#!/usr/bin/env python3
|
# -*- encoding: utf-8 -*-
|
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
|
# MIT License (https://opensource.org/licenses/MIT)
|
|
import torch
|
from typing import List, Tuple
|
|
from funasr.register import tables
|
from funasr.models.scama import utils as myutils
|
from funasr.models.transformer.utils.repeat import repeat
|
from funasr.models.transformer.decoder import DecoderLayer
|
from funasr.models.transformer.layer_norm import LayerNorm
|
from funasr.models.transformer.embedding import PositionalEncoding
|
from funasr.models.transformer.attention import MultiHeadedAttention
|
from funasr.models.transformer.utils.nets_utils import make_pad_mask
|
from funasr.models.transformer.decoder import BaseTransformerDecoder
|
from funasr.models.transformer.positionwise_feed_forward import PositionwiseFeedForward
|
from funasr.models.sanm.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
|
from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
|
|
|
class DecoderLayerSANM(torch.nn.Module):
|
"""Single decoder layer module.
|
|
Args:
|
size (int): Input dimension.
|
self_attn (torch.nn.Module): Self-attention module instance.
|
`MultiHeadedAttention` instance can be used as the argument.
|
src_attn (torch.nn.Module): Self-attention module instance.
|
`MultiHeadedAttention` instance can be used as the argument.
|
feed_forward (torch.nn.Module): Feed-forward module instance.
|
`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
|
can be used as the argument.
|
dropout_rate (float): Dropout rate.
|
normalize_before (bool): Whether to use layer_norm before the first block.
|
concat_after (bool): Whether to concat attention layer's input and output.
|
if True, additional linear will be applied.
|
i.e. x -> x + linear(concat(x, att(x)))
|
if False, no additional linear will be applied. i.e. x -> x + att(x)
|
|
|
"""
|
|
def __init__(
|
self,
|
size,
|
self_attn,
|
src_attn,
|
feed_forward,
|
dropout_rate,
|
normalize_before=True,
|
concat_after=False,
|
):
|
"""Construct an DecoderLayer object."""
|
super(DecoderLayerSANM, self).__init__()
|
self.size = size
|
self.self_attn = self_attn
|
self.src_attn = src_attn
|
self.feed_forward = feed_forward
|
self.norm1 = LayerNorm(size)
|
if self_attn is not None:
|
self.norm2 = LayerNorm(size)
|
if src_attn is not None:
|
self.norm3 = LayerNorm(size)
|
self.dropout = torch.nn.Dropout(dropout_rate)
|
self.normalize_before = normalize_before
|
self.concat_after = concat_after
|
if self.concat_after:
|
self.concat_linear1 = torch.nn.Linear(size + size, size)
|
self.concat_linear2 = torch.nn.Linear(size + size, size)
|
self.reserve_attn=False
|
self.attn_mat = []
|
|
def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
|
"""Compute decoded features.
|
|
Args:
|
tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
|
tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
|
memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
|
memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
|
cache (List[torch.Tensor]): List of cached tensors.
|
Each tensor shape should be (#batch, maxlen_out - 1, size).
|
|
Returns:
|
torch.Tensor: Output tensor(#batch, maxlen_out, size).
|
torch.Tensor: Mask for output tensor (#batch, maxlen_out).
|
torch.Tensor: Encoded memory (#batch, maxlen_in, size).
|
torch.Tensor: Encoded memory mask (#batch, maxlen_in).
