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https://github.com/k2-fsa/icefall.git
synced 2025-12-11 06:55:27 +00:00
Code cleanup
This commit is contained in:
Daniel Povey
parent
671e9ee5bd
commit
0a76215fd7
@ -58,9 +58,6 @@ class Subformer(EncoderInterface):
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encoder_dim (Tuple[int]): embedding dimension of each of the encoder stacks, one per
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encoder stack.
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num_encoder_layers (int or Tuple[int])): number of encoder layers for each stack
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encoder_unmasked_dim (int or Tuple[int]): unmasked dimension in each of
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the encoder stacks for purposes of per-frame dropout (recommend 256 for
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now).
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query_head_dim (int or Tuple[int]): dimension of query and key per attention
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head: per stack, if a tuple..
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value_head_dim (int or Tuple[int]): dimension of value in each attention head
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@ -83,11 +80,8 @@ class Subformer(EncoderInterface):
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"""
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def __init__(
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self,
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output_downsampling_factor: int = 2,
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downsampling_factor: Tuple[int] = (2, 4),
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encoder_dim: Union[int, Tuple[int]] = 384,
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encoder_dim: Union[int, Tuple[int]] = (384, 512, 384),
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num_encoder_layers: Union[int, Tuple[int]] = 4,
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encoder_unmasked_dim: Union[int, Tuple[int]] = 256,
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query_head_dim: Union[int, Tuple[int]] = 24,
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value_head_dim: Union[int, Tuple[int]] = 12,
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num_heads: Union[int, Tuple[int]] = 8,
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@ -106,19 +100,16 @@ class Subformer(EncoderInterface):
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def _to_tuple(x):
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""" Converts a single int or a 1-tuple of an int to a tuple with the same length
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as downsampling_factor"""
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as encoder_dim"""
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if isinstance(x, int):
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x = (x,)
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if len(x) == 1:
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x = x * len(downsampling_factor)
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x = x * len(encoder_dim)
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else:
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assert len(x) == len(downsampling_factor) and isinstance(x[0], int)
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assert len(x) == len(encoder_dim) and isinstance(x[0], int)
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return x
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self.output_downsampling_factor = output_downsampling_factor # int
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self.downsampling_factor = downsampling_factor # tuple
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self.encoder_dim = encoder_dim = _to_tuple(encoder_dim) # tuple
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self.encoder_unmasked_dim = encoder_unmasked_dim = _to_tuple(encoder_unmasked_dim) # tuple
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self.encoder_dim = encoder_dim
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num_encoder_layers = _to_tuple(num_encoder_layers)
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query_head_dim = _to_tuple(query_head_dim)
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value_head_dim = _to_tuple(value_head_dim)
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@ -126,13 +117,17 @@ class Subformer(EncoderInterface):
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feedforward_dim = _to_tuple(feedforward_dim)
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self.causal = causal
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for u,d in zip(encoder_unmasked_dim, encoder_dim):
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assert u <= d
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# each one will be SubformerEncoder or DownsampledSubformerEncoder
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encoders = []
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num_encoders = len(downsampling_factor)
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num_encoders = len(encoder_dim)
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assert num_encoders % 2 == 1
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downsampling_factor = [ 1 ]
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while len(downsampling_factor) < num_encoders:
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downsampling_factor = [ 1 ] + [ d * 2 for d in downsampling_factor ] + [ 1 ]
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for i in range(num_encoders):
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encoder_layer = SubformerEncoderLayer(
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@ -158,83 +153,36 @@ class Subformer(EncoderInterface):
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final_layerdrop_rate=0.035 * (downsampling_factor[i] ** 0.5),
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)
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if downsampling_factor[i] != 1:
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encoders.append(encoder)
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mid = len(encoders) // 2
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encoder = DownsampledSubformerEncoder(
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encoder,
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dim=encoder_dim[i],
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downsample=downsampling_factor[i],
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dropout=dropout,
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[ encoders[mid] ],
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input_num_channels=encoder_dim[mid],
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downsample=2
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)
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encoders.append(encoder)
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encoder = encoders[mid]
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for i in range(mid-1, -1, -1):
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this_list = [ encoders[mid-i],
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encoder,
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encoders[mid+i] ]
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encoder = DownsampledSubformerEncoder(
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this_list,
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input_num_channels=encoder_dim[max(0, mid-2)],
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downsample=2,
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)
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self.encoder = encoder
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self.encoder_pos = CompactRelPositionalEncoding(64, pos_dim,
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dropout_rate=0.15,
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length_factor=1.0)
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self.encoders = nn.ModuleList(encoders)
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#self.downsample_output = SimpleDownsample(max(encoder_dim),
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# downsample=output_downsampling_factor,
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# dropout=dropout)
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def get_feature_masks(
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self,
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x: torch.Tensor) -> List[Union[float, Tensor]]:
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"""
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In eval mode, returns [1.0] * num_encoders; in training mode, returns a number of
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randomized feature masks, one per encoder.
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On e.g. 15% of frames, these masks will zero out all enocder dims larger than
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some supplied number, e.g. >256, so in effect on those frames we are using
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a smaller encoer dim.
