Update Transformer, seq2seq, sequence_tagging to adapt to 2.0-beta hapi apis.

bert/readme.md

+**仍在开发中，待完成**
+
 1. download data: wget https://paddle-hapi.bj.bcebos.com/data/bert_data.tar.gz
 
 2. unzip data: tar -zvxf bert_data.tar.gz

bert_leveldb/readme.md

+**仍在开发中，待完成**
+
 0. python3.7 -m pip install leveldb
 
 1. download data: wget https://paddle-hapi.bj.bcebos.com/data/bert_data.tar.gz

sentiment_classification/README.md

-## 简介
+**仍在开发中，待完成**
 
+## 简介
 
 情感是人类的一种高级智能行为，为了识别文本的情感倾向，需要深入的语义建模。另外，不同领域（如餐饮、体育）在情感的表达各不相同，因而需要有大规模覆盖各个领域的数据进行模型训练。为此，我们通过基于深度学习的语义模型和大规模数据挖掘解决上述两个问题。效果上，我们基于开源情感倾向分类数据集ChnSentiCorp进行评测。具体数据如下所示：
 

seq2seq/predict.py

@@ -19,11 +19,12 @@ import random
 from functools import partial
 
 import numpy as np
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layers.utils import flatten
 from paddle.fluid.io import DataLoader
+from paddle.static import InputSpec as Input
 
-from paddle.incubate.hapi.model import Input, set_device
 from args import parse_args
 from seq2seq_base import BaseInferModel
 from seq2seq_attn import AttentionInferModel
@@ -48,7 +49,7 @@ def post_process_seq(seq, bos_idx, eos_idx, output_bos=False,
 
 
 def do_predict(args):
-    device = set_device("gpu" if args.use_gpu else "cpu")
+    device = paddle.set_device("gpu" if args.use_gpu else "cpu")
     fluid.enable_dygraph(device) if args.eager_run else None
 
     # define model
@@ -84,7 +85,8 @@ def do_predict(args):
         return_list=True)
 
     model_maker = AttentionInferModel if args.attention else BaseInferModel
-    model = model_maker(
+    model = paddle.Model(
+        model_maker(
             args.src_vocab_size,
             args.tar_vocab_size,
             args.hidden_size,
@@ -94,9 +96,10 @@ def do_predict(args):
             bos_id=bos_id,
             eos_id=eos_id,
             beam_size=args.beam_size,
-        max_out_len=256)
+            max_out_len=256),
+        inputs=inputs)
 
-    model.prepare(inputs=inputs, device=device)
+    model.prepare()
 
     # load the trained model
     assert args.reload_model, (

seq2seq/seq2seq_attn.py

@@ -18,10 +18,7 @@ from paddle.fluid import ParamAttr
 from paddle.fluid.initializer import UniformInitializer
 from paddle.fluid.dygraph import Embedding, Linear, Layer
 from paddle.fluid.layers import BeamSearchDecoder
-
-from paddle.incubate.hapi.model import Model
-from paddle.incubate.hapi.loss import Loss
-from paddle.incubate.hapi.text import DynamicDecode, RNN, BasicLSTMCell, RNNCell
+from paddle.text import DynamicDecode, RNN, BasicLSTMCell, RNNCell
 
 from seq2seq_base import Encoder
 
@@ -138,7 +135,7 @@ class Decoder(Layer):
         return predict
 
 
-class AttentionModel(Model):
+class AttentionModel(Layer):
     def __init__(self,
                  src_vocab_size,
                  trg_vocab_size,

seq2seq/seq2seq_base.py

@@ -18,18 +18,14 @@ from paddle.fluid import ParamAttr
 from paddle.fluid.initializer import UniformInitializer
 from paddle.fluid.dygraph import Embedding, Linear, Layer
 from paddle.fluid.layers import BeamSearchDecoder
+from paddle.text import DynamicDecode, RNN, BasicLSTMCell, RNNCell
 
-from paddle.incubate.hapi.model import Model
-from paddle.incubate.hapi.loss import Loss
-from paddle.incubate.hapi.text import DynamicDecode, RNN, BasicLSTMCell, RNNCell
 
