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models
“70141a73c4cd5f3855a72c6488a6cac288bda36f”上不存在“paddle/legacy/gserver/layers/Layer.cpp”
提交 31186c41 编写于 作者: H hong 提交者： Aurelius84
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update transformer support remove build once (#4115) 

* update transformer support remove build once; test=develop

* fix optimizer; test=develop
上级 e4047478
隐藏空白更改
内联 并排
Showing with 86 addition and 97 deletion
dygraph/transformer/model.py
浏览文件 @ 31186c41
...@@ -18,7 +18,7 @@ import numpy as np ...@@ -18,7 +18,7 @@ import numpy as np
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle.fluid.layers as layers import paddle.fluid.layers as layers
from paddle.fluid.dygraph import Embedding, LayerNorm, FC, to_variable, Layer, guard from paddle.fluid.dygraph import Embedding, LayerNorm, Linear, to_variable, Layer, guard
from paddle.fluid.dygraph.learning_rate_scheduler import LearningRateDecay from paddle.fluid.dygraph.learning_rate_scheduler import LearningRateDecay
from config import word_emb_param_names, pos_enc_param_names from config import word_emb_param_names, pos_enc_param_names
...@@ -71,13 +71,12 @@ class PrePostProcessLayer(Layer): ...@@ -71,13 +71,12 @@ class PrePostProcessLayer(Layer):
""" """
PrePostProcessLayer PrePostProcessLayer
""" """
def __init__(self, name_scope, process_cmd, shape_len=None): def __init__(self, process_cmd, normalized_shape=None):
super(PrePostProcessLayer, self).__init__(name_scope) super(PrePostProcessLayer, self).__init__()
for cmd in process_cmd: for cmd in process_cmd:
if cmd == "n": if cmd == "n":
self._layer_norm = LayerNorm( self._layer_norm = LayerNorm(
name_scope=self.full_name(), normalized_shape = normalized_shape,
begin_norm_axis=shape_len - 1,
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(1.)), initializer=fluid.initializer.Constant(1.)),
bias_attr=fluid.ParamAttr( bias_attr=fluid.ParamAttr(
...@@ -109,15 +108,13 @@ class PositionwiseFeedForwardLayer(Layer): ...@@ -109,15 +108,13 @@ class PositionwiseFeedForwardLayer(Layer):
""" """
PositionwiseFeedForwardLayer PositionwiseFeedForwardLayer
""" """
def __init__(self, name_scope, d_inner_hid, d_hid, dropout_rate): def __init__(self, input_hid, d_inner_hid, d_hid, dropout_rate):
super(PositionwiseFeedForwardLayer, self).__init__(name_scope) super(PositionwiseFeedForwardLayer, self).__init__()
self._i2h = FC(name_scope=self.full_name(), self._i2h = Linear( input_dim= input_hid,
size=d_inner_hid, output_dim=d_inner_hid,
num_flatten_dims=2,
act="relu") act="relu")
self._h2o = FC(name_scope=self.full_name(), self._h2o = Linear( input_dim = d_inner_hid,
size=d_hid, output_dim=d_hid)
num_flatten_dims=2)
self._dropout_rate = dropout_rate self._dropout_rate = dropout_rate
def forward(self, x): def forward(self, x):
...@@ -140,7 +137,6 @@ class MultiHeadAttentionLayer(Layer): ...@@ -140,7 +137,6 @@ class MultiHeadAttentionLayer(Layer):
MultiHeadAttentionLayer MultiHeadAttentionLayer
""" """
def __init__(self, def __init__(self,
name_scope,
d_key, d_key,
d_value, d_value,
d_model, d_model,
...@@ -149,28 +145,24 @@ class MultiHeadAttentionLayer(Layer): ...@@ -149,28 +145,24 @@ class MultiHeadAttentionLayer(Layer):
cache=None, cache=None,
gather_idx=None, gather_idx=None,
static_kv=False): static_kv=False):
super(MultiHeadAttentionLayer, self).__init__(name_scope) super(MultiHeadAttentionLayer, self).__init__()
self._n_head = n_head self._n_head = n_head
self._d_key = d_key self._d_key = d_key
