57 lines
2.6 KiB
Python
57 lines
2.6 KiB
Python
import paddle.fluid.dygraph as dg
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import paddle.fluid as fluid
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from parakeet.models.transformer_tts.utils import *
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from parakeet.modules.multihead_attention import MultiheadAttention
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from parakeet.modules.ffn import PositionwiseFeedForward
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from parakeet.models.transformer_tts.encoderprenet import EncoderPrenet
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class Encoder(dg.Layer):
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def __init__(self, embedding_size, num_hidden, num_head=4):
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super(Encoder, self).__init__()
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self.num_hidden = num_hidden
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param = fluid.ParamAttr(initializer=fluid.initializer.Constant(value=1.0))
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self.alpha = self.create_parameter(shape=(1, ), attr=param, dtype='float32')
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self.pos_inp = get_sinusoid_encoding_table(1024, self.num_hidden, padding_idx=0)
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self.pos_emb = dg.Embedding(size=[1024, num_hidden],
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padding_idx=0,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.NumpyArrayInitializer(self.pos_inp),
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trainable=False))
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self.encoder_prenet = EncoderPrenet(embedding_size = embedding_size,
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num_hidden = num_hidden,
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use_cudnn=True)
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self.layers = [MultiheadAttention(num_hidden, num_hidden//num_head, num_hidden//num_head) for _ in range(3)]
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for i, layer in enumerate(self.layers):
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self.add_sublayer("self_attn_{}".format(i), layer)
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self.ffns = [PositionwiseFeedForward(num_hidden, num_hidden*num_head, filter_size=1, use_cudnn = True) for _ in range(3)]
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for i, layer in enumerate(self.ffns):
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self.add_sublayer("ffns_{}".format(i), layer)
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def forward(self, x, positional):
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if fluid.framework._dygraph_tracer()._train_mode:
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query_mask = get_non_pad_mask(positional)
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mask = get_attn_key_pad_mask(positional, x)
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else:
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query_mask, mask = None, None
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# Encoder pre_network
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x = self.encoder_prenet(x) #(N,T,C)
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# Get positional encoding
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positional = self.pos_emb(positional)
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x = positional * self.alpha + x #(N, T, C)
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# Positional dropout
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x = layers.dropout(x, 0.1)
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# Self attention encoder
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attentions = list()
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for layer, ffn in zip(self.layers, self.ffns):
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x, attention = layer(x, x, x, mask = mask, query_mask = query_mask)
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x = ffn(x)
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attentions.append(attention)
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return x, query_mask, attentions |