-
-class CrossEntropyCriterion(Loss):
+class CrossEntropyCriterion(Layer):
     def __init__(self):
         super(CrossEntropyCriterion, self).__init__()
 
-    def forward(self, outputs, labels):
-        predict, (trg_length, label) = outputs[0], labels
+    def forward(self, predict, trg_length, label):
         # for target padding mask
         mask = layers.sequence_mask(
             trg_length, maxlen=layers.shape(predict)[1], dtype=predict.dtype)
@@ -140,7 +136,7 @@ class Decoder(Layer):
         return predict
 
 
-class BaseModel(Model):
+class BaseModel(Layer):
     def __init__(self,
                  src_vocab_size,
                  trg_vocab_size,

seq2seq/train.py

@@ -15,14 +15,15 @@
 import logging
 import os
 import random
+from args import parse_args
 from functools import partial
 
 import numpy as np
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.io import DataLoader
+from paddle.static import InputSpec as Input
 
-from paddle.incubate.hapi.model import Input, set_device
-from args import parse_args
 from seq2seq_base import BaseModel, CrossEntropyCriterion
 from seq2seq_attn import AttentionModel
 from reader import create_data_loader
@@ -30,7 +31,7 @@ from utility import PPL, TrainCallback, get_model_cls
 
 
 def do_train(args):
-    device = set_device("gpu" if args.use_gpu else "cpu")
+    device = paddle.set_device("gpu" if args.use_gpu else "cpu")
     fluid.enable_dygraph(device) if args.eager_run else None
 
     if args.enable_ce:
@@ -58,9 +59,11 @@ def do_train(args):
 
     model_maker = get_model_cls(
         AttentionModel) if args.attention else get_model_cls(BaseModel)
-    model = model_maker(args.src_vocab_size, args.tar_vocab_size,
-                        args.hidden_size, args.hidden_size, args.num_layers,
-                        args.dropout)
+    model = paddle.Model(
+        model_maker(args.src_vocab_size, args.tar_vocab_size, args.hidden_size,
+                    args.hidden_size, args.num_layers, args.dropout),
+        inputs=inputs,
+        labels=labels)
     grad_clip = fluid.clip.GradientClipByGlobalNorm(
         clip_norm=args.max_grad_norm)
     optimizer = fluid.optimizer.Adam(
@@ -69,13 +72,7 @@ def do_train(args):
         grad_clip=grad_clip)
 
     ppl_metric = PPL(reset_freq=100)  # ppl for every 100 batches
-    model.prepare(
-        optimizer,
-        CrossEntropyCriterion(),
-        ppl_metric,
-        inputs=inputs,
-        labels=labels,
-        device=device)
+    model.prepare(optimizer, CrossEntropyCriterion(), ppl_metric)
     model.fit(train_data=train_loader,
               eval_data=eval_loader,
               epochs=args.max_epoch,

seq2seq/utility.py

@@ -15,14 +15,13 @@
 import math
 import functools
 
+import paddle
 import paddle.fluid as fluid
+from paddle.metric import Metric
+from paddle.text import BasicLSTMCell
 
-from paddle.incubate.hapi.metrics import Metric
-from paddle.incubate.hapi.callbacks import ProgBarLogger
-from paddle.incubate.hapi.text import BasicLSTMCell
 
-
-class TrainCallback(ProgBarLogger):
+class TrainCallback(paddle.callbacks.ProgBarLogger):
     def __init__(self, ppl, log_freq, verbose=2):
         super(TrainCallback, self).__init__(log_freq, verbose)
         self.ppl = ppl
@@ -58,7 +57,7 @@ class PPL(Metric):
         self.reset_freq = reset_freq
         self.reset()
 
-    def add_metric_op(self, pred, seq_length, label):
+    def compute(self, pred, seq_length, label):
         word_num = fluid.layers.reduce_sum(seq_length)
         return word_num
 

sequence_tagging/eval.py

@@ -18,9 +18,10 @@ SequenceTagging eval structure
 from __future__ import division
 from __future__ import print_function
 
