def __init__(
self,
input_dim: int,
hidden_dim: int,
attention_projection_dim: int,
feedforward_hidden_dim: int,
num_convs: int,
conv_kernel_size: int,
num_attention_heads: int,
use_positional_encoding: bool = True,
dropout_prob: float = 0.1,
layer_dropout_undecayed_prob: float = 0.1,
attention_dropout_prob: float = 0,
) -> None:
super().__init__()
check_dimensions_match(input_dim, hidden_dim, "input_dim", "hidden_dim")
self._use_positional_encoding = use_positional_encoding
self._conv_norm_layers = torch.nn.ModuleList(
[LayerNorm(hidden_dim) for _ in range(num_convs)]
)
self._conv_layers = torch.nn.ModuleList()
for _ in range(num_convs):
padding = torch.nn.ConstantPad1d(
(conv_kernel_size // 2, (conv_kernel_size - 1) // 2), 0
)
depthwise_conv = torch.nn.Conv1d(
hidden_dim, hidden_dim, conv_kernel_size, groups=hidden_dim
)
pointwise_conv = torch.nn.Conv1d(hidden_dim, hidden_dim, 1)
self._conv_layers.append(
torch.nn.Sequential(
padding, depthwise_conv, pointwise_conv, Activation.by_name("relu")()
)
)
self.attention_norm_layer = LayerNorm(hidden_dim)
self.attention_layer = MultiHeadSelfAttention(
num_heads=num_attention_heads,
input_dim=hidden_dim,
attention_dim=attention_projection_dim,
values_dim=attention_projection_dim,
attention_dropout_prob=attention_dropout_prob,
)
self.feedforward_norm_layer = LayerNorm(hidden_dim)
self.feedforward = FeedForward(
hidden_dim,
activations=[Activation.by_name("relu")(), Activation.by_name("linear")()],
hidden_dims=[feedforward_hidden_dim, hidden_dim],
num_layers=2,
dropout=dropout_prob,
)
self.dropout = Dropout(dropout_prob)
self.residual_with_layer_dropout = ResidualWithLayerDropout(layer_dropout_undecayed_prob)
self._input_dim = input_dim
self._output_dim = hidden_dim
def __init__(
self,
input_dim: int,
hidden_dim: int,
attention_projection_dim: int,
feedforward_hidden_dim: int,
num_convs: int,
conv_kernel_size: int,
num_attention_heads: int,
num_semantic_labels: int,
replace_zero_semantic_labels_with_per_head_labels: bool = True,
use_positional_encoding: bool = True,
dropout_prob: float = 0.1,
layer_dropout_undecayed_prob: float = 0.1,
attention_dropout_prob: float = 0,
semantic_integration_mode: str = "projection",
semantic_emb_dim: int = 0,
use_semantic_views: bool = True,
multi_head_attention_batch_computation: bool = False,
use_separate_label_embeddings_for_q_and_k: bool = True) -> None:
super().__init__()
self.return_output_meta_is_supported = True
check_dimensions_match(input_dim, hidden_dim, 'input_dim',
'hidden_dim')
self._use_positional_encoding = use_positional_encoding
self._replace_zero_semantic_labels_with_per_head_labels = replace_zero_semantic_labels_with_per_head_labels
self._conv_norm_layers = torch.nn.ModuleList(
[LayerNorm(hidden_dim) for _ in range(num_convs)])
self._conv_layers = torch.nn.ModuleList()
if semantic_integration_mode not in semantic_integration_mode_supported:
raise Exception(
"semantic_integration_mode must be in [{0}] but is `{1}`".
format(", ".join(semantic_integration_mode_supported),
semantic_integration_mode))
self._semantic_integration_mode = semantic_integration_mode
self._use_separate_label_embeddings_for_q_and_k = use_separate_label_embeddings_for_q_and_k
for _ in range(num_convs):
padding = torch.nn.ConstantPad1d(
(conv_kernel_size // 2, (conv_kernel_size - 1) // 2), 0)
depthwise_conv = torch.nn.Conv1d(hidden_dim,
hidden_dim,
conv_kernel_size,
groups=hidden_dim)
pointwise_conv = torch.nn.Conv1d(hidden_dim, hidden_dim, 1)
self._conv_layers.append(
torch.nn.Sequential(padding, depthwise_conv, pointwise_conv,
Activation.by_name("relu")()))
self.attention_norm_layer = LayerNorm(hidden_dim)
self.num_semantic_labels = num_semantic_labels
self.num_attention_heads = num_attention_heads
self.attention_layer = MultiHeadSemanticFlatConcatSelfAttention(
num_heads=num_attention_heads,
num_semantic_labels=num_semantic_labels,
input_dim=hidden_dim,
attention_dim=attention_projection_dim,
values_dim=attention_projection_dim,
attention_dropout_prob=attention_dropout_prob,
semantic_integration_mode=semantic_integration_mode,
semantic_emb_dim=semantic_emb_dim,
use_semantic_views=use_semantic_views,
multi_head_attention_batch_computation=
multi_head_attention_batch_computation,
use_separate_label_embeddings_for_q_and_k=
use_separate_label_embeddings_for_q_and_k)
self.feedforward_norm_layer = LayerNorm(hidden_dim)
self.feedforward = FeedForward(
hidden_dim,
activations=[
Activation.by_name('relu')(),
Activation.by_name('linear')()
],
hidden_dims=[feedforward_hidden_dim, hidden_dim],
num_layers=2,
dropout=dropout_prob)
self.dropout = Dropout(dropout_prob)
self.residual_with_layer_dropout = ResidualWithLayerDropout(
layer_dropout_undecayed_prob)
self._input_dim = input_dim
self._output_dim = hidden_dim