|
|
"""
|
# tgt = self.dropout(tgt)
|
residual = tgt
|
if self.normalize_before:
|
tgt = self.norm1(tgt)
|
tgt = self.feed_forward(tgt)
|
|
x = tgt
|
if self.self_attn:
|
if self.normalize_before:
|
tgt = self.norm2(tgt)
|
x, _ = self.self_attn(tgt, tgt_mask)
|
x = residual + self.dropout(x)
|
|
if self.src_attn is not None:
|
residual = x
|
if self.normalize_before:
|
x = self.norm3(x)
|
if self.reserve_attn:
|
x_src_attn, attn_mat = self.src_attn(x, memory, memory_mask, ret_attn=True)
|
self.attn_mat.append(attn_mat)
|
else:
|
x_src_attn = self.src_attn(x, memory, memory_mask, ret_attn=False)
|
x = residual + self.dropout(x_src_attn)
|
# x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
|
|
return x, tgt_mask, memory, memory_mask, cache
|
|
def get_attn_mat(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
|
residual = tgt
|
tgt = self.norm1(tgt)
|
tgt = self.feed_forward(tgt)
|
|
x = tgt
|
if self.self_attn is not None:
|
tgt = self.norm2(tgt)
|
x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
|
x = residual + x
|
|
residual = x
|
x = self.norm3(x)
|
x_src_attn, attn_mat = self.src_attn(x, memory, memory_mask, ret_attn=True)
|
return attn_mat
|
|
def forward_one_step(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
|
"""Compute decoded features.
|
|
Args:
|
tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
|
tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
|
memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
|
memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
|
cache (List[torch.Tensor]): List of cached tensors.
|
Each tensor shape should be (#batch, maxlen_out - 1, size).
|
|
Returns:
|
torch.Tensor: Output tensor(#batch, maxlen_out, size).
|
torch.Tensor: Mask for output tensor (#batch, maxlen_out).
|
torch.Tensor: Encoded memory (#batch, maxlen_in, size).
|
torch.Tensor: Encoded memory mask (#batch, maxlen_in).
|
|
"""
|
# tgt = self.dropout(tgt)
|
residual = tgt
|
if self.normalize_before:
|
tgt = self.norm1(tgt)
|
tgt = self.feed_forward(tgt)
|
|
x = tgt
|
if self.self_attn:
|
if self.normalize_before:
|
tgt = self.norm2(tgt)
|
if self.training:
|
cache = None
|
x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
|
x = residual + self.dropout(x)
|
|
if self.src_attn is not None:
|
residual = x
|
if self.normalize_before:
|
x = self.norm3(x)
|
|
x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
|
|
|
return x, tgt_mask, memory, memory_mask, cache
|
|
def forward_chunk(self, tgt, memory, fsmn_cache=None, opt_cache=None, chunk_size=None, look_back=0):
|
"""Compute decoded features.
|
|
Args:
|
tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
|
tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
|
memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
|
memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
|
cache (List[torch.Tensor]): List of cached tensors.
|
Each tensor shape should be (#batch, maxlen_out - 1, size).
|
|
Returns:
|
torch.Tensor: Output tensor(#batch, maxlen_out, size).
|
torch.Tensor: Mask for output tensor (#batch, maxlen_out).
|
torch.Tensor: Encoded memory (#batch, maxlen_in, size).
|
torch.Tensor: Encoded memory mask (#batch, maxlen_in).