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layers.utils import flatten
-from paddle.incubate.hapi.model import Input, set_device
+from paddle.static import InputSpec as Input
 
 from sequence_tagging import SeqTagging, LacLoss, ChunkEval
 from reader import LacDataset, LacDataLoader
@@ -29,7 +30,7 @@ from utils.configure import PDConfig
 
 
 def main(args):
-    place = set_device(args.device)
+    place = paddle.set_device(args.device)
     fluid.enable_dygraph(place) if args.dynamic else None
 
     inputs = [
@@ -45,14 +46,14 @@ def main(args):
 
     vocab_size = dataset.vocab_size
     num_labels = dataset.num_labels
-    model = SeqTagging(args, vocab_size, num_labels, mode="test")
+    model = paddle.Model(
+        SeqTagging(
+            args, vocab_size, num_labels, mode="test"),
+        inputs=inputs,
+        labels=labels)
 
     model.mode = "test"
-    model.prepare(
-        metrics=ChunkEval(num_labels),
-        inputs=inputs,
-        labels=labels,
-        device=place)
+    model.prepare(metrics=ChunkEval(num_labels))
     model.load(args.init_from_checkpoint, skip_mismatch=True)
 
     eval_result = model.evaluate(

sequence_tagging/predict.py

@@ -20,9 +20,10 @@ from __future__ import print_function
 
 import six
 
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layers.utils import flatten
-from paddle.incubate.hapi.model import Input, set_device
+from paddle.static import InputSpec as Input
 
 from sequence_tagging import SeqTagging, LacLoss, ChunkEval
 from reader import LacDataset, LacDataLoader
@@ -31,7 +32,7 @@ from utils.configure import PDConfig
 
 
 def main(args):
-    place = set_device(args.device)
+    place = paddle.set_device(args.device)
     fluid.enable_dygraph(place) if args.dynamic else None
 
     inputs = [
@@ -46,10 +47,13 @@ def main(args):
 
     vocab_size = dataset.vocab_size
     num_labels = dataset.num_labels
-    model = SeqTagging(args, vocab_size, num_labels, mode="predict")
+    model = paddle.Model(
+        SeqTagging(
+            args, vocab_size, num_labels, mode="predict"),
+        inputs=inputs)
 
     model.mode = "test"
-    model.prepare(inputs=inputs)
+    model.prepare()
 
     model.load(args.init_from_checkpoint, skip_mismatch=True)
 

sequence_tagging/sequence_tagging.py

@@ -25,17 +25,16 @@ import math
 import argparse
 import numpy as np
 
+import paddle
 import paddle.fluid as fluid
-from paddle.incubate.hapi.metrics import Metric
-from paddle.incubate.hapi.model import Model
-from paddle.incubate.hapi.loss import Loss
-from paddle.incubate.hapi.text import SequenceTagging
+from paddle.metric import Metric
+from paddle.text import SequenceTagging
 
 from utils.check import check_gpu, check_version
 from utils.configure import PDConfig
 
 
-class SeqTagging(Model):
+class SeqTagging(fluid.dygraph.Layer):
     def __init__(self, args, vocab_size, num_labels, length=None,
                  mode="train"):
         super(SeqTagging, self).__init__()
@@ -131,13 +130,13 @@ class Chunk_eval(fluid.dygraph.Layer):
         return (num_infer_chunks, num_label_chunks, num_correct_chunks)
 
 
-class LacLoss(Loss):
+class LacLoss(fluid.dygraph.Layer):
     def __init__(self):
         super(LacLoss, self).__init__()
         pass
 
-    def forward(self, outputs, labels):
-        avg_cost = outputs[1]
+    def forward(self, *args):
+        avg_cost = args[1]
         return avg_cost
 
 
@@ -149,7 +148,7 @@ class ChunkEval(Metric):
             int(math.ceil((num_labels - 1) / 2.0)), "IOB")
         self.reset()
 
-    def add_metric_op(self, *args):
+    def compute(self, *args):
         crf_decode = args[0]
         lengths = args[2]
         label = args[3]

sequence_tagging/train.py

@@ -18,9 +18,10 @@ SequenceTagging network structure
 from __future__ import division
 from __future__ import print_function
 
