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#!/usr/bin/env python3
import torch
from torch import nn
import torch_model
class MultiHeadedAttention(nn.Module):
"""
This class is copied and modified from
https://github.com/modelscope/FunASR/blob/main/funasr/models/transformer/attention.py
"""
def __init__(self, n_head, n_feat, dropout_rate):
super().__init__()
assert n_feat % n_head == 0
# We assume d_v always equals d_k
self.d_k = n_feat // n_head
self.h = n_head
self.linear_q = nn.Linear(n_feat, n_feat)
self.linear_k = nn.Linear(n_feat, n_feat)
self.linear_v = nn.Linear(n_feat, n_feat)
self.linear_out = nn.Linear(n_feat, n_feat)
self.attn = None
self.dropout = nn.Dropout(p=dropout_rate)
def forward_qkv(self, query, key, value):
"""Transform query, key and value.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
Returns:
torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
"""
n_batch = query.size(0)
q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
q = q.transpose(1, 2) # (batch, head, time1, d_k)
k = k.transpose(1, 2) # (batch, head, time2, d_k)
v = v.transpose(1, 2) # (batch, head, time2, d_k)
return q, k, v
def forward_attention(self, value, scores, mask):
"""Compute attention context vector.
Args:
value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
Returns:
torch.Tensor: Transformed value (#batch, time1, d_model)
weighted by the attention score (#batch, time1, time2).
"""
n_batch = value.size(0)
if mask is not None:
mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2)
min_value = -float(
"inf"
) # min_value = float(np.finfo(torch.tensor(0, dtype=qk.dtype).numpy().dtype).min)
scores = scores.masked_fill(mask, min_value)
attn = torch.softmax(scores, dim=-1).masked_fill(
mask, 0.0
) # (batch, head, time1, time2)
else:
attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2)
p_attn = self.dropout(attn)
x = torch.matmul(p_attn, value) # (batch, head, time1, d_k)
x = (
x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
) # (batch, time1, d_model)
return self.linear_out(x) # (batch, time1, d_model)
def forward(self, query, key, value, mask):
"""Compute scaled dot product attention.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
(#batch, time1, time2).
Returns:
torch.Tensor: Output tensor (#batch, time1, d_model).
"""
q, k, v = self.forward_qkv(query, key, value)
# scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
scores = torch.matmul(q, k.transpose(-2, -1)) * self.d_k ** (-0.5)
return self.forward_attention(v, scores, mask)
class EncoderLayer(nn.Module):
"""
This class is copied and modified from
https://github.com/modelscope/FunASR/blob/main/funasr/models/transformer/encoder.py
"""
def __init__(
self,
size,
self_attn,
feed_forward,
dropout_rate,
normalize_before=True,
concat_after=False,
stochastic_depth_rate=0.0,
):
super().__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.norm1 = nn.LayerNorm(size, eps=1e-12)
self.norm2 = nn.LayerNorm(size, eps=1e-12)
self.dropout = nn.Dropout(dropout_rate)
self.size = size
self.normalize_before = normalize_before
self.concat_after = concat_after
if self.concat_after:
self.concat_linear = nn.Linear(size + size, size)
self.stochastic_depth_rate = stochastic_depth_rate
def forward(self, x, mask=None, cache=None):
"""Compute encoded features.
Args:
x_input (torch.Tensor): Input tensor (#batch, time, size).
mask (torch.Tensor): Mask tensor for the input (#batch, time).
cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
Returns:
torch.Tensor: Output tensor (#batch, time, size).
torch.Tensor: Mask tensor (#batch, time).
"""
skip_layer = False
# with stochastic depth, residual connection `x + f(x)` becomes
# `x <- x + 1 / (1 - p) * f(x)` at training time.
stoch_layer_coeff = 1.0
if skip_layer:
if cache is not None:
x = torch.cat([cache, x], dim=1)
return x, mask
residual = x
if self.normalize_before:
x = self.norm1(x)
if cache is None:
x_q = x
else:
assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
x_q = x[:, -1:, :]
residual = residual[:, -1:, :]
mask = None if mask is None else mask[:, -1:, :]
if self.concat_after:
x_concat = torch.cat((x, self.self_attn(x_q, x, x, mask)), dim=-1)
x = residual + stoch_layer_coeff * self.concat_linear(x_concat)
else:
x = residual + stoch_layer_coeff * self.dropout(
self.self_attn(x_q, x, x, mask)
)
if not self.normalize_before:
x = self.norm1(x)
residual = x
if self.normalize_before:
x = self.norm2(x)
x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x))
if not self.normalize_before:
x = self.norm2(x)
if cache is not None:
x = torch.cat([cache, x], dim=1)
return x, mask
class Transformer(nn.Module):
# This class is copied and modified from
# https://github.com/modelscope/FunASR/blob/main/funasr/models/llm_asr/adaptor.py
def __init__(
self,
downsample_rate=1,
encoder_dim=512,
llm_dim=512,
ffn_dim: int = 2048,
n_layer: int = 5,
**kwargs
):
super().__init__()
assert downsample_rate == 1, downsample_rate
self.k = downsample_rate
self.encoder_dim = encoder_dim
self.llm_dim = llm_dim
self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim)
self.relu = nn.ReLU()
self.linear2 = nn.Linear(ffn_dim, self.llm_dim)
self.blocks = None
if n_layer > 0:
self.blocks = nn.ModuleList(
[
EncoderLayer(
llm_dim,
MultiHeadedAttention(
kwargs.get("attention_heads", 8),
llm_dim,
kwargs.get("attention_dropout_rate", 0.0),
),
torch_model.PositionwiseFeedForward(
llm_dim,
llm_dim // 4,
kwargs.get("dropout_rate", 0.0),
),
kwargs.get("dropout_rate", 0.0),
)
for i in range(n_layer)
]
)
def forward(self, x):
x = self.linear1(x)
x = self.relu(x)
x = self.linear2(x)
masks = None
if self.blocks is not None:
for layer, block in enumerate(self.blocks):
x, masks = block(x, masks)
return x
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