|
|
"""
|
residual = tgt
|
if self.normalize_before:
|
tgt = self.norm1(tgt)
|
tgt = self.feed_forward(tgt)
|
|
x = tgt
|
if self.self_attn:
|
if self.normalize_before:
|
tgt = self.norm2(tgt)
|
x, fsmn_cache = self.self_attn(tgt, None, fsmn_cache)
|
x = residual + self.dropout(x)
|
|
if self.src_attn is not None:
|
residual = x
|
if self.normalize_before:
|
x = self.norm3(x)
|
|
x, opt_cache = self.src_attn.forward_chunk(x, memory, opt_cache, chunk_size, look_back)
|
x = residual + x
|
|
return x, memory, fsmn_cache, opt_cache
|
|
|
@tables.register("decoder_classes", "ParaformerSANMDecoder")
|
class ParaformerSANMDecoder(BaseTransformerDecoder):
|
"""
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
https://arxiv.org/abs/2006.01713
|
"""
|
def __init__(
|
self,
|
vocab_size: int,
|
encoder_output_size: int,
|
attention_heads: int = 4,
|
linear_units: int = 2048,
|
num_blocks: int = 6,
|
dropout_rate: float = 0.1,
|
positional_dropout_rate: float = 0.1,
|
self_attention_dropout_rate: float = 0.0,
|
src_attention_dropout_rate: float = 0.0,
|
input_layer: str = "embed",
|
use_output_layer: bool = True,
|
wo_input_layer: bool = False,
|
pos_enc_class=PositionalEncoding,
|
normalize_before: bool = True,
|
concat_after: bool = False,
|
att_layer_num: int = 6,
|
kernel_size: int = 21,
|
sanm_shfit: int = 0,
|
lora_list: List[str] = None,
|
lora_rank: int = 8,
|
lora_alpha: int = 16,
|
lora_dropout: float = 0.1,
|
chunk_multiply_factor: tuple = (1,),
|
tf2torch_tensor_name_prefix_torch: str = "decoder",
|
tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
|
):
|
super().__init__(
|
vocab_size=vocab_size,
|
encoder_output_size=encoder_output_size,
|
dropout_rate=dropout_rate,
|
positional_dropout_rate=positional_dropout_rate,
|
input_layer=input_layer,
|
use_output_layer=use_output_layer,
|
pos_enc_class=pos_enc_class,
|
normalize_before=normalize_before,
|
)
|
|
attention_dim = encoder_output_size
|
if wo_input_layer:
|
self.embed = None
|
else:
|
if input_layer == "embed":
|
self.embed = torch.nn.Sequential(
|
torch.nn.Embedding(vocab_size, attention_dim),
|
# pos_enc_class(attention_dim, positional_dropout_rate),
|
)
|
elif input_layer == "linear":
|
self.embed = torch.nn.Sequential(
|
torch.nn.Linear(vocab_size, attention_dim),
|
torch.nn.LayerNorm(attention_dim),
|
torch.nn.Dropout(dropout_rate),
|
torch.nn.ReLU(),
|
pos_enc_class(attention_dim, positional_dropout_rate),
|
)
|
else:
|
raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}")
|
|
self.normalize_before = normalize_before
|
if self.normalize_before:
|
self.after_norm = LayerNorm(attention_dim)
|
if use_output_layer:
|
self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
|
else:
|
self.output_layer = None
|
|
self.att_layer_num = att_layer_num
|
self.num_blocks = num_blocks
|
if sanm_shfit is None:
|
sanm_shfit = (kernel_size - 1) // 2
|
self.decoders = repeat(
|
att_layer_num,
|
lambda lnum: DecoderLayerSANM(
|
attention_dim,
|
MultiHeadedAttentionSANMDecoder(
|
attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
|
),
|
MultiHeadedAttentionCrossAtt(
|
attention_heads, attention_dim, src_attention_dropout_rate, lora_list, lora_rank, lora_alpha, lora_dropout
|
),
|
PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
|
dropout_rate,
|
normalize_before,
|
concat_after,
|
),
|
)
|
if num_blocks - att_layer_num <= 0:
|
self.decoders2 = None
|
else:
|
self.decoders2 = repeat(
|
num_blocks - att_layer_num,
|
lambda lnum: DecoderLayerSANM(
|
attention_dim,
|
MultiHeadedAttentionSANMDecoder(
|
attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=0
|
),
|
None,
|
PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
|
dropout_rate,
|
normalize_before,
|
concat_after,
|
),
|
)
|
|
self.decoders3 = repeat(
|
1,
|
lambda lnum: DecoderLayerSANM(
|
attention_dim,
|
None,
|
None,
|
PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
|
dropout_rate,
|
normalize_before,
|
concat_after,
|
),
|
)
|
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
|
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
|
self.chunk_multiply_factor = chunk_multiply_factor
|
|
def forward(
|
self,
|
hs_pad: torch.Tensor,
|
hlens: torch.Tensor,
|
ys_in_pad: torch.Tensor,
|
ys_in_lens: torch.Tensor,
|
return_hidden: bool = False,
|
return_both: bool= False,
|
chunk_mask: torch.Tensor = None,
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
"""Forward decoder.
