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#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import math
from typing import List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from icefall.transformer_lm.attention import RelPositionMultiheadAttention
from icefall.transformer_lm.scaling import (
ActivationBalancer,
BasicNorm,
DoubleSwish,
ScaledConv1d,
ScaledConv2d,
ScaledLinear,
)
from icefall.utils import is_jit_tracing, make_pad_mask
class Transformer(torch.nn.Module):
"""_summary_
Args:
input_dim (int): Input feature dimension
d_mode (int): The dimension of the transformer
dim_feedforward (int ): The dimension of the ffw module
nhead (int): The number of attention heads
dropout_rate (float): dropout rate
att_dropout (float): dropout rate in attention module
"""
def __init__(
self,
input_dim: int,
d_model: int,
dim_feedforward: int,
nhead: int = 4,
num_layers: int = 6,
dropout_rate: float = 0.1,
att_dropout: float = 0.0,
):
super().__init__()
self.encoder_layers = num_layers
self.d_model = d_model
self.embed = ScaledLinear(input_dim, d_model)
self.norm_before = BasicNorm(d_model, learn_eps=False)
self.encoder_pos = RelPositionalEncoding(d_model, dropout_rate)
encoder_layer = TransformerEncoderLayer(
d_model=d_model,
dim_feedforward=dim_feedforward,
nhead=nhead,
dropout_rate=dropout_rate,
)
self.encoder = TransformerEncoder(encoder_layer, num_layers)
def _create_attention_mask(self, x_lens: torch.Tensor):
# create a 2D attention mask to mask out
# the upper right half of the attention matrix
max_len = max(x_lens)
ones = torch.ones(max_len, max_len, device=x_lens.device, dtype=torch.bool)
return torch.triu(ones, diagonal=1)
def forward(
self, x: torch.Tensor, x_lens: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Transformer forward
Args:
x (torch.Tensor): Input tensor (B,T,input_dim)
x_lens (torch.Tensor): The length of input tensors before padding (B,)
Returns:
Return a tuple of 2 tensors:
- x: output feature of the transformer (B,T,d_model)
- x_lens: output feature lens of the transformer
"""
attention_mask = self._create_attention_mask(x_lens)
src_key_padding_mask = make_pad_mask(x_lens)
x = self.norm_before(self.embed(x))
x, pos_emb = self.encoder_pos(x)
x = x.permute(1, 0, 2)
x = self.encoder(
x,
pos_emb,
mask=attention_mask, # pass the attention mast
src_key_padding_mask=src_key_padding_mask,
) # (T, N, C)
x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
return x, x_lens
class TransformerEncoder(torch.nn.Module):
def __init__(self, encoder_layer: torch.nn.Module, num_layers: int) -> None:
"""TransformerEncoder is a stack of N encoder layers
Args:
encoder_layer (torch.nn.Module): an instance of the TransformerEncoderLayer()
num_layers (int): Number of layers to be stacked
"""
super().__init__()
self.layers = nn.ModuleList(
[copy.deepcopy(encoder_layer) for i in range(num_layers)]
)
self.num_layers = num_layers
def forward(
self,
src: torch.Tensor,
pos_emb: torch.Tensor,
src_key_padding_mask: Optional[torch.Tensor] = None,
mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""_summary_
Args:
src: the sequence to the encoder (required).
pos_emb: Positional embedding tensor (required).
mask: the mask for the src sequence (optional).
src_key_padding_mask: the mask for the src keys per batch (optional).
Returns:
output: transformer encoded features
"""
output = src
for layer_index, mod in enumerate(self.layers):
output = mod(
output,
pos_emb,
src_key_padding_mask=src_key_padding_mask,
src_mask=mask,
)
return output
class TransformerEncoderLayer(torch.nn.Module):
def __init__(
self,
d_model: int,
dim_feedforward: int,
nhead: int,
dropout_rate: float,
):
"""TransformerEncoderLayer is made up of self-attn and feedforward module
Args:
d_model (int): The model size
dim_feedforward (int): Dimension of ffw module
nhead (int): Number of heads
dropout_rate (float): Dropout rate
"""
super().__init__()
self.d_model = d_model
self.self_attn = RelPositionMultiheadAttention(d_model, nhead, dropout=0.0)
self.feed_forward = nn.Sequential(
ScaledLinear(d_model, dim_feedforward),
ActivationBalancer(channel_dim=-1),
DoubleSwish(),
nn.Dropout(dropout_rate),
ScaledLinear(dim_feedforward, d_model, initial_scale=0.25),
)
self.norm_final = BasicNorm(d_model)
self.balancer = ActivationBalancer(
channel_dim=-1, min_positive=0.45, max_positive=0.55, max_abs=6.0
)
self.dropout = nn.Dropout(dropout_rate)
def forward(
self,
src: torch.Tensor,
pos_emb: torch.Tensor,
src_key_padding_mask: Optional[torch.Tensor] = None,
src_mask: Optional[torch.Tensor] = None,
cache=None,
):
"""
Pass the input through the encoder layer.