self._d_value = d_value self._d_value = d_value
self._d_model = d_model self._d_model = d_model
self._dropout_rate = dropout_rate self._dropout_rate = dropout_rate
self._q_fc = FC(name_scope=self.full_name(), self._q_fc = Linear( input_dim = d_model,
size=d_key * n_head, output_dim=d_key * n_head,
bias_attr=False, bias_attr=False )
num_flatten_dims=2) self._k_fc = Linear( input_dim = d_model,
self._k_fc = FC(name_scope=self.full_name(), output_dim=d_key * n_head,
size=d_key * n_head, bias_attr=False )
bias_attr=False, self._v_fc = Linear( input_dim = d_model,
num_flatten_dims=2) output_dim=d_value * n_head,
self._v_fc = FC(name_scope=self.full_name(), bias_attr=False )
size=d_value * n_head, self._proj_fc = Linear( input_dim = d_model,
bias_attr=False, output_dim=self._d_model,
num_flatten_dims=2) bias_attr=False )
self._proj_fc = FC(name_scope=self.full_name(),
size=self._d_model,
bias_attr=False,
num_flatten_dims=2)
def forward(self, def forward(self,
queries, queries,
...@@ -194,18 +186,18 @@ class MultiHeadAttentionLayer(Layer): ...@@ -194,18 +186,18 @@ class MultiHeadAttentionLayer(Layer):
q = self._q_fc(queries) q = self._q_fc(queries)
k = self._k_fc(keys) k = self._k_fc(keys)
v = self._v_fc(values) v = self._v_fc(values)
# split head # split head
reshaped_q = layers.reshape(x=q, reshaped_q = layers.reshape(x=q,
shape=[0, 0, self._n_head, self._d_key], shape=[ q.shape[0], q.shape[1], self._n_head, self._d_key],
inplace=False) inplace=False)
transpose_q = layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3]) transpose_q = layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3])
reshaped_k = layers.reshape(x=k, reshaped_k = layers.reshape(x=k,
shape=[0, 0, self._n_head, self._d_key], shape=[ k.shape[0], k.shape[1], self._n_head, self._d_key],
inplace=False) inplace=False)
transpose_k = layers.transpose(x=reshaped_k, perm=[0, 2, 1, 3]) transpose_k = layers.transpose(x=reshaped_k, perm=[0, 2, 1, 3])
reshaped_v = layers.reshape(x=v, reshaped_v = layers.reshape(x=v,
shape=[0, 0, self._n_head, self._d_value], shape=[ v.shape[0], v.shape[1], self._n_head, self._d_value],
inplace=False) inplace=False)
transpose_v = layers.transpose(x=reshaped_v, perm=[0, 2, 1, 3]) transpose_v = layers.transpose(x=reshaped_v, perm=[0, 2, 1, 3])
...@@ -250,7 +242,6 @@ class EncoderSubLayer(Layer): ...@@ -250,7 +242,6 @@ class EncoderSubLayer(Layer):
EncoderSubLayer EncoderSubLayer
""" """
def __init__(self, def __init__(self,
name_scope,
n_head, n_head,
d_key, d_key,
d_value, d_value,
...@@ -262,24 +253,21 @@ class EncoderSubLayer(Layer): ...@@ -262,24 +253,21 @@ class EncoderSubLayer(Layer):
preprocess_cmd="n", preprocess_cmd="n",
postprocess_cmd="da"): postprocess_cmd="da"):
super(EncoderSubLayer, self).__init__(name_scope) super(EncoderSubLayer, self).__init__()
self._preprocess_cmd = preprocess_cmd self._preprocess_cmd = preprocess_cmd
self._postprocess_cmd = postprocess_cmd self._postprocess_cmd = postprocess_cmd
self._prepostprocess_dropout = prepostprocess_dropout self._prepostprocess_dropout = prepostprocess_dropout
self._preprocess_layer = PrePostProcessLayer(self.full_name(), self._preprocess_layer = PrePostProcessLayer(self._preprocess_cmd, [d_model])
self._preprocess_cmd, 3)
self._multihead_attention_layer = MultiHeadAttentionLayer( self._multihead_attention_layer = MultiHeadAttentionLayer(
self.full_name(), d_key, d_value, d_model, n_head, d_key, d_value, d_model, n_head,
attention_dropout) attention_dropout)
