xiaoting
GitHub
commit
31e62cb8ba
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@ -257,6 +257,7 @@ class SimpleReader(object):
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norm_img = process_image_srn(
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img=img,
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image_shape=self.image_shape,
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char_ops=self.char_ops,
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num_heads=self.num_heads,
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max_text_length=self.max_text_length)
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else:
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@ -4,8 +4,10 @@ import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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# Set seed for CE
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dropout_seed = None
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encoder_data_input_fields = (
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"src_word",
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"src_pos",
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"src_slf_attn_bias", )
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def wrap_layer_with_block(layer, block_idx):
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@ -45,25 +47,6 @@ def wrap_layer_with_block(layer, block_idx):
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return layer_wrapper
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def position_encoding_init(n_position, d_pos_vec):
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"""
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Generate the initial values for the sinusoid position encoding table.
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"""
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channels = d_pos_vec
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position = np.arange(n_position)
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num_timescales = channels // 2
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log_timescale_increment = (np.log(float(1e4) / float(1)) /
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(num_timescales - 1))
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inv_timescales = np.exp(np.arange(
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num_timescales)) * -log_timescale_increment
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scaled_time = np.expand_dims(position, 1) * np.expand_dims(inv_timescales,
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0)
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signal = np.concatenate([np.sin(scaled_time), np.cos(scaled_time)], axis=1)
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signal = np.pad(signal, [[0, 0], [0, np.mod(channels, 2)]], 'constant')
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position_enc = signal
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return position_enc.astype("float32")
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def multi_head_attention(queries,
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keys,
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values,
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@ -200,10 +183,7 @@ def multi_head_attention(queries,
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weights = layers.softmax(product)
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if dropout_rate:
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weights = layers.dropout(
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weights,
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dropout_prob=dropout_rate,
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seed=dropout_seed,
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is_test=False)
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weights, dropout_prob=dropout_rate, seed=None, is_test=False)
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out = layers.matmul(weights, v)
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return out
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@ -235,7 +215,7 @@ def positionwise_feed_forward(x, d_inner_hid, d_hid, dropout_rate):
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act="relu")
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if dropout_rate:
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hidden = layers.dropout(
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hidden, dropout_prob=dropout_rate, seed=dropout_seed, is_test=False)
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hidden, dropout_prob=dropout_rate, seed=None, is_test=False)
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out = layers.fc(input=hidden, size=d_hid, num_flatten_dims=2)
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return out
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@ -259,10 +239,7 @@ def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0.):
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elif cmd == "d": # add dropout
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if dropout_rate:
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out = layers.dropout(
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out,
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dropout_prob=dropout_rate,
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seed=dropout_seed,
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is_test=False)
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out, dropout_prob=dropout_rate, seed=None, is_test=False)
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return out
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@ -286,9 +263,8 @@ def prepare_encoder(
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This module is used at the bottom of the encoder stacks.
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"""
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src_word_emb = src_word #layers.concat(res,axis=1)
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src_word_emb = src_word
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src_word_emb = layers.cast(src_word_emb, 'float32')
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# print("src_word_emb",src_word_emb)
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src_word_emb = layers.scale(x=src_word_emb, scale=src_emb_dim**0.5)
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src_pos_enc = layers.embedding(
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@ -299,7 +275,7 @@ def prepare_encoder(
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src_pos_enc.stop_gradient = True
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enc_input = src_word_emb + src_pos_enc
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return layers.dropout(
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enc_input, dropout_prob=dropout_rate, seed=dropout_seed,
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enc_input, dropout_prob=dropout_rate, seed=None,
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is_test=False) if dropout_rate else enc_input
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@ -324,7 +300,7 @@ def prepare_decoder(src_word,
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param_attr=fluid.ParamAttr(
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name=word_emb_param_name,
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initializer=fluid.initializer.Normal(0., src_emb_dim**-0.5)))
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# print("target_word_emb",src_word_emb)
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src_word_emb = layers.scale(x=src_word_emb, scale=src_emb_dim**0.5)
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src_pos_enc = layers.embedding(
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src_pos,
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@ -334,16 +310,10 @@ def prepare_decoder(src_word,
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src_pos_enc.stop_gradient = True
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enc_input = src_word_emb + src_pos_enc
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return layers.dropout(
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enc_input, dropout_prob=dropout_rate, seed=dropout_seed,
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enc_input, dropout_prob=dropout_rate, seed=None,
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is_test=False) if dropout_rate else enc_input
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# prepare_encoder = partial(
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# prepare_encoder_decoder, pos_enc_param_name=pos_enc_param_names[0])
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# prepare_decoder = partial(
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# prepare_encoder_decoder, pos_enc_param_name=pos_enc_param_names[1])
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def encoder_layer(enc_input,
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attn_bias,
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n_head,
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@ -412,234 +382,6 @@ def encoder(enc_input,
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return enc_output
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def decoder_layer(dec_input,
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enc_output,
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slf_attn_bias,
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dec_enc_attn_bias,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd,
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postprocess_cmd,
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cache=None,
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gather_idx=None):
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""" The layer to be stacked in decoder part.
