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attention_decoder.py
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attention_decoder.py
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#!/usr/bin/env python3
# Copyright 2023 Xiaomi Corp. (authors: Zengwei Yao)
#
# 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 math
from typing import List, Optional
import k2
import torch
import torch.nn as nn
from label_smoothing import LabelSmoothingLoss
from scaling import penalize_abs_values_gt
from icefall.utils import add_eos, add_sos, make_pad_mask
class AttentionDecoderModel(nn.Module):
"""
Args:
vocab_size (int): Number of classes.
decoder_dim: (int,int): embedding dimension of 2 encoder stacks
attention_dim: (int,int): attention dimension of 2 encoder stacks
num_heads (int, int): number of heads
dim_feedforward (int, int): feedforward dimension in 2 encoder stacks
num_encoder_layers (int): number of encoder layers
dropout (float): dropout rate
"""
def __init__(
self,
vocab_size: int,
decoder_dim: int = 512,
num_decoder_layers: int = 6,
attention_dim: int = 512,
num_heads: int = 8,
feedforward_dim: int = 2048,
memory_dim: int = 512,
sos_id: int = 1,
eos_id: int = 1,
dropout: float = 0.1,
ignore_id: int = -1,
label_smoothing: float = 0.1,
):
super().__init__()
self.eos_id = eos_id
self.sos_id = sos_id
self.ignore_id = ignore_id
# For the segment of the warmup period, we let the Embedding
# layer learn something. Then we start to warm up the other encoders.
self.decoder = TransformerDecoder(
vocab_size=vocab_size,
d_model=decoder_dim,
num_decoder_layers=num_decoder_layers,
attention_dim=attention_dim,
num_heads=num_heads,
feedforward_dim=feedforward_dim,
memory_dim=memory_dim,
dropout=dropout,
)
# Used to calculate attention-decoder loss
self.loss_fun = LabelSmoothingLoss(
ignore_index=ignore_id, label_smoothing=label_smoothing, reduction="sum"
)
def _pre_ys_in_out(self, ys: k2.RaggedTensor, ys_lens: torch.Tensor):
"""Prepare ys_in_pad and ys_out_pad."""
ys_in = add_sos(ys, sos_id=self.sos_id)
# [B, S+1], start with SOS
ys_in_pad = ys_in.pad(mode="constant", padding_value=self.eos_id)
ys_in_lens = ys_lens + 1
ys_out = add_eos(ys, eos_id=self.eos_id)
# [B, S+1], end with EOS
ys_out_pad = ys_out.pad(mode="constant", padding_value=self.ignore_id)
return ys_in_pad.to(torch.int64), ys_in_lens, ys_out_pad.to(torch.int64)
def calc_att_loss(
self,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
ys: k2.RaggedTensor,
ys_lens: torch.Tensor,
) -> torch.Tensor:
"""Calculate attention-decoder loss.
Args:
encoder_out: (batch, num_frames, encoder_dim)
encoder_out_lens: (batch,)
token_ids: A list of token id list.
Return: The attention-decoder loss.
"""
ys_in_pad, ys_in_lens, ys_out_pad = self._pre_ys_in_out(ys, ys_lens)
# decoder forward
decoder_out = self.decoder(
x=ys_in_pad,
x_lens=ys_in_lens,
memory=encoder_out,
memory_lens=encoder_out_lens,
)
loss = self.loss_fun(x=decoder_out, target=ys_out_pad)
return loss
def nll(
self,
encoder_out: torch.Tensor,
encoder_out_lens: torch.Tensor,
token_ids: List[List[int]],
) -> torch.Tensor:
"""Compute negative log likelihood(nll) from attention-decoder.
Args:
encoder_out: (batch, num_frames, encoder_dim)
encoder_out_lens: (batch,)
token_ids: A list of token id list.
Return: A tensor of shape (batch, num_tokens).
"""
ys = k2.RaggedTensor(token_ids).to(device=encoder_out.device)
row_splits = ys.shape.row_splits(1)
ys_lens = row_splits[1:] - row_splits[:-1]
ys_in_pad, ys_in_lens, ys_out_pad = self._pre_ys_in_out(ys, ys_lens)
# decoder forward
decoder_out = self.decoder(
x=ys_in_pad,
x_lens=ys_in_lens,
memory=encoder_out,
memory_lens=encoder_out_lens,
)
batch_size, _, num_classes = decoder_out.size()
nll = nn.functional.cross_entropy(
decoder_out.view(-1, num_classes),
ys_out_pad.view(-1),
ignore_index=self.ignore_id,
reduction="none",
)
nll = nll.view(batch_size, -1)
return nll
class TransformerDecoder(nn.Module):
"""Transfomer decoder module.