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.optimizer import AdamOptimizer
-from paddle.incubate.hapi.model import Input, set_device
+from paddle.static import InputSpec as Input
 
 from sequence_tagging import SeqTagging, LacLoss, ChunkEval
 from reader import LacDataset, LacDataLoader
@@ -29,7 +30,7 @@ from utils.configure import PDConfig
 
 
 def main(args):
-    place = set_device(args.device)
+    place = paddle.set_device(args.device)
     fluid.enable_dygraph(place) if args.dynamic else None
 
     inputs = [
@@ -48,19 +49,17 @@ def main(args):
 
     vocab_size = dataset.vocab_size
     num_labels = dataset.num_labels
-    model = SeqTagging(args, vocab_size, num_labels, mode="train")
+    model = paddle.Model(
+        SeqTagging(
+            args, vocab_size, num_labels, mode="train"),
+        inputs=inputs,
+        labels=labels)
 
     optim = AdamOptimizer(
         learning_rate=args.base_learning_rate,
         parameter_list=model.parameters())
 
-    model.prepare(
-        optim,
-        LacLoss(),
-        ChunkEval(num_labels),
-        inputs=inputs,
-        labels=labels,
-        device=args.device)
+    model.prepare(optim, LacLoss(), ChunkEval(num_labels))
 
     if args.init_from_checkpoint:
         model.load(args.init_from_checkpoint)

transformer/predict.py

@@ -21,11 +21,11 @@ import paddle
 import paddle.fluid as fluid
 from paddle.io import DataLoader
 from paddle.fluid.layers.utils import flatten
+from paddle.static import InputSpec as Input
 
 from utils.configure import PDConfig
 from utils.check import check_gpu, check_version
 
-from paddle.incubate.hapi.model import Input, set_device
 from reader import prepare_infer_input, Seq2SeqDataset, Seq2SeqBatchSampler
 from transformer import InferTransformer
 
@@ -48,7 +48,7 @@ def post_process_seq(seq, bos_idx, eos_idx, output_bos=False,
 
 
 def do_predict(args):
-    device = set_device("gpu" if args.use_cuda else "cpu")
+    device = paddle.set_device("gpu" if args.use_cuda else "cpu")
     fluid.enable_dygraph(device) if args.eager_run else None
 
     inputs = [
@@ -99,7 +99,8 @@ def do_predict(args):
         return_list=True)
 
     # define model
-    transformer = InferTransformer(
+    model = paddle.Model(
+        InferTransformer(
             args.src_vocab_size,
             args.trg_vocab_size,
             args.max_length + 1,
@@ -118,18 +119,19 @@ def do_predict(args):
             args.bos_idx,
             args.eos_idx,
             beam_size=args.beam_size,
-        max_out_len=args.max_out_len)
-    transformer.prepare(inputs=inputs, device=device)
+            max_out_len=args.max_out_len),
+        inputs)
+    model.prepare()
 
     # load the trained model
     assert args.init_from_params, (
         "Please set init_from_params to load the infer model.")
-    transformer.load(args.init_from_params)
+    model.load(args.init_from_params)
 
     # TODO: use model.predict when support variant length
     f = open(args.output_file, "wb")
     for data in data_loader():
-        finished_seq = transformer.test_batch(inputs=flatten(data))[0]
+        finished_seq = model.test_batch(inputs=flatten(data))[0]
         finished_seq = np.transpose(finished_seq, [0, 2, 1])
         for ins in finished_seq:
             for beam_idx, beam in enumerate(ins):

transformer/train.py

@@ -19,17 +19,16 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.io import DataLoader
+from paddle.static import InputSpec as Input
 
 from utils.configure import PDConfig
 from utils.check import check_gpu, check_version
 
-from paddle.incubate.hapi.model import Input, set_device
-from paddle.incubate.hapi.callbacks import ProgBarLogger
 from reader import create_data_loader
 from transformer import Transformer, CrossEntropyCriterion
 
