)
             self.num_examples['test'] = len(examples)
         elif phase == 'search_train':
-            examples = self.get_train_examples(self.data_dir)
-            self.num_examples['search_train'] = len(examples) / 2
-            examples = examples[:self.num_examples['search_train']]
+            #examples = self.get_train_examples(self.data_dir)
+            self.num_examples['search_train'] = len(search_examples) / 2
+            examples = search_examples[:self.num_examples['search_train']]
         elif phase == 'search_valid':
-            examples = self.get_train_examples(self.data_dir)
-            self.num_examples['search_valid'] = len(examples) / 2
-            examples = examples[self.num_examples['search_train']:]
+            #examples = self.get_train_examples(self.data_dir)
+            self.num_examples['search_valid'] = len(search_examples) / 2
+            examples = search_examples[self.num_examples['search_valid']:]
         else:
             raise ValueError(
                 "Unknown phase, which should be in ['train', 'dev', 'test'].")
@@ -213,16 +216,53 @@ class DataProcessor(object):
                     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:
+                        batch = self.split_seq_pair(batch)
                         yield batch
                     all_dev_batches = []
 
         return wrapper
 
+    def split_seq_pair(self, data_ids):
+        src_ids = data_ids[0]
+        sentence_ids = data_ids[2]
+
+        ids = np.squeeze(src_ids)
+        sids = np.squeeze(sentence_ids)
+        batchsize = ids.shape[0]
+
+        ids_0 = ids[((sids == 0) & (ids != 0))]
+        seqlen_0 = ((sids == 0) & (ids != 0)).astype(np.int64).sum(1)
+        y_0 = np.concatenate([np.arange(s) for s in seqlen_0])
+        x_0 = np.concatenate([
+            np.ones(
+                [s], dtype=np.int64) * i for i, s in enumerate(seqlen_0)
+        ])
+        ids0 = np.zeros([batchsize, seqlen_0.max()], dtype=np.int64)
+        ids0[(x_0, y_0)] = ids_0
+
+        ids_1 = ids[(sids == 1) & (ids != 0)]
+        seqlen_1 = ((sids == 1) & (ids != 0)).astype(np.int64).sum(1)
+        y_1 = np.concatenate([np.arange(s) for s in seqlen_1])
+        x_1 = np.concatenate([
+            np.ones(
+                [s], dtype=np.int64) * i for i, s in enumerate(seqlen_1)
+        ])
+        ids1 = np.zeros([batchsize, seqlen_1.max()], dtype=np.int64)
+        ids1[(x_1, y_1)] = ids_1
+
+        msl = max(seqlen_0.max(), seqlen_1.max())
+        ids0 = np.pad(ids0, [[0, 0], [0, msl - seqlen_0.max()]],
+                      mode='constant')
+        ids1 = np.pad(ids1, [[0, 0], [0, msl - seqlen_1.max()]],
+                      mode='constant')
+        return data_ids + [ids0, ids1]
+
 
 class InputExample(object):
     """A single training/test example for simple sequence classification."""