|
|
Args:
|
hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
|
hlens: (batch)
|
ys_in_pad:
|
input token ids, int64 (batch, maxlen_out)
|
if input_layer == "embed"
|
input tensor (batch, maxlen_out, #mels) in the other cases
|
ys_in_lens: (batch)
|
Returns:
|
(tuple): tuple containing:
|
|
x: decoded token score before softmax (batch, maxlen_out, token)
|
if use_output_layer is True,
|
olens: (batch, )
|
"""
|
tgt = ys_in_pad
|
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
|
memory = hs_pad
|
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
if chunk_mask is not None:
|
memory_mask = memory_mask * chunk_mask
|
if tgt_mask.size(1) != memory_mask.size(1):
|
memory_mask = torch.cat((memory_mask, memory_mask[:, -2:-1, :]), dim=1)
|
|
x = tgt
|
x, tgt_mask, memory, memory_mask, _ = self.decoders(
|
x, tgt_mask, memory, memory_mask
|
)
|
if self.decoders2 is not None:
|
x, tgt_mask, memory, memory_mask, _ = self.decoders2(
|
x, tgt_mask, memory, memory_mask
|
)
|
x, tgt_mask, memory, memory_mask, _ = self.decoders3(
|
x, tgt_mask, memory, memory_mask
|
)
|
if self.normalize_before:
|
hidden = self.after_norm(x)
|
|
olens = tgt_mask.sum(1)
|
if self.output_layer is not None and return_hidden is False:
|
x = self.output_layer(hidden)
|
return x, olens
|
if return_both:
|
x = self.output_layer(hidden)
|
return x, hidden, olens
|
return hidden, olens
|
|
def score(self, ys, state, x):
|
"""Score."""
|
ys_mask = myutils.sequence_mask(torch.tensor([len(ys)], dtype=torch.int32), device=x.device)[:, :, None]
|
logp, state = self.forward_one_step(
|
ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state
|
)
|
return logp.squeeze(0), state
|
|
def forward_asf2(
|
self,
|
hs_pad: torch.Tensor,
|
hlens: torch.Tensor,
|
ys_in_pad: torch.Tensor,
|
ys_in_lens: torch.Tensor,
|
):
|
|
tgt = ys_in_pad
|
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
|
memory = hs_pad
|
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[0](tgt, tgt_mask, memory, memory_mask)
|
attn_mat = self.model.decoders[1].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
|
return attn_mat
|
|
def forward_asf6(
|
self,
|
hs_pad: torch.Tensor,
|
hlens: torch.Tensor,
|
ys_in_pad: torch.Tensor,
|
ys_in_lens: torch.Tensor,
|
):
|
|
tgt = ys_in_pad
|
tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
|
memory = hs_pad
|
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[0](tgt, tgt_mask, memory, memory_mask)
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[1](tgt, tgt_mask, memory, memory_mask)
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[2](tgt, tgt_mask, memory, memory_mask)
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[3](tgt, tgt_mask, memory, memory_mask)
|
tgt, tgt_mask, memory, memory_mask, _ = self.decoders[4](tgt, tgt_mask, memory, memory_mask)
|
attn_mat = self.decoders[5].get_attn_mat(tgt, tgt_mask, memory, memory_mask)
|
return attn_mat
|
|
def forward_chunk(
|
self,
|
memory: torch.Tensor,
|
tgt: torch.Tensor,
|
cache: dict = None,
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
"""Forward decoder.