 
-class TrainCallback(ProgBarLogger):
+class TrainCallback(paddle.callbacks.ProgBarLogger):
     def __init__(self,
                  args,
                  verbose=2,
@@ -75,7 +74,7 @@ class TrainCallback(ProgBarLogger):
 
 
 def do_train(args):
-    device = set_device("gpu" if args.use_cuda else "cpu")
+    device = paddle.set_device("gpu" if args.use_cuda else "cpu")
     fluid.enable_dygraph(device) if args.eager_run else None
 
     # set seed for CE
@@ -119,14 +118,16 @@ def do_train(args):
         eval_loader, eval_steps_fn) = create_data_loader(args, device)
 
     # define model
-    transformer = Transformer(
-        args.src_vocab_size, args.trg_vocab_size, args.max_length + 1,
-        args.n_layer, args.n_head, args.d_key, args.d_value, args.d_model,
-        args.d_inner_hid, args.prepostprocess_dropout, args.attention_dropout,
-        args.relu_dropout, args.preprocess_cmd, args.postprocess_cmd,
-        args.weight_sharing, args.bos_idx, args.eos_idx)
-
-    transformer.prepare(
+    model = paddle.Model(
+        Transformer(args.src_vocab_size, args.trg_vocab_size,
+                    args.max_length + 1, args.n_layer, args.n_head, args.d_key,
+                    args.d_value, args.d_model, args.d_inner_hid,
+                    args.prepostprocess_dropout, args.attention_dropout,
+                    args.relu_dropout, args.preprocess_cmd,
+                    args.postprocess_cmd, args.weight_sharing, args.bos_idx,
+                    args.eos_idx), inputs, labels)
+
+    model.prepare(
         fluid.optimizer.Adam(
             learning_rate=fluid.layers.noam_decay(
                 args.d_model,
@@ -135,21 +136,18 @@ def do_train(args):
             beta1=args.beta1,
             beta2=args.beta2,
             epsilon=float(args.eps),
-            parameter_list=transformer.parameters()),
-        CrossEntropyCriterion(args.label_smooth_eps),
-        inputs=inputs,
-        labels=labels,
-        device=device)
+            parameter_list=model.parameters()),
+        CrossEntropyCriterion(args.label_smooth_eps))
 
     ## init from some checkpoint, to resume the previous training
     if args.init_from_checkpoint:
-        transformer.load(args.init_from_checkpoint)
+        model.load(args.init_from_checkpoint)
     ## init from some pretrain models, to better solve the current task
     if args.init_from_pretrain_model:
-        transformer.load(args.init_from_pretrain_model, reset_optimizer=True)
+        model.load(args.init_from_pretrain_model, reset_optimizer=True)
 
     # model train
-    transformer.fit(train_data=train_loader,
+    model.fit(train_data=train_loader,
               eval_data=eval_loader,
               epochs=args.epoch,
               eval_freq=1,

transformer/transformer.py

@@ -19,9 +19,7 @@ import numpy as np
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from paddle.fluid.dygraph import Embedding, LayerNorm, Linear, Layer
-from paddle.incubate.hapi.model import Model
-from paddle.incubate.hapi.loss import Loss
-from paddle.incubate.hapi.text import TransformerBeamSearchDecoder, DynamicDecode
+from paddle.text import TransformerBeamSearchDecoder, DynamicDecode
 
 
 def position_encoding_init(n_position, d_pos_vec):
@@ -498,13 +496,12 @@ class WrapDecoder(Layer):
         return logits
 
 
-class CrossEntropyCriterion(Loss):
+class CrossEntropyCriterion(Layer):
     def __init__(self, label_smooth_eps):
         super(CrossEntropyCriterion, self).__init__()
         self.label_smooth_eps = label_smooth_eps
 
-    def forward(self, outputs, labels):
-        predict, (label, weights) = outputs[0], labels
+    def forward(self, predict, label, weights):
         if self.label_smooth_eps:
             label = layers.label_smooth(
                 label=layers.one_hot(
@@ -523,7 +520,7 @@ class CrossEntropyCriterion(Loss):
         return avg_cost
 
 
-class Transformer(Model):
+class Transformer(Layer):
     """
     model
     """