self._postprocess_layer = PrePostProcessLayer(self.full_name(), self._postprocess_layer = PrePostProcessLayer(self._postprocess_cmd,
self._postprocess_cmd,
None) None)
self._preprocess_layer2 = PrePostProcessLayer(self.full_name(), self._preprocess_layer2 = PrePostProcessLayer(self._preprocess_cmd, [d_model])
self._preprocess_cmd, 3)
self._positionwise_feed_forward = PositionwiseFeedForwardLayer( self._positionwise_feed_forward = PositionwiseFeedForwardLayer(
self.full_name(), d_inner_hid, d_model, relu_dropout) d_model, d_inner_hid, d_model, relu_dropout)
self._postprocess_layer2 = PrePostProcessLayer(self.full_name(), self._postprocess_layer2 = PrePostProcessLayer(
self._postprocess_cmd, self._postprocess_cmd,
None) None)
...@@ -311,7 +299,6 @@ class EncoderLayer(Layer): ...@@ -311,7 +299,6 @@ class EncoderLayer(Layer):
encoder encoder
""" """
def __init__(self, def __init__(self,
name_scope,
n_layer, n_layer,
n_head, n_head,
d_key, d_key,
...@@ -324,18 +311,18 @@ class EncoderLayer(Layer): ...@@ -324,18 +311,18 @@ class EncoderLayer(Layer):
preprocess_cmd="n", preprocess_cmd="n",
postprocess_cmd="da"): postprocess_cmd="da"):
super(EncoderLayer, self).__init__(name_scope) super(EncoderLayer, self).__init__()
self._preprocess_cmd = preprocess_cmd self._preprocess_cmd = preprocess_cmd
self._encoder_sublayers = list() self._encoder_sublayers = list()
self._prepostprocess_dropout = prepostprocess_dropout self._prepostprocess_dropout = prepostprocess_dropout
self._n_layer = n_layer self._n_layer = n_layer
self._preprocess_layer = PrePostProcessLayer(self.full_name(), self._preprocess_layer = PrePostProcessLayer(
self._preprocess_cmd, 3) self._preprocess_cmd, [d_model])
for i in range(n_layer): for i in range(n_layer):
self._encoder_sublayers.append( self._encoder_sublayers.append(
self.add_sublayer( self.add_sublayer(
'esl_%d' % i, 'esl_%d' % i,
EncoderSubLayer(self.full_name(), n_head, d_key, d_value, EncoderSubLayer(n_head, d_key, d_value,
d_model, d_inner_hid, d_model, d_inner_hid,
prepostprocess_dropout, attention_dropout, prepostprocess_dropout, attention_dropout,
relu_dropout, preprocess_cmd, relu_dropout, preprocess_cmd,
...@@ -361,20 +348,18 @@ class PrepareEncoderDecoderLayer(Layer): ...@@ -361,20 +348,18 @@ class PrepareEncoderDecoderLayer(Layer):
PrepareEncoderDecoderLayer PrepareEncoderDecoderLayer
""" """
def __init__(self, def __init__(self,
name_scope,
src_vocab_size, src_vocab_size,
src_emb_dim, src_emb_dim,
src_max_len, src_max_len,
dropout_rate, dropout_rate,
word_emb_param_name=None, word_emb_param_name=None,
pos_enc_param_name=None): pos_enc_param_name=None):
super(PrepareEncoderDecoderLayer, self).__init__(name_scope) super(PrepareEncoderDecoderLayer, self).__init__()
self._src_max_len = src_max_len self._src_max_len = src_max_len
self._src_emb_dim = src_emb_dim self._src_emb_dim = src_emb_dim
self._src_vocab_size = src_vocab_size self._src_vocab_size = src_vocab_size
self._dropout_rate = dropout_rate self._dropout_rate = dropout_rate
self._input_emb = Embedding(name_scope=self.full_name(), self._input_emb = Embedding(size=[src_vocab_size, src_emb_dim],
size=[src_vocab_size, src_emb_dim],
padding_idx=0, padding_idx=0,
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
name=word_emb_param_name, name=word_emb_param_name,
...@@ -383,7 +368,6 @@ class PrepareEncoderDecoderLayer(Layer): ...@@ -383,7 +368,6 @@ class PrepareEncoderDecoderLayer(Layer):
pos_inp = position_encoding_init(src_max_len, src_emb_dim) pos_inp = position_encoding_init(src_max_len, src_emb_dim)
