Update seq2seq to adapt to latest code.

seq2seq/predict.py → examples/seq2seq/predict.py

@@ -15,8 +15,6 @@
 import logging
 import os
 import io
-import sys
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 import random
 from functools import partial
 
@@ -25,10 +23,10 @@ import paddle.fluid as fluid
 from paddle.fluid.layers.utils import flatten
 from paddle.fluid.io import DataLoader
 
-from model import Input, set_device
+from hapi.model import Input, set_device
 from args import parse_args
 from seq2seq_base import BaseInferModel
-from seq2seq_attn import AttentionInferModel, AttentionGreedyInferModel
+from seq2seq_attn import AttentionInferModel
 from reader import Seq2SeqDataset, Seq2SeqBatchSampler, SortType, prepare_infer_input
 
 
@@ -87,8 +85,7 @@ def do_predict(args):
         num_workers=0,
         return_list=True)
 
-    # model_maker = AttentionInferModel if args.attention else BaseInferModel
-    model_maker = AttentionGreedyInferModel if args.attention else BaseInferModel
+    model_maker = AttentionInferModel if args.attention else BaseInferModel
     model = model_maker(
         args.src_vocab_size,
         args.tar_vocab_size,

seq2seq/seq2seq_attn.py → examples/seq2seq/seq2seq_attn.py

@@ -19,8 +19,9 @@ from paddle.fluid.initializer import UniformInitializer
 from paddle.fluid.dygraph import Embedding, Linear, Layer
 from paddle.fluid.layers import BeamSearchDecoder
 
-from text import DynamicDecode, RNN, BasicLSTMCell, RNNCell
-from model import Model, Loss
+from hapi.model import Model, Loss
+from hapi.text import DynamicDecode, RNN, BasicLSTMCell, RNNCell
+
 from seq2seq_base import Encoder
 
 
@@ -238,92 +239,3 @@ class AttentionInferModel(AttentionModel):
             encoder_output=encoder_output,
             encoder_padding_mask=encoder_padding_mask)
         return rs
-
-
-class GreedyEmbeddingHelper(fluid.layers.GreedyEmbeddingHelper):
-    def __init__(self, embedding_fn, start_tokens, end_token):
-        if isinstance(start_tokens, int):
-            self.need_convert_start_tokens = True
-            self.start_token_value = start_tokens
-        super(GreedyEmbeddingHelper, self).__init__(embedding_fn, start_tokens,
-                                                    end_token)
-        self.end_token = fluid.layers.create_global_var(
-            shape=[1], dtype="int64", value=end_token, persistable=True)
-
-    def initialize(self, batch_ref=None):
-        if getattr(self, "need_convert_start_tokens", False):
-            assert batch_ref is not None, (
-                "Need to give batch_ref to get batch size "
-                "to initialize the tensor for start tokens.")
-            self.start_tokens = fluid.layers.fill_constant_batch_size_like(
-                input=fluid.layers.utils.flatten(batch_ref)[0],
-                shape=[-1],
-                dtype="int64",
-                value=self.start_token_value,
-                input_dim_idx=0)
-        return super(GreedyEmbeddingHelper, self).initialize()
-
-
-class BasicDecoder(fluid.layers.BasicDecoder):
-    def initialize(self, initial_cell_states):
-        (initial_inputs,
-         initial_finished) = self.helper.initialize(initial_cell_states)
-        return initial_inputs, initial_cell_states, initial_finished
-
-
-class AttentionGreedyInferModel(AttentionModel):
-    def __init__(self,
-                 src_vocab_size,
-                 trg_vocab_size,
-                 embed_dim,
-                 hidden_size,
-                 num_layers,
-                 dropout_prob=0.,
-                 bos_id=0,
-                 eos_id=1,
-                 beam_size=1,
-                 max_out_len=256):
-        args = dict(locals())
-        args.pop("self")
-        args.pop("__class__", None)  # py3
-        args.pop("beam_size", None)
-        self.bos_id = args.pop("bos_id")
-        self.eos_id = args.pop("eos_id")
-        self.max_out_len = args.pop("max_out_len")
-        super(AttentionGreedyInferModel, self).__init__(**args)
-        # dynamic decoder for inference
-        decoder_helper = GreedyEmbeddingHelper(
-            start_tokens=bos_id,
-            end_token=eos_id,
-            embedding_fn=self.decoder.embedder)
-        decoder = BasicDecoder(
-            cell=self.decoder.lstm_attention.cell,
-            helper=decoder_helper,
-            output_fn=self.decoder.output_layer)
-        self.greedy_search_decoder = DynamicDecode(
-            decoder, max_step_num=max_out_len, is_test=True)
-
-    def forward(self, src, src_length):
-        # encoding
-        encoder_output, encoder_final_state = self.encoder(src, src_length)
-
-        # decoder initial states
-        decoder_initial_states = [
-            encoder_final_state,
-            self.decoder.lstm_attention.cell.get_initial_states(
-                batch_ref=encoder_output, shape=[self.hidden_size])
-        ]
-        # attention mask to avoid paying attention on padddings
-        src_mask = layers.sequence_mask(
-            src_length,
-            maxlen=layers.shape(src)[1],
-            dtype=encoder_output.dtype)
-        encoder_padding_mask = (src_mask - 1.0) * 1e9
-        encoder_padding_mask = layers.unsqueeze(encoder_padding_mask, [1])
-
-        # dynamic decoding with greedy search
-        rs, _ = self.greedy_search_decoder(
-            inits=decoder_initial_states,
-            encoder_output=encoder_output,
-            encoder_padding_mask=encoder_padding_mask)
-        return rs.sample_ids