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The structure of this module is similar to that in the encoder part except
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a multi-head attention is added to implement encoder-decoder attention.
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"""
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slf_attn_output = multi_head_attention(
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pre_process_layer(dec_input, preprocess_cmd, prepostprocess_dropout),
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None,
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None,
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slf_attn_bias,
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d_key,
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d_value,
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d_model,
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n_head,
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attention_dropout,
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cache=cache,
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gather_idx=gather_idx)
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slf_attn_output = post_process_layer(
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dec_input,
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slf_attn_output,
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postprocess_cmd,
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prepostprocess_dropout, )
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enc_attn_output = multi_head_attention(
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pre_process_layer(slf_attn_output, preprocess_cmd,
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prepostprocess_dropout),
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enc_output,
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enc_output,
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dec_enc_attn_bias,
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d_key,
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d_value,
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d_model,
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n_head,
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attention_dropout,
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cache=cache,
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gather_idx=gather_idx,
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static_kv=True)
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enc_attn_output = post_process_layer(
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slf_attn_output,
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enc_attn_output,
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postprocess_cmd,
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prepostprocess_dropout, )
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ffd_output = positionwise_feed_forward(
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pre_process_layer(enc_attn_output, preprocess_cmd,
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prepostprocess_dropout),
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d_inner_hid,
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d_model,
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relu_dropout, )
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dec_output = post_process_layer(
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enc_attn_output,
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ffd_output,
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postprocess_cmd,
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prepostprocess_dropout, )
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return dec_output
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def decoder(dec_input,
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enc_output,
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dec_slf_attn_bias,
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dec_enc_attn_bias,
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n_layer,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd,
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postprocess_cmd,
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caches=None,
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gather_idx=None):
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"""
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The decoder is composed of a stack of identical decoder_layer layers.
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"""
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for i in range(n_layer):
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dec_output = decoder_layer(
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dec_input,
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enc_output,
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dec_slf_attn_bias,
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dec_enc_attn_bias,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd,
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postprocess_cmd,
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cache=None if caches is None else caches[i],
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gather_idx=gather_idx)
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dec_input = dec_output
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dec_output = pre_process_layer(dec_output, preprocess_cmd,
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prepostprocess_dropout)
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return dec_output
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def make_all_inputs(input_fields):
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"""
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Define the input data layers for the transformer model.
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"""
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inputs = []
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for input_field in input_fields:
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input_var = layers.data(
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name=input_field,
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shape=input_descs[input_field][0],
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dtype=input_descs[input_field][1],
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lod_level=input_descs[input_field][2]
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if len(input_descs[input_field]) == 3 else 0,
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append_batch_size=False)
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inputs.append(input_var)
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return inputs
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def make_all_py_reader_inputs(input_fields, is_test=False):
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reader = layers.py_reader(
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capacity=20,
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name="test_reader" if is_test else "train_reader",
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shapes=[input_descs[input_field][0] for input_field in input_fields],
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dtypes=[input_descs[input_field][1] for input_field in input_fields],
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lod_levels=[
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input_descs[input_field][2]
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if len(input_descs[input_field]) == 3 else 0
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for input_field in input_fields
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])
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return layers.read_file(reader), reader
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def transformer(src_vocab_size,
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trg_vocab_size,
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max_length,
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n_layer,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd,
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postprocess_cmd,
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weight_sharing,
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label_smooth_eps,
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bos_idx=0,
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use_py_reader=False,
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is_test=False):
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if weight_sharing:
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assert src_vocab_size == trg_vocab_size, (
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"Vocabularies in source and target should be same for weight sharing."