Args:
src: the sequence to the encoder layer (required).
pos_emb: Positional embedding tensor (required).
src_key_padding_mask: the mask for the src keys per batch (optional).
src_mask: the mask for the src sequence (optional).
"""
src_orig = src
src_att = self.self_attn(
src,
src,
src,
pos_emb=pos_emb,
attn_mask=src_mask,
key_padding_mask=src_key_padding_mask,
)[0]
src = src + self.dropout(src_att)
# feed forward module
src = src + self.dropout(self.feed_forward(src))
src = self.norm_final(self.balancer(src))
return src
class RelPositionalEncoding(torch.nn.Module):
"""Relative positional encoding module.
See : Appendix B in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"
Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py
Args:
d_model: Embedding dimension.
dropout_rate: Dropout rate.
max_len: Maximum input length.
"""
def __init__(self, d_model: int, dropout_rate: float, max_len: int = 5000) -> None:
"""Construct an PositionalEncoding object."""
super(RelPositionalEncoding, self).__init__()
if is_jit_tracing():
# 10k frames correspond to ~100k ms, e.g., 100 seconds, i.e.,
# It assumes that the maximum input won't have more than
# 10k frames.
#
# TODO(fangjun): Use torch.jit.script() for this module
max_len = 10000
self.d_model = d_model
self.dropout = torch.nn.Dropout(p=dropout_rate)
self.pe = None
self.extend_pe(torch.tensor(0.0).expand(1, max_len))
def extend_pe(self, x: torch.Tensor, left_context: int = 0) -> None:
"""Reset the positional encodings."""
x_size_1 = x.size(1) + left_context
if self.pe is not None:
# self.pe contains both positive and negative parts
# the length of self.pe is 2 * input_len - 1
if self.pe.size(1) >= x_size_1 * 2 - 1:
# Note: TorchScript doesn't implement operator== for torch.Device
if self.pe.dtype != x.dtype or str(self.pe.device) != str(x.device):
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
# Suppose `i` means to the position of query vector and `j` means the
# position of key vector. We use position relative positions when keys
# are to the left (i>j) and negative relative positions otherwise (i<j).
pe_positive = torch.zeros(x_size_1, self.d_model)
pe_negative = torch.zeros(x_size_1, self.d_model)
position = torch.arange(0, x_size_1, dtype=torch.float32).unsqueeze(1)
div_term = torch.exp(
torch.arange(0, self.d_model, 2, dtype=torch.float32)
* -(math.log(10000.0) / self.d_model)
)
pe_positive[:, 0::2] = torch.sin(position * div_term)
pe_positive[:, 1::2] = torch.cos(position * div_term)
pe_negative[:, 0::2] = torch.sin(-1 * position * div_term)
pe_negative[:, 1::2] = torch.cos(-1 * position * div_term)
# Reserve the order of positive indices and concat both positive and
# negative indices. This is used to support the shifting trick
# as in "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context"
pe_positive = torch.flip(pe_positive, [0]).unsqueeze(0)
pe_negative = pe_negative[1:].unsqueeze(0)
pe = torch.cat([pe_positive, pe_negative], dim=1)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(
self,
x: torch.Tensor,
left_context: int = 0,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Add positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
left_context (int): left context (in frames) used during streaming decoding.
this is used only in real streaming decoding, in other circumstances,
it MUST be 0.
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
torch.Tensor: Encoded tensor (batch, 2*time-1, `*`).
"""
self.extend_pe(x, left_context)
x_size_1 = x.size(1) + left_context
pos_emb = self.pe[
:,
self.pe.size(1) // 2
- x_size_1
+ 1 : self.pe.size(1) // 2 # noqa E203
+ x.size(1),
]
return self.dropout(x), self.dropout(pos_emb)
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