def __init__(self, args, no_encoder_attn=False):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.dropout = args.dropout
self.relu_dropout = args.relu_dropout
self.more_dropouts = args.decoder_aan_more_dropouts
if args.decoder_attn_window_size <= 0:
self.avg_attn = AverageAttention(self.embed_dim,
dropout=args.attention_dropout)
else:
self.avg_attn = AverageWindowAttention(
self.embed_dim,
dropout=args.attention_dropout,
window_size=args.decoder_attn_window_size,
)
# self.activation = getattr(args, "decoder_ffn_activation", "relu")
self.aan_layer_norm = LayerNorm(self.embed_dim)
if args.no_decoder_aan_ffn:
self.aan_ffn = None
else:
aan_ffn_hidden_dim = (args.decoder_ffn_embed_dim
if args.decoder_aan_ffn_use_embed_dim else
args.decoder_ffn_embed_dim)
self.aan_ffn = FeedForwardNetwork(
self.embed_dim,
aan_ffn_hidden_dim,
self.embed_dim,
num_layers=2,
dropout=args.relu_dropout,
)
if args.no_decoder_aan_gating:
self.aan_gating_fc = None
else:
self.aan_gating_fc = Linear(self.embed_dim * 2, self.embed_dim * 2)
self.normalize_before = args.decoder_normalize_before
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,
kdim=args.encoder_embed_dim,
vdim=args.encoder_embed_dim,
dropout=args.attention_dropout,
encoder_decoder_attention=True,
)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
self.ffn = FeedForwardNetwork(
self.embed_dim,
args.decoder_ffn_embed_dim,
self.embed_dim,
num_layers=2,
dropout=args.relu_dropout,
)
self.final_layer_norm = LayerNorm(self.embed_dim)
self.need_attn = True
self.onnx_trace = False
class TransformerAANDecoderLayer(nn.Module):
"""Decoder layer block.
In the original paper each operation (multi-head attention, encoder
attention or FFN) is postprocessed with: `dropout -> add residual ->
layernorm`. In the tensor2tensor code they suggest that learning is more
robust when preprocessing each layer with layernorm and postprocessing with:
`dropout -> add residual`. We default to the approach in the paper, but the
tensor2tensor approach can be enabled by setting
*args.decoder_normalize_before* to ``True``.
Args:
args (argparse.Namespace): parsed command-line arguments
no_encoder_attn (bool, optional): whether to attend to encoder outputs.
Default: ``False``
"""
def __init__(self, args, no_encoder_attn=False):
super().__init__()
self.embed_dim = args.decoder_embed_dim
self.dropout = args.dropout
self.relu_dropout = args.relu_dropout
self.more_dropouts = args.decoder_aan_more_dropouts
if args.decoder_attn_window_size <= 0:
self.avg_attn = AverageAttention(self.embed_dim,
dropout=args.attention_dropout)
else:
self.avg_attn = AverageWindowAttention(
self.embed_dim,
dropout=args.attention_dropout,
window_size=args.decoder_attn_window_size,
)
# self.activation = getattr(args, "decoder_ffn_activation", "relu")
self.aan_layer_norm = LayerNorm(self.embed_dim)
if args.no_decoder_aan_ffn:
self.aan_ffn = None
else:
aan_ffn_hidden_dim = (args.decoder_ffn_embed_dim
if args.decoder_aan_ffn_use_embed_dim else
args.decoder_ffn_embed_dim)
self.aan_ffn = FeedForwardNetwork(
self.embed_dim,
aan_ffn_hidden_dim,
self.embed_dim,
num_layers=2,
dropout=args.relu_dropout,
)
if args.no_decoder_aan_gating:
self.aan_gating_fc = None
else:
self.aan_gating_fc = Linear(self.embed_dim * 2, self.embed_dim * 2)
self.normalize_before = args.decoder_normalize_before
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,
kdim=args.encoder_embed_dim,
vdim=args.encoder_embed_dim,
dropout=args.attention_dropout,
encoder_decoder_attention=True,
)
self.encoder_attn_layer_norm = LayerNorm(self.embed_dim)
self.ffn = FeedForwardNetwork(
self.embed_dim,
args.decoder_ffn_embed_dim,
self.embed_dim,
num_layers=2,
dropout=args.relu_dropout,
)
self.final_layer_norm = LayerNorm(self.embed_dim)
self.need_attn = True
self.onnx_trace = False
def prepare_for_onnx_export_(self):
self.onnx_trace = True
def forward(
self,
x,
encoder_out,
encoder_padding_mask,
incremental_state,
prev_self_attn_state=None,
prev_attn_state=None,
self_attn_mask=None,
self_attn_padding_mask=None,
):
"""
Args:
x (Tensor): input to the layer of shape `(seq_len, batch, embed_dim)`
encoder_padding_mask (ByteTensor): binary ByteTensor of shape
`(batch, src_len)` where padding elements are indicated by ``1``.
