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from typing import List, Optional, Tuple, Union
import torch
from torch import Tensor, nn
from .attention import CompactRelPositionalEncoding
from .scaling import ScheduledFloat
class Zipformer2Encoder(nn.Module):
r"""Zipformer2Encoder is a stack of N encoder layers
Args:
encoder_layer: an instance of the Zipformer2EncoderLayer() class (required).
num_layers: the number of sub-encoder-layers in the encoder (required).
pos_dim: the dimension for the relative positional encoding
Examples::
>>> encoder_layer = Zipformer2EncoderLayer(embed_dim=512, nhead=8)
>>> zipformer_encoder = Zipformer2Encoder(encoder_layer, num_layers=6)
>>> src = torch.rand(10, 32, 512)
>>> out = zipformer_encoder(src)
"""
def __init__(
self,
encoder_layer: nn.Module,
num_layers: int,
pos_dim: int,
dropout: float,
warmup_begin: float,
warmup_end: float,
initial_layerdrop_rate: float = 0.5,
final_layerdrop_rate: float = 0.05,
) -> None:
super().__init__()
self.encoder_pos = CompactRelPositionalEncoding(pos_dim, dropout_rate=0.15, length_factor=1.0)
self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for i in range(num_layers)])
self.num_layers = num_layers
assert 0 <= warmup_begin <= warmup_end
delta = (1.0 / num_layers) * (warmup_end - warmup_begin)
cur_begin = warmup_begin # interpreted as a training batch index
for i in range(num_layers):
cur_end = cur_begin + delta
self.layers[i].bypass.skip_rate = ScheduledFloat(
(cur_begin, initial_layerdrop_rate),
(cur_end, final_layerdrop_rate),
default=0.0,
)
cur_begin = cur_end
def forward(
self,
src: Tensor,
chunk_size: int = -1,
feature_mask: Union[Tensor, float] = 1.0,
attn_mask: Optional[Tensor] = None,
src_key_padding_mask: Optional[Tensor] = None,
) -> Tensor:
r"""Pass the input through the encoder layers in turn.
Args:
src: the sequence to the encoder (required): shape (seq_len, batch_size, embedding_dim).
chunk_size: the number of frames per chunk, of >= 0; if -1, no chunking.
feature_mask: something that broadcasts with src, that we'll multiply `src`
by at every layer: if a Tensor, likely of shape (seq_len, batch_size, embedding_dim)
attn_mask: the attention mask, of shape (batch_size, seq_len, seq_len) or (seq_len, seq_len),
interpreted as (batch_size, tgt_seq_len, src_seq_len) or (tgt_seq_len, src_seq_len).
True means masked position. May be None.
src_key_padding_mask: the mask for padding, of shape (batch_size, seq_len); True means
masked position. May be None.
Returns: a Tensor with the same shape as src.
"""
pos_emb = self.encoder_pos(src)
output = src
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
output = output * feature_mask
for i, mod in enumerate(self.layers):
output = mod(
output,
pos_emb,
chunk_size=chunk_size,
attn_mask=attn_mask,
src_key_padding_mask=src_key_padding_mask,
)
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
output = output * feature_mask
return output
def streaming_forward(
self,
src: Tensor,
states: List[Tensor],
left_context_len: int,
src_key_padding_mask: Tensor,
) -> Tuple[Tensor, List[Tensor]]:
r"""Pass the input through the encoder layers in turn.
Args:
src: the sequence to the encoder (required): shape (seq_len, batch_size, embedding_dim).
states: list of cached tensors of N encoder layers. For layer-i, states[i*6:(i+1)*6] is
(cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2).
left_context_len: Number of left context frames.
src_key_padding_mask: the mask for padding, of shape
(batch_size, left_context_len + seq_len); True means masked position.
May be None.
Returns:
- output, a Tensor with the same shape as src.
- updated states
"""
pos_emb = self.encoder_pos(src, left_context_len)
output = src
new_states = []
for i, mod in enumerate(self.layers):
(
cached_key,
cached_nonlin_attn,
cached_val1,
cached_val2,
cached_conv1,
cached_conv2,
) = states[i * 6 : (i + 1) * 6]
(
output,
new_cached_key,
new_cached_nonlin_attn,
new_cached_val1,
new_cached_val2,
new_cached_conv1,
new_cached_conv2,
) = mod.streaming_forward(
output,
pos_emb,
cached_key=cached_key,
cached_nonlin_attn=cached_nonlin_attn,
cached_val1=cached_val1,
cached_val2=cached_val2,
cached_conv1=cached_conv1,
cached_conv2=cached_conv2,
left_context_len=left_context_len,
src_key_padding_mask=src_key_padding_mask,
)
new_states += [
new_cached_key,
new_cached_nonlin_attn,
new_cached_val1,
new_cached_val2,
new_cached_conv1,
new_cached_conv2,
]
return output, new_states
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