self._pos_emb = Embedding( self._pos_emb = Embedding(
name_scope=self.full_name(),
size=[self._src_max_len, src_emb_dim], size=[self._src_max_len, src_emb_dim],
param_attr=fluid.ParamAttr( param_attr=fluid.ParamAttr(
name=pos_enc_param_name, name=pos_enc_param_name,
...@@ -411,6 +395,7 @@ class PrepareEncoderDecoderLayer(Layer): ...@@ -411,6 +395,7 @@ class PrepareEncoderDecoderLayer(Layer):
src_pos_emb = self._pos_emb(src_pos) src_pos_emb = self._pos_emb(src_pos)
src_pos_emb.stop_gradient = True src_pos_emb.stop_gradient = True
enc_input = src_word_emb + src_pos_emb enc_input = src_word_emb + src_pos_emb
enc_input = layers.reshape( enc_input, shape=[ enc_input.shape[0], enc_input.shape[1], -1])
return layers.dropout( return layers.dropout(
enc_input, dropout_prob=self._dropout_rate, enc_input, dropout_prob=self._dropout_rate,
is_test=False) if self._dropout_rate else enc_input is_test=False) if self._dropout_rate else enc_input
...@@ -420,24 +405,23 @@ class WrapEncoderLayer(Layer): ...@@ -420,24 +405,23 @@ class WrapEncoderLayer(Layer):
""" """
encoderlayer encoderlayer
""" """
def __init__(self, name_cope, src_vocab_size, max_length, n_layer, n_head, def __init__(self, src_vocab_size, max_length, n_layer, n_head,
d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout,
attention_dropout, relu_dropout, preprocess_cmd, attention_dropout, relu_dropout, preprocess_cmd,
postprocess_cmd, weight_sharing): postprocess_cmd, weight_sharing):
""" """
The wrapper assembles together all needed layers for the encoder. The wrapper assembles together all needed layers for the encoder.
""" """
super(WrapEncoderLayer, self).__init__(name_cope) super(WrapEncoderLayer, self).__init__()
self._prepare_encoder_layer = PrepareEncoderDecoderLayer( self._prepare_encoder_layer = PrepareEncoderDecoderLayer(
self.full_name(),
src_vocab_size, src_vocab_size,
d_model, d_model,
max_length, max_length,
prepostprocess_dropout, prepostprocess_dropout,
word_emb_param_name=word_emb_param_names[0], word_emb_param_name=word_emb_param_names[0],
pos_enc_param_name=pos_enc_param_names[0]) pos_enc_param_name=pos_enc_param_names[0])
self._encoder = EncoderLayer(self.full_name(), n_layer, n_head, d_key, self._encoder = EncoderLayer(n_layer, n_head, d_key,
d_value, d_model, d_inner_hid, d_value, d_model, d_inner_hid,
prepostprocess_dropout, attention_dropout, prepostprocess_dropout, attention_dropout,
relu_dropout, preprocess_cmd, relu_dropout, preprocess_cmd,
...@@ -455,32 +439,32 @@ class DecoderSubLayer(Layer): ...@@ -455,32 +439,32 @@ class DecoderSubLayer(Layer):
""" """
decoder decoder
""" """
def __init__(self, name_scope, n_head, d_key, d_value, d_model, d_inner_hid, def __init__(self, n_head, d_key, d_value, d_model, d_inner_hid,
prepostprocess_dropout, attention_dropout, relu_dropout, prepostprocess_dropout, attention_dropout, relu_dropout,
preprocess_cmd, postprocess_cmd): preprocess_cmd, postprocess_cmd):
super(DecoderSubLayer, self).__init__(name_scope) super(DecoderSubLayer, self).__init__()
self._postprocess_cmd = postprocess_cmd self._postprocess_cmd = postprocess_cmd
self._preprocess_cmd = preprocess_cmd self._preprocess_cmd = preprocess_cmd
self._prepostprcess_dropout = prepostprocess_dropout self._prepostprcess_dropout = prepostprocess_dropout
self._pre_process_layer = PrePostProcessLayer(self.full_name(), self._pre_process_layer = PrePostProcessLayer(
preprocess_cmd, 3) preprocess_cmd, [d_model])