Args:
vocab_size: output dim
d_model: decoder dimension
num_decoder_layers: number of decoder layers
attention_dim: total dimension of multi head attention
num_heads: number of attention heads
feedforward_dim: hidden dimension of feed_forward module
dropout: dropout rate
"""
def __init__(
self,
vocab_size: int,
d_model: int = 512,
num_decoder_layers: int = 6,
attention_dim: int = 512,
num_heads: int = 8,
feedforward_dim: int = 2048,
memory_dim: int = 512,
dropout: float = 0.1,
):
super().__init__()
self.embed = nn.Embedding(num_embeddings=vocab_size, embedding_dim=d_model)
# Absolute positional encoding
self.pos = PositionalEncoding(d_model, dropout_rate=0.1)
self.num_layers = num_decoder_layers
self.layers = nn.ModuleList(
[
DecoderLayer(
d_model=d_model,
attention_dim=attention_dim,
num_heads=num_heads,
feedforward_dim=feedforward_dim,
memory_dim=memory_dim,
dropout=dropout,
)
for _ in range(num_decoder_layers)
]
)
self.output_layer = nn.Linear(d_model, vocab_size)
def forward(
self,
x: torch.Tensor,
x_lens: torch.Tensor,
memory: Optional[torch.Tensor] = None,
memory_lens: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Args:
x: Input tensor of shape (batch, tgt_len).
x_lens: A tensor of shape (batch,) containing the number of tokens in `x`
before padding.
memory:
Memory sequence of shape (batch, src_len, memory_dim).
memory_lens:
A tensor of shape (batch,) containing the number of frames in
`memory` before padding.
Returns:
Decoded token logits before softmax (batch, tgt_len, vocab_size)
"""
x = self.embed(x) # (batch, tgt_len, embed_dim)
x = self.pos(x) # (batch, tgt_len, embed_dim)
x = x.permute(1, 0, 2) # (tgt_len, batch, embed_dim)
# construct attn_mask for self-attn modules
padding_mask = make_pad_mask(x_lens) # (batch, tgt_len)
causal_mask = subsequent_mask(x.shape[0], device=x.device) # (seq_len, seq_len)
attn_mask = torch.logical_or(
padding_mask.unsqueeze(1), # (batch, 1, seq_len)
torch.logical_not(causal_mask).unsqueeze(0) # (1, seq_len, seq_len)
) # (batch, seq_len, seq_len)
if memory is not None:
memory = memory.permute(1, 0, 2) # (src_len, batch, memory_dim)
# construct memory_attn_mask for cross-attn modules
memory_padding_mask = make_pad_mask(memory_lens) # (batch, src_len)
memory_attn_mask = memory_padding_mask.unsqueeze(1) # (batch, 1, src_len)
else:
memory_attn_mask = None
for i, mod in enumerate(self.layers):
x = mod(
x,
attn_mask=attn_mask,
memory=memory,
memory_attn_mask=memory_attn_mask,
)
x = x.permute(1, 0, 2) # (batch, tgt_len, vocab_size)
x = self.output_layer(x)
return x
class DecoderLayer(nn.Module):
"""Single decoder layer module.
Args:
d_model: equal to decoder_dim, total dimension of the decoder
attention_dim: total dimension of multi head attention
num_heads: number of attention heads
feedforward_dim: hidden dimension of feed_forward module
dropout: dropout rate
"""
def __init__(
self,
d_model: int = 512,
attention_dim: int = 512,
num_heads: int = 8,
feedforward_dim: int = 2048,
memory_dim: int = 512,
dropout: float = 0.1,
):
"""Construct an DecoderLayer object."""