seq2seq/seq2seq_base.py → examples/seq2seq/seq2seq_base.py

@@ -18,8 +18,9 @@ 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 text import DynamicDecode, RNN, BasicLSTMCell, RNNCell
-from model import Model, Loss
+
+from hapi.model import Model, Loss
+from hapi.text import DynamicDecode, RNN, BasicLSTMCell, RNNCell
 
 
 class CrossEntropyCriterion(Loss):
@@ -200,44 +201,3 @@ class BaseInferModel(BaseModel):
         # dynamic decoding with beam search
         rs, _ = self.beam_search_decoder(inits=encoder_final_states)
         return rs
-
-
-class BaseGreedyInferModel(BaseModel):
-    def __init__(self,
-                 src_vocab_size,
-                 trg_vocab_size,
-                 embed_dim,
-                 hidden_size,
-                 num_layers,
-                 dropout_prob=0.,
-                 bos_id=0,
-                 eos_id=1,
-                 beam_size=1,
-                 max_out_len=256):
-        args = dict(locals())
-        args.pop("self")
-        args.pop("__class__", None)  # py3
-        args.pop("beam_size", None)
-        self.bos_id = args.pop("bos_id")
-        self.eos_id = args.pop("eos_id")
-        self.max_out_len = args.pop("max_out_len")
-        super(BaseGreedyInferModel, self).__init__(**args)
-        # dynamic decoder for inference
-        decoder_helper = GreedyEmbeddingHelper(
-            start_tokens=bos_id,
-            end_token=eos_id,
-            embedding_fn=self.decoder.embedder)
-        decoder = BasicDecoder(
-            cell=self.decoder.stack_lstm.cell,
-            helper=decoder_helper,
-            output_fn=self.decoder.output_layer)
-        self.greedy_search_decoder = DynamicDecode(
-            decoder, max_step_num=max_out_len, is_test=True)
-
-    def forward(self, src, src_length):
-        # encoding
-        encoder_output, encoder_final_states = self.encoder(src, src_length)
-
-        # dynamic decoding with greedy search
-        rs, _ = self.greedy_search_decoder(inits=encoder_final_states)
-        return rs.sample_ids

seq2seq/train.py → examples/seq2seq/train.py

@@ -14,8 +14,6 @@
 
 import logging
 import os
-import sys
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 import random
 from functools import partial
 
@@ -23,9 +21,7 @@ import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.io import DataLoader
 
-from model import Input, set_device
-from metrics import Metric
-from callbacks import ProgBarLogger
+from hapi.model import Input, set_device
 from args import parse_args
 from seq2seq_base import BaseModel, CrossEntropyCriterion
 from seq2seq_attn import AttentionModel

seq2seq/utility.py → examples/seq2seq/utility.py

@@ -15,8 +15,8 @@
 import numpy as np
 import paddle.fluid as fluid
 
-from metrics import Metric
-from callbacks import ProgBarLogger
+from hapi.metrics import Metric
+from hapi.callbacks import ProgBarLogger
 
 
 class TrainCallback(ProgBarLogger):

hapi/text/text.py

@@ -238,8 +238,9 @@ class BasicLSTMCell(RNNCell):
         self._bias_attr = bias_attr
         self._gate_activation = gate_activation or layers.sigmoid
         self._activation = activation or layers.tanh
-        self._forget_bias = layers.fill_constant(
-            [1], dtype=dtype, value=forget_bias)
+        # TODO(guosheng): find better way to resolve constants in __init__
+        self._forget_bias = layers.create_global_var(
+            shape=[1], dtype=dtype, value=forget_bias, persistable=True)
         self._forget_bias.stop_gradient = False
         self._dtype = dtype
         self._input_size = input_size