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)
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data_input_names = encoder_data_input_fields + \
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decoder_data_input_fields[:-1] + label_data_input_fields
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if use_py_reader:
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all_inputs, reader = make_all_py_reader_inputs(data_input_names,
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is_test)
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else:
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all_inputs = make_all_inputs(data_input_names)
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# print("all inputs",all_inputs)
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enc_inputs_len = len(encoder_data_input_fields)
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dec_inputs_len = len(decoder_data_input_fields[:-1])
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enc_inputs = all_inputs[0:enc_inputs_len]
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dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len + dec_inputs_len]
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label = all_inputs[-2]
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weights = all_inputs[-1]
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enc_output = wrap_encoder(
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src_vocab_size, 64, n_layer, n_head, d_key, d_value, d_model,
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d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout,
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preprocess_cmd, postprocess_cmd, weight_sharing, enc_inputs)
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predict = wrap_decoder(
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trg_vocab_size,
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max_length,
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n_layer,
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n_head,
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d_key,
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d_value,
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d_model,
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d_inner_hid,
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prepostprocess_dropout,
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attention_dropout,
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relu_dropout,
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preprocess_cmd,
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postprocess_cmd,
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weight_sharing,
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dec_inputs,
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enc_output, )
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# Padding index do not contribute to the total loss. The weights is used to
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# cancel padding index in calculating the loss.
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if label_smooth_eps:
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label = layers.label_smooth(
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label=layers.one_hot(
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input=label, depth=trg_vocab_size),
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epsilon=label_smooth_eps)
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cost = layers.softmax_with_cross_entropy(
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logits=predict,
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label=label,
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soft_label=True if label_smooth_eps else False)
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weighted_cost = cost * weights
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sum_cost = layers.reduce_sum(weighted_cost)
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token_num = layers.reduce_sum(weights)
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token_num.stop_gradient = True
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avg_cost = sum_cost / token_num
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return sum_cost, avg_cost, predict, token_num, reader if use_py_reader else None
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def wrap_encoder_forFeature(src_vocab_size,
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max_length,
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n_layer,
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@ -662,44 +404,8 @@ def wrap_encoder_forFeature(src_vocab_size,
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img
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"""
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if enc_inputs is None:
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# This is used to implement independent encoder program in inference.
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conv_features, src_pos, src_slf_attn_bias = make_all_inputs(
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encoder_data_input_fields)
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else:
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conv_features, src_pos, src_slf_attn_bias = enc_inputs #
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b, t, c = conv_features.shape
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#"""
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# insert cnn
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#"""
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#import basemodel
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# feat = basemodel.resnet_50(img)
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# mycrnn = basemodel.CRNN()
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# feat = mycrnn.ocr_convs(img,use_cudnn=TrainTaskConfig.use_gpu)
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# b, c, w, h = feat.shape
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# src_word = layers.reshape(feat, shape=[-1, c, w * h])
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#myconv8 = basemodel.conv8()
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#feat = myconv8.net(img )
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#b , c, h, w = feat.shape#h=6
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#print(feat)
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#layers.Print(feat,message="conv_feat",summarize=10)
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#feat =layers.conv2d(feat,c,filter_size =[4 , 1],act="relu")
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#feat = layers.pool2d(feat,pool_stride=(3,1),pool_size=(3,1))
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#src_word = layers.squeeze(feat,axes=[2]) #src_word [-1,c,ww]
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#feat = layers.transpose(feat, [0,3,1,2])
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#src_word = layers.reshape(feat,[-1,w, c*h])
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#src_word = layers.im2sequence(
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# input=feat,
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# stride=[1, 1],
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# filter_size=[feat.shape[2], 1])
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#layers.Print(src_word,message="src_word",summarize=10)
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# print('feat',feat)
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#print("src_word",src_word)
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enc_input = prepare_encoder(
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conv_features,
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@ -749,43 +455,9 @@ def wrap_encoder(src_vocab_size,
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img, src_pos, src_slf_attn_bias = enc_inputs
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img
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"""
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if enc_inputs is None:
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# This is used to implement independent encoder program in inference.
