def __init__(self, args, kernel_size=0):
super().__init__()
self.embed_dim = args.encoder_embed_dim
self.conv_dim = args.encoder_conv_dim
padding_l = kernel_size // 2 if kernel_size % 2 == 1 else ((kernel_size - 1) // 2, kernel_size // 2)
if args.encoder_glu:
self.linear1 = Linear(self.embed_dim, 2*self.conv_dim)
self.act = nn.GLU()
else:
self.linear1 = Linear(self.embed_dim, self.conv_dim)
self.act = None
if args.encoder_conv_type == 'lightweight':
self.conv = LightweightConv1dTBC(self.conv_dim, kernel_size, padding_l=padding_l,
weight_softmax=args.weight_softmax,
num_heads=args.encoder_attention_heads,
weight_dropout=args.weight_dropout)
elif args.encoder_conv_type == 'dynamic':
self.conv = DynamicConv1dTBC(self.conv_dim, kernel_size, padding_l=padding_l,
weight_softmax=args.weight_softmax,
num_heads=args.encoder_attention_heads,
weight_dropout=args.weight_dropout)
else:
raise NotImplementedError
self.linear2 = Linear(self.conv_dim, self.embed_dim)
self.dropout = args.dropout
self.relu_dropout = args.relu_dropout
self.input_dropout = args.input_dropout
self.normalize_before = args.encoder_normalize_before
self.fc1 = Linear(self.embed_dim, args.encoder_ffn_embed_dim)
self.fc2 = Linear(args.encoder_ffn_embed_dim, self.embed_dim)
self.layer_norms = nn.ModuleList([LayerNorm(self.embed_dim) for _ in range(2)])
def __init__(self, args, no_encoder_attn=False, kernel_size=0):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.conv_dim = args.decoder_conv_dim
if args.decoder_glu:
self.linear1 = Linear(self.embed_dim, 2 * self.conv_dim)
self.act = nn.GLU()
else:
self.linear1 = Linear(self.embed_dim, self.conv_dim)
self.act = None
if args.decoder_conv_type == 'lightweight':
self.conv = LightweightConv1dTBC(
self.conv_dim,
kernel_size,
padding_l=kernel_size - 1,
weight_softmax=args.weight_softmax,
num_heads=args.decoder_attention_heads,
weight_dropout=args.weight_dropout)
elif args.decoder_conv_type == 'dynamic':
self.conv = DynamicConv1dTBC(
self.conv_dim,
kernel_size,
padding_l=kernel_size - 1,
weight_softmax=args.weight_softmax,
num_heads=args.decoder_attention_heads,
weight_dropout=args.weight_dropout)
else:
raise NotImplementedError
self.linear2 = Linear(self.conv_dim, self.embed_dim)
self.dropout = args.dropout
self.relu_dropout = args.relu_dropout
self.input_dropout = args.input_dropout
self.normalize_before = args.decoder_normalize_before
self.conv_layer_norm = LayerNorm(self.embed_dim)
if no_encoder_attn:
self.encoder_attn = None
self.encoder_attn_layer_norm = None
else:
self.encoder_attn = MultiheadAttention(
self.embed_dim,
args.decoder_attention_heads,
dropout=args.attention_dropout,
encoder_decoder_attention=True)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
self.fc1 = Linear(self.embed_dim, args.decoder_ffn_embed_dim)
self.fc2 = Linear(args.decoder_ffn_embed_dim, self.embed_dim)
self.final_layer_norm = LayerNorm(self.embed_dim)
self.need_attn = True
def __init__(self, args):
super(LightweightConvolution, self).__init__()
self.first_kernel = args.kernel_size
self.first_padding = args.kernel_padding
self.conv_layer_1 = LightweightConv1dTBC(
input_size=args.hidden_dim * 2,
kernel_size=self.first_kernel,
padding_l=self.first_padding,
num_heads=args.num_head,
weight_dropout=args.conv_dropout,
weight_softmax=args.kernel_softmax)
self.conv_layer_2 = LightweightConv1dTBC(
input_size=args.hidden_dim * 2,
kernel_size=self.first_kernel,
padding_l=self.first_padding,
num_heads=args.num_head,
weight_dropout=args.conv_dropout,
weight_softmax=args.kernel_softmax)
self.conv_layer_3 = LightweightConv1dTBC(
input_size=args.hidden_dim * 2,
kernel_size=self.first_kernel,
padding_l=self.first_padding,
num_heads=int(args.num_head),
weight_dropout=args.conv_dropout,
weight_softmax=args.kernel_softmax)
self.conv_layer_4 = LightweightConv1dTBC(
input_size=args.hidden_dim * 2,
kernel_size=args.kernel_size,
padding_l=args.kernel_padding,
num_heads=int(args.num_head),
weight_dropout=args.conv_dropout,
weight_softmax=args.kernel_softmax)
self.conv_layer_5 = LightweightConv1dTBC(
input_size=args.hidden_dim * 2,
kernel_size=args.kernel_size,
padding_l=args.kernel_padding,
num_heads=int(args.num_head),
weight_dropout=args.conv_dropout,
weight_softmax=args.kernel_softmax)
self.conv_layer_6 = LightweightConv1dTBC(
input_size=args.hidden_dim * 2,
kernel_size=args.kernel_size,
padding_l=args.kernel_padding,
num_heads=int(args.num_head),
weight_dropout=args.conv_dropout,
weight_softmax=args.kernel_softmax)
self.relu = nn.ReLU()