super(DecoderLayer, self).__init__()
self.norm_self_attn = nn.LayerNorm(d_model)
self.self_attn = MultiHeadAttention(
d_model, attention_dim, num_heads, dropout=0.0
)
self.norm_src_attn = nn.LayerNorm(d_model)
self.src_attn = MultiHeadAttention(
d_model, attention_dim, num_heads, memory_dim=memory_dim, dropout=0.0
)
self.norm_ff = nn.LayerNorm(d_model)
self.feed_forward = nn.Sequential(
nn.Linear(d_model, feedforward_dim),
Swish(),
nn.Dropout(dropout),
nn.Linear(feedforward_dim, d_model),
)
self.dropout = nn.Dropout(dropout)
def forward(
self,
x: torch.Tensor,
attn_mask: Optional[torch.Tensor] = None,
memory: Optional[torch.Tensor] = None,
memory_attn_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Args:
x: Input sequence of shape (seq_len, batch, embed_dim).
attn_mask: A binary mask for self-attention module indicating which
elements will be filled with -inf.
Its shape is (batch, 1, src_len) or (batch, tgt_len, src_len).
memory: Memory sequence of shape (seq_len, batch, memory_dim).
memory_attn_mask: A binary mask for cross-attention module indicating which
elements will be filled with -inf.
Its shape is (batch, 1, src_len) or (batch, tgt_len, src_len).
"""
# self-attn module
qkv = self.norm_self_attn(x)
self_attn_out = self.self_attn(
query=qkv, key=qkv, value=qkv, attn_mask=attn_mask
)
x = x + self.dropout(self_attn_out)
# cross-attn module
q = self.norm_src_attn(x)
src_attn_out = self.src_attn(
query=q, key=memory, value=memory, attn_mask=memory_attn_mask
)
x = x + self.dropout(src_attn_out)
# feed-forward module
x = x + self.dropout(self.feed_forward(self.norm_ff(x)))
return x
class MultiHeadAttention(nn.Module):
"""Multi-Head Attention layer.
Args:
embed_dim: total dimension of the model.
attention_dim: dimension in the attention module, but must be a multiple of num_heads.
num_heads: number of parallel attention heads.
memory_dim: dimension of memory embedding, optional.
dropout: a Dropout layer on attn_output_weights.
"""
def __init__(
self,
embed_dim: int,
attention_dim: int,
num_heads: int,
memory_dim: Optional[int] = None,
dropout: float = 0.0,
):
super(MultiHeadAttention, self).__init__()
self.embed_dim = embed_dim
self.attention_dim = attention_dim
self.num_heads = num_heads
self.head_dim = attention_dim // num_heads
assert self.head_dim * num_heads == attention_dim, (
self.head_dim, num_heads, attention_dim
)
self.dropout = dropout
self.name = None # will be overwritten in training code; for diagnostics.
self.linear_q = nn.Linear(embed_dim, attention_dim, bias=True)
self.linear_k = nn.Linear(
embed_dim if memory_dim is None else memory_dim, attention_dim, bias=True
)
self.linear_v = nn.Linear(
embed_dim if memory_dim is None else memory_dim, attention_dim, bias=True
)
self.out_proj = nn.Linear(attention_dim, embed_dim, bias=True)
def forward(
self,
query: torch.Tensor,
key: torch.Tensor,
value: torch.Tensor,
key_padding_mask: Optional[torch.Tensor] = None,
attn_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Compute dot product attention.
Args:
query: Query tensor of shape (tgt_len, batch, embed_dim).
key: Key tensor of shape (src_len, batch, embed_dim or memory_dim).
value: Value tensor of shape (src_len, batch, embed_dim or memory_dim).
key_padding_mask: A binary mask indicating which elements are padding.
Its shape is (batch, src_len).
attn_mask: A binary mask indicating which elements will be filled with -inf.
Its shape is (batch, 1, src_len) or (batch, tgt_len, src_len).
Returns:
Output tensor of shape (tgt_len, batch, embed_dim).
"""
num_heads = self.num_heads
head_dim = self.head_dim
tgt_len, batch, _ = query.shape
src_len = key.shape[0]
q = self.linear_q(query) # (tgt_len, batch, num_heads * head_dim)
k = self.linear_k(key) # (src_len, batch, num_heads * head_dim)
v = self.linear_v(value) # (src_len, batch, num_heads * head_dim)
q = q.reshape(tgt_len, batch, num_heads, head_dim)
q = q.permute(1, 2, 0, 3) # (batch, head, tgt_len, head_dim)
k = k.reshape(src_len, batch, num_heads, head_dim)
k = k.permute(1, 2, 3, 0) # (batch, head, head_dim, src_len)
v = v.reshape(src_len, batch, num_heads, head_dim)
v = v.reshape(src_len, batch * num_heads, head_dim).transpose(0, 1)
# Note: could remove the scaling operation when using ScaledAdam
# (batch, head, tgt_len, src_len)
attn_weights = torch.matmul(q, k) / math.sqrt(head_dim)