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src_word, src_pos, src_slf_attn_bias = make_all_inputs(
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encoder_data_input_fields)
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else:
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src_word, src_pos, src_slf_attn_bias = enc_inputs #
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#"""
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# insert cnn
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#"""
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#import basemodel
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# feat = basemodel.resnet_50(img)
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# mycrnn = basemodel.CRNN()
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# feat = mycrnn.ocr_convs(img,use_cudnn=TrainTaskConfig.use_gpu)
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# b, c, w, h = feat.shape
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# src_word = layers.reshape(feat, shape=[-1, c, w * h])
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#myconv8 = basemodel.conv8()
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#feat = myconv8.net(img )
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#b , c, h, w = feat.shape#h=6
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#print(feat)
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#layers.Print(feat,message="conv_feat",summarize=10)
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#feat =layers.conv2d(feat,c,filter_size =[4 , 1],act="relu")
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#feat = layers.pool2d(feat,pool_stride=(3,1),pool_size=(3,1))
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#src_word = layers.squeeze(feat,axes=[2]) #src_word [-1,c,ww]
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#feat = layers.transpose(feat, [0,3,1,2])
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#src_word = layers.reshape(feat,[-1,w, c*h])
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#src_word = layers.im2sequence(
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# input=feat,
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# stride=[1, 1],
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# filter_size=[feat.shape[2], 1])
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#layers.Print(src_word,message="src_word",summarize=10)
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# print('feat',feat)
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#print("src_word",src_word)
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enc_input = prepare_decoder(
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src_word,
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src_pos,
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@ -811,248 +483,3 @@ def wrap_encoder(src_vocab_size,
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|||
preprocess_cmd,
|
||||
postprocess_cmd, )
|
||||
return enc_output
|
||||
|
||||
|
||||
def wrap_decoder(trg_vocab_size,
|
||||
max_length,
|
||||
n_layer,
|
||||
n_head,
|
||||
d_key,
|
||||
d_value,
|
||||
d_model,
|
||||
d_inner_hid,
|
||||
prepostprocess_dropout,
|
||||
attention_dropout,
|
||||
relu_dropout,
|
||||
preprocess_cmd,
|
||||
postprocess_cmd,
|
||||
weight_sharing,
|
||||
dec_inputs=None,
|
||||
enc_output=None,
|
||||
caches=None,
|
||||
gather_idx=None,
|
||||
bos_idx=0):
|
||||
"""
|
||||
The wrapper assembles together all needed layers for the decoder.
|
||||
"""
|
||||
if dec_inputs is None:
|
||||
# This is used to implement independent decoder program in inference.
|
||||
trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias, enc_output = \
|
||||
make_all_inputs(decoder_data_input_fields)
|
||||
else:
|
||||
trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias = dec_inputs
|
||||
|
||||
dec_input = prepare_decoder(
|
||||
trg_word,
|
||||
trg_pos,
|
||||
trg_vocab_size,
|
||||
d_model,
|
||||
max_length,
|
||||
prepostprocess_dropout,
|
||||
bos_idx=bos_idx,
|
||||
word_emb_param_name="src_word_emb_table"
|
||||
if weight_sharing else "trg_word_emb_table")
|
||||
dec_output = decoder(
|
||||
dec_input,
|
||||
enc_output,
|
||||
trg_slf_attn_bias,
|
||||
trg_src_attn_bias,
|
||||
n_layer,
|
||||
n_head,
|
||||
d_key,
|
||||
d_value,
|
||||
d_model,
|
||||
d_inner_hid,
|
||||
prepostprocess_dropout,
|
||||
attention_dropout,
|
||||
relu_dropout,
|
||||
preprocess_cmd,
|
||||
postprocess_cmd,
|
||||
caches=caches,
|
||||
gather_idx=gather_idx)
|
||||
return dec_output
|
||||
# Reshape to 2D tensor to use GEMM instead of BatchedGEMM
|
||||
dec_output = layers.reshape(
|
||||
dec_output, shape=[-1, dec_output.shape[-1]], inplace=True)
|
||||
if weight_sharing:
|
||||
predict = layers.matmul(
|
||||
x=dec_output,
|
||||
y=fluid.default_main_program().global_block().var(
|
||||
"trg_word_emb_table"),
|
||||
transpose_y=True)
|
||||
else:
|
||||
predict = layers.fc(input=dec_output,
|
||||
size=trg_vocab_size,
|
||||
bias_attr=False)
|
||||
if dec_inputs is None:
|
||||
# Return probs for independent decoder program.
|
||||
predict = layers.softmax(predict)
|
||||
return predict
|
||||
|
||||
|
||||
def fast_decode(src_vocab_size,
|
||||
trg_vocab_size,
|
||||
max_in_len,
|
||||
n_layer,
|
||||
n_head,
|
||||
d_key,
|
||||
d_value,
|
||||
d_model,
|
||||
d_inner_hid,
|
||||
prepostprocess_dropout,
|
||||
attention_dropout,
|
||||
relu_dropout,
|
||||
preprocess_cmd,
|
||||
postprocess_cmd,
|
||||
weight_sharing,
|
||||
beam_size,
|
||||
max_out_len,
|
||||
bos_idx,
|
||||
eos_idx,
|
||||
use_py_reader=False):
|
||||
"""
|
||||
Use beam search to decode. Caches will be used to store states of history
|
||||
steps which can make the decoding faster.
|
||||
"""
|
||||
data_input_names = encoder_data_input_fields + fast_decoder_data_input_fields
|
||||
|
||||
if use_py_reader:
|
||||
all_inputs, reader = make_all_py_reader_inputs(data_input_names)
|
||||
else:
|
||||
all_inputs = make_all_inputs(data_input_names)
|
||||
|
||||
enc_inputs_len = len(encoder_data_input_fields)
|
||||
dec_inputs_len = len(fast_decoder_data_input_fields)
|
||||
enc_inputs = all_inputs[0:enc_inputs_len] #enc_inputs tensor
|
||||
dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len +
|
||||
dec_inputs_len] #dec_inputs tensor
|
||||
|
||||
enc_output = wrap_encoder(
|
||||
src_vocab_size,
|
||||
64, ##to do !!!!!????
|
||||
n_layer,
|
||||
n_head,
|
||||
d_key,
|
||||
d_value,
|
||||
d_model,
|
||||
d_inner_hid,
|
||||
prepostprocess_dropout,
|
||||
attention_dropout,
|
||||
relu_dropout,
|
||||
preprocess_cmd,
|
||||
postprocess_cmd,
|
||||
weight_sharing,
|
||||
enc_inputs,
|
||||
bos_idx=bos_idx)
|
||||
start_tokens, init_scores, parent_idx, trg_src_attn_bias = dec_inputs
|
||||
|
||||
def beam_search():
|
||||
max_len = layers.fill_constant(
|
||||
shape=[1],
|
||||
dtype=start_tokens.dtype,
|
||||
value=max_out_len,
|
||||
force_cpu=True)
|
||||
step_idx = layers.fill_constant(
|
||||
shape=[1], dtype=start_tokens.dtype, value=0, force_cpu=True)
|
||||
cond = layers.less_than(x=step_idx, y=max_len) # default force_cpu=True
|
||||
while_op = layers.While(cond)
|
||||
# array states will be stored for each step.
|
||||
ids = layers.array_write(
|
||||
layers.reshape(start_tokens, (-1, 1)), step_idx)
|
||||
scores = layers.array_write(init_scores, step_idx)