|
|
Args:
|
hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
|
hlens: (batch)
|
ys_in_pad:
|
input token ids, int64 (batch, maxlen_out)
|
if input_layer == "embed"
|
input tensor (batch, maxlen_out, #mels) in the other cases
|
ys_in_lens: (batch)
|
Returns:
|
(tuple): tuple containing:
|
|
x: decoded token score before softmax (batch, maxlen_out, token)
|
if use_output_layer is True,
|
olens: (batch, )
|
"""
|
x = tgt
|
if cache["decode_fsmn"] is None:
|
cache_layer_num = len(self.decoders)
|
if self.decoders2 is not None:
|
cache_layer_num += len(self.decoders2)
|
fsmn_cache = [None] * cache_layer_num
|
else:
|
fsmn_cache = cache["decode_fsmn"]
|
|
if cache["opt"] is None:
|
cache_layer_num = len(self.decoders)
|
opt_cache = [None] * cache_layer_num
|
else:
|
opt_cache = cache["opt"]
|
|
for i in range(self.att_layer_num):
|
decoder = self.decoders[i]
|
x, memory, fsmn_cache[i], opt_cache[i] = decoder.forward_chunk(
|
x, memory, fsmn_cache=fsmn_cache[i], opt_cache=opt_cache[i],
|
chunk_size=cache["chunk_size"], look_back=cache["decoder_chunk_look_back"]
|
)
|
|
if self.num_blocks - self.att_layer_num > 1:
|
for i in range(self.num_blocks - self.att_layer_num):
|
j = i + self.att_layer_num
|
decoder = self.decoders2[i]
|
x, memory, fsmn_cache[j], _ = decoder.forward_chunk(
|
x, memory, fsmn_cache=fsmn_cache[j]
|
)
|
|
for decoder in self.decoders3:
|
x, memory, _, _ = decoder.forward_chunk(
|
x, memory
|
)
|
if self.normalize_before:
|
x = self.after_norm(x)
|
if self.output_layer is not None:
|
x = self.output_layer(x)
|
|
cache["decode_fsmn"] = fsmn_cache
|
if cache["decoder_chunk_look_back"] > 0 or cache["decoder_chunk_look_back"] == -1:
|
cache["opt"] = opt_cache
|
return x
|
|
def forward_one_step(
|
self,
|
tgt: torch.Tensor,
|
tgt_mask: torch.Tensor,
|
memory: torch.Tensor,
|
cache: List[torch.Tensor] = None,
|
) -> Tuple[torch.Tensor, List[torch.Tensor]]:
|
"""Forward one step.
|
|
Args:
|
tgt: input token ids, int64 (batch, maxlen_out)
|
tgt_mask: input token mask, (batch, maxlen_out)
|
dtype=torch.uint8 in PyTorch 1.2-
|
dtype=torch.bool in PyTorch 1.2+ (include 1.2)
|
memory: encoded memory, float32 (batch, maxlen_in, feat)
|
cache: cached output list of (batch, max_time_out-1, size)
|
Returns:
|
y, cache: NN output value and cache per `self.decoders`.