Returns:
encoded output of shape `(batch, src_len, embed_dim)`
"""
residual = x
if "residual" in self.more_dropouts:
residual = F.dropout(residual,
p=self.dropout,
training=self.training)
if prev_self_attn_state is not None:
if incremental_state is None:
incremental_state = {}
prev_key, prev_value = prev_self_attn_state
saved_state = {"prev_key": prev_key, "prev_value": prev_value}
self.avg_attn._set_input_buffer(incremental_state, saved_state)
x, _ = self.avg_attn(
value=x,
mask_future_timesteps=True,
incremental_state=incremental_state,
mask_trick=self.training,
)
if "after_avg" in self.more_dropouts:
x = F.dropout(x, p=self.dropout, training=self.training)
if self.aan_layer_norm is not None:
x = self.maybe_layer_norm(self.aan_layer_norm, x, before=True)
if self.aan_ffn is not None:
x = self.aan_ffn(x)
if "after_ffn" in self.more_dropouts:
x = F.dropout(x, p=self.dropout, training=self.training)
if self.aan_gating_fc is not None:
i, f = self.aan_gating_fc(torch.cat([residual, x],
dim=-1)).chunk(2, dim=-1)
x = torch.sigmoid(f) * residual + torch.sigmoid(i) * x
if "after_gating" in self.more_dropouts:
x = F.dropout(x, p=self.dropout, training=self.training)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
if self.aan_layer_norm is not None:
x = self.maybe_layer_norm(self.aan_layer_norm, x, after=True)
attn = None
if self.encoder_attn is not None:
residual = x
x = self.maybe_layer_norm(self.encoder_attn_layer_norm,
x,
before=True)
if prev_attn_state is not None:
if incremental_state is None:
incremental_state = {}
prev_key, prev_value = prev_attn_state
saved_state = {"prev_key": prev_key, "prev_value": prev_value}
self.encoder_attn._set_input_buffer(incremental_state,
saved_state)
x, attn = self.encoder_attn(
query=x,
key=encoder_out,
value=encoder_out,
key_padding_mask=encoder_padding_mask,
incremental_state=incremental_state,
static_kv=True,
need_weights=(not self.training and self.need_attn),
)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.encoder_attn_layer_norm,
x,
after=True)
residual = x
x = self.maybe_layer_norm(self.final_layer_norm, x, before=True)
x = self.ffn(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = residual + x
x = self.maybe_layer_norm(self.final_layer_norm, x, after=True)
return x, attn
def maybe_layer_norm(self, layer_norm, x, before=False, after=False):
assert before ^ after
if after ^ self.normalize_before:
return layer_norm(x)
else:
return x
def make_generation_fast_(self, need_attn=False, **kwargs):
self.need_attn = need_attn
def extra_repr(self):
return "dropout={}, more_dropouts={}".format(self.dropout,
self.more_dropouts)