self._multihead_attention_layer = MultiHeadAttentionLayer( self._multihead_attention_layer = MultiHeadAttentionLayer(
self.full_name(), d_key, d_value, d_model, n_head, d_key, d_value, d_model, n_head,
attention_dropout) attention_dropout)
self._post_process_layer = PrePostProcessLayer(self.full_name(), self._post_process_layer = PrePostProcessLayer(
postprocess_cmd, None) postprocess_cmd, None)
self._pre_process_layer2 = PrePostProcessLayer(self.full_name(), self._pre_process_layer2 = PrePostProcessLayer(
preprocess_cmd, 3) preprocess_cmd, [d_model])
self._multihead_attention_layer2 = MultiHeadAttentionLayer( self._multihead_attention_layer2 = MultiHeadAttentionLayer(
self.full_name(), d_key, d_value, d_model, n_head, d_key, d_value, d_model, n_head,
attention_dropout) attention_dropout)
self._post_process_layer2 = PrePostProcessLayer(self.full_name(), self._post_process_layer2 = PrePostProcessLayer(
postprocess_cmd, None) postprocess_cmd, [d_model])
self._pre_process_layer3 = PrePostProcessLayer(self.full_name(), self._pre_process_layer3 = PrePostProcessLayer(
preprocess_cmd, 3) preprocess_cmd, [d_model])
self._positionwise_feed_forward_layer = PositionwiseFeedForwardLayer( self._positionwise_feed_forward_layer = PositionwiseFeedForwardLayer(
self.full_name(), d_inner_hid, d_model, relu_dropout) d_model, d_inner_hid, d_model, relu_dropout)
self._post_process_layer3 = PrePostProcessLayer(self.full_name(), self._post_process_layer3 = PrePostProcessLayer(
postprocess_cmd, None) postprocess_cmd, None)
def forward(self, def forward(self,
...@@ -529,12 +513,11 @@ class DecoderLayer(Layer): ...@@ -529,12 +513,11 @@ class DecoderLayer(Layer):
""" """
decoder decoder
""" """
def __init__(self, name_scope, n_layer, n_head, d_key, d_value, d_model, def __init__(self, n_layer, n_head, d_key, d_value, d_model,
d_inner_hid, prepostprocess_dropout, attention_dropout, d_inner_hid, prepostprocess_dropout, attention_dropout,
relu_dropout, preprocess_cmd, postprocess_cmd): relu_dropout, preprocess_cmd, postprocess_cmd):
super(DecoderLayer, self).__init__(name_scope) super(DecoderLayer, self).__init__()
self._pre_process_layer = PrePostProcessLayer(self.full_name(), self._pre_process_layer = PrePostProcessLayer(preprocess_cmd, [d_model])
preprocess_cmd, 3)
self._decoder_sub_layers = list() self._decoder_sub_layers = list()
self._n_layer = n_layer self._n_layer = n_layer
self._preprocess_cmd = preprocess_cmd self._preprocess_cmd = preprocess_cmd
...@@ -543,7 +526,7 @@ class DecoderLayer(Layer): ...@@ -543,7 +526,7 @@ class DecoderLayer(Layer):
self._decoder_sub_layers.append( self._decoder_sub_layers.append(
self.add_sublayer( self.add_sublayer(
'dsl_%d' % i, 'dsl_%d' % i,
DecoderSubLayer(self.full_name(), n_head, d_key, d_value, DecoderSubLayer( n_head, d_key, d_value,
d_model, d_inner_hid, d_model, d_inner_hid,
prepostprocess_dropout, attention_dropout, prepostprocess_dropout, attention_dropout,
relu_dropout, preprocess_cmd, relu_dropout, preprocess_cmd,
...@@ -581,7 +564,6 @@ class WrapDecoderLayer(Layer): ...@@ -581,7 +564,6 @@ class WrapDecoderLayer(Layer):
decoder decoder
""" """
def __init__(self, def __init__(self,
name_scope,
trg_vocab_size, trg_vocab_size,
max_length, max_length,
n_layer, n_layer,
...@@ -600,25 +582,24 @@ class WrapDecoderLayer(Layer): ...@@ -600,25 +582,24 @@ class WrapDecoderLayer(Layer):
""" """
The wrapper assembles together all needed layers for the encoder. The wrapper assembles together all needed layers for the encoder.