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We generate the random masks at this level because we want the 2 masks to 'agree'
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all the way up the encoder stack. This will mean that the 1st mask will have
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mask values repeated self.zipformer_subsampling_factor times.
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Args:
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x: the embeddings (needed for the shape and dtype and device), of shape
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(1, batch_size, encoder_dims0)
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"""
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num_encoders = len(self.encoder_dim)
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if not self.training:
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return [ 1.0 ] * num_encoders
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(num_frames0, batch_size, _encoder_dims0) = x.shape
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assert self.encoder_dim[0] == _encoder_dims0
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feature_mask_dropout_prob = 0.125
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# mask1 shape: (1, batch_size, 1)
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mask1 = (torch.rand(1, batch_size, 1,
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device=x.device) >
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feature_mask_dropout_prob).to(x.dtype)
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# mask2 has additional sequences masked, about twice the number.
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mask2 = torch.logical_and(mask1,
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(torch.rand(1, batch_size, 1,
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device=x.device) >
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feature_mask_dropout_prob).to(x.dtype))
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# dim: (1, batch_size, 2)
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mask = torch.cat((mask1, mask2), dim=-1)
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feature_masks = []
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for i in range(num_encoders):
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channels = self.encoder_dim[i]
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feature_mask = torch.ones(1, batch_size, channels,
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dtype=x.dtype, device=x.device)
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u1 = self.encoder_unmasked_dim[i]
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u2 = u1 + (channels - u1) // 2
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feature_mask[:, :, u1:u2] *= mask[..., 0:1]
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feature_mask[:, :, u2:] *= mask[..., 1:2]
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feature_masks.append(feature_mask)
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return feature_masks
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def forward(
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@ -267,7 +215,6 @@ class Subformer(EncoderInterface):
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of frames in `embeddings` before padding.
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"""
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outputs = []
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feature_masks = self.get_feature_masks(x)
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attn_offset = self._get_attn_offset(x, src_key_padding_mask)
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@ -281,44 +228,17 @@ class Subformer(EncoderInterface):
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pos_emb = self.encoder_pos(x)
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for i, module in enumerate(self.encoders):
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x = convert_num_channels(x, self.encoder_dim[i])
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x = module(x,
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x = self.encoder(x,
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pos_emb,
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feature_mask=feature_masks[i],
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attn_offset=attn_offset,
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memory=memory,
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memory_key_padding_mask=memory_key_padding_mask,
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)
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outputs.append(x)
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def get_full_dim_output():
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num_encoders = len(self.encoder_dim)
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assert len(outputs) == num_encoders
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output_dim = max(self.encoder_dim)
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output_pieces = [ outputs[-1] ]
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cur_dim = self.encoder_dim[-1]
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for i in range(num_encoders - 2, -1, -1):
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d = self.encoder_dim[i]
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if d > cur_dim:
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this_output = outputs[i]
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output_pieces.append(this_output[..., cur_dim:d])
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cur_dim = d
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assert cur_dim == output_dim
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return torch.cat(output_pieces, dim=-1)
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# d = self.output_downsampling_factor
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# lengths = (x_lens + d - 1) // d
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# if the last output has the largest dimension, x will be unchanged,
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# it will be the same as outputs[-1]. Otherwise it will be concatenated
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# from different pieces of 'outputs', taking each dimension from the
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# most recent output that has it present.
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x = get_full_dim_output()
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#x = self.downsample_output(x)
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d = self.output_downsampling_factor
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lengths = (x_lens + d - 1) // d
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return x, lengths
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return x, x_lens
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def _get_attn_offset(self, x: Tensor, src_key_padding_mask: Optional[Tensor]) -> Optional[Tensor]:
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"""
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@ -507,7 +427,6 @@ class SubformerEncoderLayer(nn.Module):
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min_abs=0.1, max_abs=4.0,
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)
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def get_sequence_dropout_mask(self, x: Tensor, dropout_rate: float) -> Optional[Tensor]:
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if dropout_rate == 0.0 or not self.training or torch.jit.is_scripting():
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return None
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@ -650,11 +569,14 @@ class SubformerEncoder(nn.Module):
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final_layerdrop_rate: float = 0.05,
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) -> None:
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super().__init__()
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self.layers = nn.ModuleList(
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[copy.deepcopy(encoder_layer) for i in range(num_layers)]
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)
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self.num_layers = num_layers
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self.bypass = BypassModule(self.embed_dim())
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assert 0 <= warmup_begin <= warmup_end
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delta = (1. / num_layers) * (warmup_end - warmup_begin)
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@ -666,11 +588,14 @@ class SubformerEncoder(nn.Module):
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default=0.0)
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cur_begin = cur_end
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def embed_dim(self):
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return self.layers[0].embed_dim
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def forward(
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self,
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src: Tensor,
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pos_emb: Tensor,
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feature_mask: Union[Tensor, float] = 1.0,
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feature_mask: Optional[Tensor] = None,
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attn_offset: Optional[Tensor] = None,
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memory: Optional[Tensor] = None,
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memory_key_padding_mask: Optional[Tensor] = None,
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@ -693,10 +618,12 @@ class SubformerEncoder(nn.Module):
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Returns: a Tensor with the same shape as src.