# From zipformer.py:
# This is a harder way of limiting the attention scores to not be too large.
# It incurs a penalty if any of them has an absolute value greater than 50.0.
# this should be outside the normal range of the attention scores. We use
# this mechanism instead of, say, a limit on entropy, because once the entropy
# gets very small gradients through the softmax can become very small, and
# some mechanisms like that become ineffective.
attn_weights = penalize_abs_values_gt(attn_weights, limit=50.0, penalty=1.0e-04)
if key_padding_mask is not None:
assert key_padding_mask.shape == (batch, src_len), key_padding_mask.shape
attn_weights = attn_weights.masked_fill(
key_padding_mask.unsqueeze(1).unsqueeze(2), float("-inf"),
)
if attn_mask is not None:
assert (
attn_mask.shape == (batch, 1, src_len)
or attn_mask.shape == (batch, tgt_len, src_len)
), attn_mask.shape
attn_weights = attn_weights.masked_fill(attn_mask.unsqueeze(1), float("-inf"))
attn_weights = attn_weights.view(batch * num_heads, tgt_len, src_len)
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
attn_weights = nn.functional.dropout(
attn_weights, p=self.dropout, training=self.training
)
# (batch * head, tgt_len, head_dim)
attn_output = torch.bmm(attn_weights, v)
assert attn_output.shape == (batch * num_heads, tgt_len, head_dim), attn_output.shape
attn_output = attn_output.transpose(0, 1).contiguous()
attn_output = attn_output.view(tgt_len, batch, num_heads * head_dim)
# (batch, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output
class PositionalEncoding(nn.Module):
"""Positional encoding.
Copied from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/embedding.py#L35.
Args:
d_model (int): Embedding dimension.
dropout_rate (float): Dropout rate.
max_len (int): Maximum input length.
"""
def __init__(self, d_model, dropout_rate, max_len=5000):
"""Construct an PositionalEncoding object."""
super(PositionalEncoding, self).__init__()
self.d_model = d_model
self.xscale = math.sqrt(self.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):
"""Reset the positional encodings."""
if self.pe is not None:
if self.pe.size(1) >= x.size(1):
if self.pe.dtype != x.dtype or self.pe.device != x.device:
self.pe = self.pe.to(dtype=x.dtype, device=x.device)
return
pe = 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[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, x: torch.Tensor):
"""Add positional encoding.
Args:
x (torch.Tensor): Input tensor (batch, time, `*`).
Returns:
torch.Tensor: Encoded tensor (batch, time, `*`).
"""
self.extend_pe(x)
x = x * self.xscale + self.pe[:, : x.size(1)]
return self.dropout(x)
class Swish(torch.nn.Module):
"""Construct an Swish object."""
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Return Swich activation function."""
return x * torch.sigmoid(x)
def subsequent_mask(size, device="cpu", dtype=torch.bool):
"""Create mask for subsequent steps (size, size).
:param int size: size of mask
:param str device: "cpu" or "cuda" or torch.Tensor.device
:param torch.dtype dtype: result dtype
:rtype: torch.Tensor
>>> subsequent_mask(3)
[[1, 0, 0],
[1, 1, 0],
[1, 1, 1]]
"""
ret = torch.ones(size, size, device=device, dtype=dtype)
return torch.tril(ret, out=ret)
def _test_attention_decoder_model():
m = AttentionDecoderModel(
vocab_size=500,
decoder_dim=512,
num_decoder_layers=6,
attention_dim=512,
num_heads=8,
feedforward_dim=2048,
memory_dim=384,
dropout=0.1,
sos_id=1,
eos_id=1,
ignore_id=-1,
)
num_param = sum([p.numel() for p in m.parameters()])
print(f"Number of model parameters: {num_param}")
m.eval()
encoder_out = torch.randn(2, 50, 384)
encoder_out_lens = torch.full((2,), 50)
token_ids = [[1, 2, 3, 4], [2, 3, 10]]
nll = m.nll(encoder_out, encoder_out_lens, token_ids)
print(nll)
if __name__ == "__main__":
_test_attention_decoder_model()