|
||||
# cell states will be overwrited at each step.
|
||||
# caches contains states of history steps in decoder self-attention
|
||||
# and static encoder output projections in encoder-decoder attention
|
||||
# to reduce redundant computation.
|
||||
caches = [
|
||||
{
|
||||
"k": # for self attention
|
||||
layers.fill_constant_batch_size_like(
|
||||
input=start_tokens,
|
||||
shape=[-1, n_head, 0, d_key],
|
||||
dtype=enc_output.dtype,
|
||||
value=0),
|
||||
"v": # for self attention
|
||||
layers.fill_constant_batch_size_like(
|
||||
input=start_tokens,
|
||||
shape=[-1, n_head, 0, d_value],
|
||||
dtype=enc_output.dtype,
|
||||
value=0),
|
||||
"static_k": # for encoder-decoder attention
|
||||
layers.create_tensor(dtype=enc_output.dtype),
|
||||
"static_v": # for encoder-decoder attention
|
||||
layers.create_tensor(dtype=enc_output.dtype)
|
||||
} for i in range(n_layer)
|
||||
]
|
||||
|
||||
with while_op.block():
|
||||
pre_ids = layers.array_read(array=ids, i=step_idx)
|
||||
# Since beam_search_op dosen't enforce pre_ids' shape, we can do
|
||||
# inplace reshape here which actually change the shape of pre_ids.
|
||||
pre_ids = layers.reshape(pre_ids, (-1, 1, 1), inplace=True)
|
||||
pre_scores = layers.array_read(array=scores, i=step_idx)
|
||||
# gather cell states corresponding to selected parent
|
||||
pre_src_attn_bias = layers.gather(
|
||||
trg_src_attn_bias, index=parent_idx)
|
||||
pre_pos = layers.elementwise_mul(
|
||||
x=layers.fill_constant_batch_size_like(
|
||||
input=pre_src_attn_bias, # cann't use lod tensor here
|
||||
value=1,
|
||||
shape=[-1, 1, 1],
|
||||
dtype=pre_ids.dtype),
|
||||
y=step_idx,
|
||||
axis=0)
|
||||
logits = wrap_decoder(
|
||||
trg_vocab_size,
|
||||
max_in_len,
|
||||
n_layer,
|
||||
n_head,
|
||||
d_key,
|
||||
d_value,
|
||||
d_model,
|
||||
d_inner_hid,
|
||||
prepostprocess_dropout,
|
||||
attention_dropout,
|
||||
relu_dropout,
|
||||
preprocess_cmd,
|
||||
postprocess_cmd,
|
||||
weight_sharing,
|
||||
dec_inputs=(pre_ids, pre_pos, None, pre_src_attn_bias),
|
||||
enc_output=enc_output,
|
||||
caches=caches,
|
||||
gather_idx=parent_idx,
|
||||
bos_idx=bos_idx)
|
||||
# intra-beam topK
|
||||
topk_scores, topk_indices = layers.topk(
|
||||
input=layers.softmax(logits), k=beam_size)
|
||||
accu_scores = layers.elementwise_add(
|
||||
x=layers.log(topk_scores), y=pre_scores, axis=0)
|
||||
# beam_search op uses lod to differentiate branches.
|
||||
accu_scores = layers.lod_reset(accu_scores, pre_ids)
|
||||
# topK reduction across beams, also contain special handle of
|
||||
# end beams and end sentences(batch reduction)
|
||||
selected_ids, selected_scores, gather_idx = layers.beam_search(
|
||||
pre_ids=pre_ids,
|
||||
pre_scores=pre_scores,
|
||||
ids=topk_indices,
|
||||
scores=accu_scores,
|
||||
beam_size=beam_size,
|
||||
end_id=eos_idx,
|
||||
return_parent_idx=True)
|
||||
layers.increment(x=step_idx, value=1.0, in_place=True)
|
||||
# cell states(caches) have been updated in wrap_decoder,
|
||||
# only need to update beam search states here.
|
||||
layers.array_write(selected_ids, i=step_idx, array=ids)
|
||||
layers.array_write(selected_scores, i=step_idx, array=scores)
|
||||
layers.assign(gather_idx, parent_idx)
|
||||
layers.assign(pre_src_attn_bias, trg_src_attn_bias)
|
||||
length_cond = layers.less_than(x=step_idx, y=max_len)
|
||||
finish_cond = layers.logical_not(layers.is_empty(x=selected_ids))
|
||||
layers.logical_and(x=length_cond, y=finish_cond, out=cond)
|
||||
|
||||
finished_ids, finished_scores = layers.beam_search_decode(
|
||||
ids, scores, beam_size=beam_size, end_id=eos_idx)
|
||||
return finished_ids, finished_scores
|
||||
|
||||
finished_ids, finished_scores = beam_search()
|
||||
return finished_ids, finished_scores, reader if use_py_reader else None
|
||||
|
|
|
|||
Loading…
Reference in New Issue