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"""
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src = convert_num_channels(src, self.embed_dim())
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output = src
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rnd_seed = src.numel() + random.randint(0, 1000)
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if feature_mask is not None:
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output = output * feature_mask
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for i, mod in enumerate(self.layers):
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@ -708,9 +635,10 @@ class SubformerEncoder(nn.Module):
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memory_key_padding_mask=memory_key_padding_mask,
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)
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if feature_mask is not None:
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output = output * feature_mask
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return output
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return self.bypass(src, output)
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class BypassModule(nn.Module):
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@ -1014,17 +942,17 @@ class DownsampledSubformerEncoder(nn.Module):
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with the origin input, so that the output has the same shape as the input.
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"""
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def __init__(self,
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encoder: nn.Module,
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dim: int,
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downsample: int,
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dropout: FloatLike):
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encoders: List[nn.Module],
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input_num_channels: int,
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downsample: int):
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super(DownsampledSubformerEncoder, self).__init__()
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self.downsample_factor = downsample
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self.downsampler = LearnedDownsamplingModule(dim,
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self.downsampler = LearnedDownsamplingModule(input_num_channels,
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downsample)
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self.encoder = encoder
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self.encoders = nn.ModuleList(encoders)
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self.out_combiner = BypassModule(dim, straight_through_rate=0.025)
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self.out_combiner = BypassModule(max(e.embed_dim() for e in encoders),
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straight_through_rate=0.0)
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def forward(self,
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src: Tensor,
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@ -1059,15 +987,35 @@ class DownsampledSubformerEncoder(nn.Module):
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attn_offset = self.downsampler.downsample_attn_offset(attn_offset,
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indexes,
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weights)
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outputs = [ src ]
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src = self.encoder(
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for encoder in self.encoders:
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src = encoder(
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src,
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pos_emb,
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feature_mask=feature_mask,
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attn_offset=attn_offset,
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memory=memory,
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memory_key_padding_mask=memory_key_padding_mask,
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)
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outputs.append(src)
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def get_full_dim_output():
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num_encoders = len(outputs)
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output_dim = max(o.shape[-1] for o in outputs)
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output_pieces = [ outputs[-1] ]
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cur_dim = outputs[-1].shape[-1]
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for i in range(num_encoders - 2, -1, -1):
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d = outputs[i].shape[-1]
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if d > cur_dim:
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this_output = outputs[i]
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output_pieces.append(this_output[..., cur_dim:d])
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cur_dim = d
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assert cur_dim == output_dim
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return torch.cat(output_pieces, dim=-1)
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src = get_full_dim_output()
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src_orig = convert_num_channels(src_orig, src.shape[-1])
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src = self.downsampler.upsample(src_orig, src, indexes)
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return self.out_combiner(src_orig, src)
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@ -1793,7 +1741,8 @@ def _test_zipformer_main(causal: bool = False):
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memory_dim = 100
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c = Subformer(
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encoder_dim=(64, 96), encoder_unmasked_dim=(48, 64), num_heads=(4, 4),
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encoder_dim=(64, 96, 64),
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num_heads=(4, 4, 8),
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causal=causal,
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memory_dim=memory_dim,
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)
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@ -125,15 +125,6 @@ def add_model_arguments(parser: argparse.ArgumentParser):
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help="Number of subformer encoder layers per stack, comma separated.",
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)
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parser.add_argument(
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"--downsampling-factor",
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type=str,
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default="1,2,4,8,4,2,1",
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help="Downsampling factor for each stack of encoder layers.",
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)
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parser.add_argument(
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"--feedforward-dim",
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type=str,
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@ -176,13 +167,7 @@ def add_model_arguments(parser: argparse.ArgumentParser):
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help="Positional-encoding dimension in encoder stacks: a single int or comma-separated list."
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)
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parser.add_argument(
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"--encoder-unmasked-dim",
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type=str,
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default="192,192,256,256,256,192,192",
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help="Unmasked dimensions in the encoders, relates to augmentation during training. "
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"A single int or comma-separated list. Must be <= each corresponding encoder_dim."
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)
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def get_parser():
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@ -478,11 +463,8 @@ def get_encoder_embed(params: AttributeDict) -> nn.Module:
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def get_encoder_model(params: AttributeDict) -> nn.Module:
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#chunk_size = _to_int_tuple(params.downsampling_factor)[-1]
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encoder = Subformer(
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#output_downsampling_factor=chunk_size,
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downsampling_factor=_to_int_tuple(params.downsampling_factor),
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num_encoder_layers=_to_int_tuple(params.num_encoder_layers),
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encoder_dim=_to_int_tuple(params.encoder_dim),
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encoder_unmasked_dim=_to_int_tuple(params.encoder_unmasked_dim),
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query_head_dim=_to_int_tuple(params.query_head_dim),
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pos_dim=int(params.pos_dim),
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value_head_dim=_to_int_tuple(params.value_head_dim),
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