""" """
super(WrapDecoderLayer, self).__init__(name_scope) super(WrapDecoderLayer, self).__init__()
self._prepare_decoder_layer = PrepareEncoderDecoderLayer( self._prepare_decoder_layer = PrepareEncoderDecoderLayer(
self.full_name(),
trg_vocab_size, trg_vocab_size,
d_model, d_model,
max_length, max_length,
prepostprocess_dropout, prepostprocess_dropout,
word_emb_param_name=word_emb_param_names[1], word_emb_param_name=word_emb_param_names[1],
pos_enc_param_name=pos_enc_param_names[1]) pos_enc_param_name=pos_enc_param_names[1])
self._decoder_layer = DecoderLayer(self.full_name(), n_layer, n_head, self._decoder_layer = DecoderLayer(n_layer, n_head,
d_key, d_value, d_model, d_inner_hid, d_key, d_value, d_model, d_inner_hid,
prepostprocess_dropout, prepostprocess_dropout,
attention_dropout, relu_dropout, attention_dropout, relu_dropout,
preprocess_cmd, postprocess_cmd) preprocess_cmd, postprocess_cmd)
self._weight_sharing = weight_sharing self._weight_sharing = weight_sharing
if not weight_sharing: if not weight_sharing:
self._fc = FC(self.full_name(), self._fc = Linear(input_dim = d_model,
size=trg_vocab_size, output_dim=trg_vocab_size,
bias_attr=False) bias_attr=False)
def forward(self, dec_inputs, enc_output, caches=None, gather_idx=None): def forward(self, dec_inputs, enc_output, caches=None, gather_idx=None):
...@@ -657,7 +638,6 @@ class TransFormer(Layer): ...@@ -657,7 +638,6 @@ class TransFormer(Layer):
model model
""" """
def __init__(self, def __init__(self,
name_scope,
src_vocab_size, src_vocab_size,
trg_vocab_size, trg_vocab_size,
max_length, max_length,
...@@ -674,7 +654,7 @@ class TransFormer(Layer): ...@@ -674,7 +654,7 @@ class TransFormer(Layer):
postprocess_cmd, postprocess_cmd,
weight_sharing, weight_sharing,
label_smooth_eps=0.0): label_smooth_eps=0.0):
super(TransFormer, self).__init__(name_scope) super(TransFormer, self).__init__()
self._label_smooth_eps = label_smooth_eps self._label_smooth_eps = label_smooth_eps
self._trg_vocab_size = trg_vocab_size self._trg_vocab_size = trg_vocab_size
if weight_sharing: if weight_sharing:
...@@ -682,12 +662,12 @@ class TransFormer(Layer): ...@@ -682,12 +662,12 @@ class TransFormer(Layer):
"Vocabularies in source and target should be same for weight sharing." "Vocabularies in source and target should be same for weight sharing."
) )
self._wrap_encoder_layer = WrapEncoderLayer( self._wrap_encoder_layer = WrapEncoderLayer(
self.full_name(), src_vocab_size, max_length, n_layer, n_head, src_vocab_size, max_length, n_layer, n_head,
d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout,
attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd,
weight_sharing) weight_sharing)
self._wrap_decoder_layer = WrapDecoderLayer( self._wrap_decoder_layer = WrapDecoderLayer(
self.full_name(), trg_vocab_size, max_length, n_layer, n_head, trg_vocab_size, max_length, n_layer, n_head,
d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout,
attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd,
weight_sharing) weight_sharing)
...@@ -869,18 +849,25 @@ class TransFormer(Layer): ...@@ -869,18 +849,25 @@ class TransFormer(Layer):
topk_scores, topk_ids = layers.topk(flat_curr_scores, topk_scores, topk_ids = layers.topk(flat_curr_scores,
k=beam_size * 2) k=beam_size * 2)
print( "topk ids", topk_ids)
topk_log_probs = topk_scores * length_penalty topk_log_probs = topk_scores * length_penalty
topk_beam_index = topk_ids // self._trg_vocab_size topk_beam_index = topk_ids // self._trg_vocab_size
topk_ids = topk_ids % self._trg_vocab_size topk_ids = topk_ids % self._trg_vocab_size
print( "topk ids2", topk_ids)
# use gather as gather_nd, TODO: use gather_nd # use gather as gather_nd, TODO: use gather_nd
topk_seq = gather_2d_by_gather(alive_seq, topk_beam_index, topk_seq = gather_2d_by_gather(alive_seq, topk_beam_index,
beam_size, batch_size) beam_size, batch_size)
print( "topk ids", topk_ids )
reshape_temp = layers.reshape(topk_ids, topk_ids.shape + [1])
topk_seq = layers.concat( topk_seq = layers.concat(
[topk_seq, [topk_seq,
layers.reshape(topk_ids, topk_ids.shape + [1])], reshape_temp],
axis=2) axis=2)
states = update_states(states, topk_beam_index, beam_size) states = update_states(states, topk_beam_index, beam_size)
eos = layers.fill_constant(shape=topk_ids.shape, eos = layers.fill_constant(shape=topk_ids.shape,
......
dygraph/transformer/predict.py
浏览文件 @ 31186c41
...@@ -62,9 +62,9 @@ def prepare_infer_input(insts, src_pad_idx, bos_idx, n_head): ...@@ -62,9 +62,9 @@ def prepare_infer_input(insts, src_pad_idx, bos_idx, n_head):
trg_word = np.asarray([[bos_idx]] * len(insts), dtype="int64") trg_word = np.asarray([[bos_idx]] * len(insts), dtype="int64")
trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :], trg_src_attn_bias = np.tile(src_slf_attn_bias[:, :, ::src_max_len, :],
[1, 1, 1, 1]).astype("float32") [1, 1, 1, 1]).astype("float32")
trg_word = trg_word.reshape(-1, 1, 1) trg_word = trg_word.reshape(-1, 1, 1 )
src_word = src_word.reshape(-1, src_max_len, 1) src_word = src_word.reshape(-1, src_max_len, 1 )
src_pos = src_pos.reshape(-1, src_max_len, 1) src_pos = src_pos.reshape(-1, src_max_len,1 )
data_inputs = [ data_inputs = [
src_word, src_pos, src_slf_attn_bias, trg_word, trg_src_attn_bias src_word, src_pos, src_slf_attn_bias, trg_word, trg_src_attn_bias
...@@ -101,7 +101,7 @@ def infer(args): ...@@ -101,7 +101,7 @@ def infer(args):
if args.use_data_parallel else fluid.CUDAPlace(0) if args.use_data_parallel else fluid.CUDAPlace(0)
with fluid.dygraph.guard(place): with fluid.dygraph.guard(place):
transformer = TransFormer( transformer = TransFormer(
'transformer', ModelHyperParams.src_vocab_size, ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1, ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer, ModelHyperParams.n_head, ModelHyperParams.n_layer, ModelHyperParams.n_head,
ModelHyperParams.d_key, ModelHyperParams.d_value, ModelHyperParams.d_key, ModelHyperParams.d_value,
...@@ -129,7 +129,8 @@ def infer(args): ...@@ -129,7 +129,8 @@ def infer(args):
enc_inputs, dec_inputs = prepare_infer_input( enc_inputs, dec_inputs = prepare_infer_input(
batch, ModelHyperParams.eos_idx, ModelHyperParams.bos_idx, batch, ModelHyperParams.eos_idx, ModelHyperParams.bos_idx,
ModelHyperParams.n_head) ModelHyperParams.n_head)
print( "enc inputs", enc_inputs[0].shape )
finished_seq, finished_scores = transformer.beam_search( finished_seq, finished_scores = transformer.beam_search(
enc_inputs, enc_inputs,
dec_inputs, dec_inputs,
......
dygraph/transformer/train.py
浏览文件 @ 31186c41
...@@ -110,7 +110,7 @@ def train(args): ...@@ -110,7 +110,7 @@ def train(args):
# define model # define model
transformer = TransFormer( transformer = TransFormer(
'transformer', ModelHyperParams.src_vocab_size, ModelHyperParams.src_vocab_size,
ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1, ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1,
ModelHyperParams.n_layer, ModelHyperParams.n_head, ModelHyperParams.n_layer, ModelHyperParams.n_head,
ModelHyperParams.d_key, ModelHyperParams.d_value, ModelHyperParams.d_key, ModelHyperParams.d_value,
...@@ -123,6 +123,7 @@ def train(args): ...@@ -123,6 +123,7 @@ def train(args):
optimizer = fluid.optimizer.Adam(learning_rate=NoamDecay( optimizer = fluid.optimizer.Adam(learning_rate=NoamDecay(
ModelHyperParams.d_model, TrainTaskConfig.warmup_steps, ModelHyperParams.d_model, TrainTaskConfig.warmup_steps,
TrainTaskConfig.learning_rate), TrainTaskConfig.learning_rate),
parameter_list = transformer.parameters(),
beta1=TrainTaskConfig.beta1, beta1=TrainTaskConfig.beta1,
beta2=TrainTaskConfig.beta2, beta2=TrainTaskConfig.beta2,
epsilon=TrainTaskConfig.eps) epsilon=TrainTaskConfig.eps)
......
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