|
y.shape` is (batch, maxlen_out, token)
|
"""
|
x = self.embed(tgt)
|
if cache is None:
|
cache_layer_num = len(self.decoders)
|
if self.decoders2 is not None:
|
cache_layer_num += len(self.decoders2)
|
cache = [None] * cache_layer_num
|
new_cache = []
|
# for c, decoder in zip(cache, self.decoders):
|
for i in range(self.att_layer_num):
|
decoder = self.decoders[i]
|
c = cache[i]
|
x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
|
x, tgt_mask, memory, None, cache=c
|
)
|
new_cache.append(c_ret)
|
|
if self.num_blocks - self.att_layer_num > 1:
|
for i in range(self.num_blocks - self.att_layer_num):
|
j = i + self.att_layer_num
|
decoder = self.decoders2[i]
|
c = cache[j]
|
x, tgt_mask, memory, memory_mask, c_ret = decoder.forward_one_step(
|
x, tgt_mask, memory, None, cache=c
|
)
|
new_cache.append(c_ret)
|
|
for decoder in self.decoders3:
|
|
x, tgt_mask, memory, memory_mask, _ = decoder.forward_one_step(
|
x, tgt_mask, memory, None, cache=None
|
)
|
|
if self.normalize_before:
|
y = self.after_norm(x[:, -1])
|
else:
|
y = x[:, -1]
|
if self.output_layer is not None:
|
y = torch.log_softmax(self.output_layer(y), dim=-1)
|
|
return y, new_cache
|
|
class DecoderLayerSANMExport(torch.nn.Module):
|
|
def __init__(
|
self,
|
model
|
):
|
super().__init__()
|
self.self_attn = model.self_attn
|
self.src_attn = model.src_attn
|
self.feed_forward = model.feed_forward
|
self.norm1 = model.norm1
|
self.norm2 = model.norm2 if hasattr(model, 'norm2') else None
|
self.norm3 = model.norm3 if hasattr(model, 'norm3') else None
|
self.size = model.size
|
|
|
def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
|
|
residual = tgt
|
tgt = self.norm1(tgt)
|
tgt = self.feed_forward(tgt)
|
|
x = tgt
|
if self.self_attn is not None:
|
tgt = self.norm2(tgt)
|
x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
|
x = residual + x
|
|
if self.src_attn is not None:
|
residual = x
|
x = self.norm3(x)
|
x = residual + self.src_attn(x, memory, memory_mask)
|
|
|
return x, tgt_mask, memory, memory_mask, cache
|
|
|
@tables.register("decoder_classes", "ParaformerSANMDecoderExport")
|
class ParaformerSANMDecoderExport(torch.nn.Module):
|
def __init__(self, model,
|
max_seq_len=512,
|
model_name='decoder',
|
onnx: bool = True, ):
|
super().__init__()
|
# self.embed = model.embed #Embedding(model.embed, max_seq_len)
|
from funasr.utils.torch_function import MakePadMask
|
from funasr.utils.torch_function import sequence_mask
|
|
self.model = model
|
if onnx:
|
self.make_pad_mask = MakePadMask(max_seq_len, flip=False)
|
else:
|
self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
|
|
from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoderExport
|
from funasr.models.sanm.attention import MultiHeadedAttentionCrossAttExport
|
|
|
for i, d in enumerate(self.model.decoders):
|
if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
|
d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
|
if isinstance(d.src_attn, MultiHeadedAttentionCrossAtt):
|
d.src_attn = MultiHeadedAttentionCrossAttExport(d.src_attn)
|
self.model.decoders[i] = DecoderLayerSANMExport(d)
|
|
if self.model.decoders2 is not None:
|
for i, d in enumerate(self.model.decoders2):
|
if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
|
d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
|
self.model.decoders2[i] = DecoderLayerSANMExport(d)
|
|
for i, d in enumerate(self.model.decoders3):
|
self.model.decoders3[i] = DecoderLayerSANMExport(d)
|
|
self.output_layer = model.output_layer
|
self.after_norm = model.after_norm
|
self.model_name = model_name
|
|
def prepare_mask(self, mask):
|
mask_3d_btd = mask[:, :, None]
|
if len(mask.shape) == 2:
|
mask_4d_bhlt = 1 - mask[:, None, None, :]
|
elif len(mask.shape) == 3:
|
mask_4d_bhlt = 1 - mask[:, None, :]
|
mask_4d_bhlt = mask_4d_bhlt * -10000.0
|
|
return mask_3d_btd, mask_4d_bhlt
|
|
def forward(
|
self,
|
hs_pad: torch.Tensor,
|
hlens: torch.Tensor,
|
ys_in_pad: torch.Tensor,
|
ys_in_lens: torch.Tensor,
|
):
|
|
tgt = ys_in_pad
|
tgt_mask = self.make_pad_mask(ys_in_lens)
|
tgt_mask, _ = self.prepare_mask(tgt_mask)
|
# tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
|
|
memory = hs_pad
|
memory_mask = self.make_pad_mask(hlens)
|
_, memory_mask = self.prepare_mask(memory_mask)
|
# memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
|
x = tgt
|
x, tgt_mask, memory, memory_mask, _ = self.model.decoders(
|
x, tgt_mask, memory, memory_mask
|
)
|
if self.model.decoders2 is not None:
|
x, tgt_mask, memory, memory_mask, _ = self.model.decoders2(
|
x, tgt_mask, memory, memory_mask
|
)
|
x, tgt_mask, memory, memory_mask, _ = self.model.decoders3(
|
x, tgt_mask, memory, memory_mask
|
)
|
x = self.after_norm(x)
|
x = self.output_layer(x)
|
|
return x, ys_in_lens
|
|
def get_dummy_inputs(self, enc_size):
|
tgt = torch.LongTensor([0]).unsqueeze(0)
|
memory = torch.randn(1, 100, enc_size)
|
pre_acoustic_embeds = torch.randn(1, 1, enc_size)
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
cache = [
|
torch.zeros((1, self.model.decoders[0].size, self.model.decoders[0].self_attn.kernel_size))
|
for _ in range(cache_num)
|
]
|
return (tgt, memory, pre_acoustic_embeds, cache)
|
|
def is_optimizable(self):
|
return True
|
|
def get_input_names(self):
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
return ['tgt', 'memory', 'pre_acoustic_embeds'] \
|
+ ['cache_%d' % i for i in range(cache_num)]
|
|
def get_output_names(self):
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
return ['y'] \
|
+ ['out_cache_%d' % i for i in range(cache_num)]
|
|
def get_dynamic_axes(self):
|
ret = {
|
'tgt': {
|
0: 'tgt_batch',
|
1: 'tgt_length'
|
},
|
'memory': {
|
0: 'memory_batch',
|
1: 'memory_length'
|
},
|
'pre_acoustic_embeds': {
|
0: 'acoustic_embeds_batch',
|
1: 'acoustic_embeds_length',
|
}
|
}
|
cache_num = len(self.model.decoders) + len(self.model.decoders2)
|
ret.update({
|
'cache_%d' % d: {
|
0: 'cache_%d_batch' % d,
|
2: 'cache_%d_length' % d
|
}
|
for d in range(cache_num)
|
})
|
return ret
|
|
|
|
@tables.register("decoder_classes", "ParaformerSANDecoder")
|
class ParaformerSANDecoder(BaseTransformerDecoder):
|
"""
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
https://arxiv.org/abs/2006.01713
|
"""
|
def __init__(
|
self,
|
vocab_size: int,
|
encoder_output_size: int,
|
attention_heads: int = 4,
|
linear_units: int = 2048,
|
num_blocks: int = 6,
|
dropout_rate: float = 0.1,
|
positional_dropout_rate: float = 0.1,
|
self_attention_dropout_rate: float = 0.0,
|
src_attention_dropout_rate: float = 0.0,
|
input_layer: str = "embed",
|
use_output_layer: bool = True,
|
pos_enc_class=PositionalEncoding,
|
normalize_before: bool = True,
|
concat_after: bool = False,
|
embeds_id: int = -1,
|
):
|
super().__init__(
|
vocab_size=vocab_size,
|
encoder_output_size=encoder_output_size,
|
dropout_rate=dropout_rate,
|
positional_dropout_rate=positional_dropout_rate,
|
input_layer=input_layer,
|
use_output_layer=use_output_layer,
|
pos_enc_class=pos_enc_class,
|
normalize_before=normalize_before,
|
)
|
|
attention_dim = encoder_output_size
|
self.decoders = repeat(
|
num_blocks,
|
lambda lnum: DecoderLayer(
|
attention_dim,
|
MultiHeadedAttention(
|
attention_heads, attention_dim, self_attention_dropout_rate
|
),
|
MultiHeadedAttention(
|
attention_heads, attention_dim, src_attention_dropout_rate
|
),
|
PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
|
dropout_rate,
|
normalize_before,
|
concat_after,
|
),
|
)
|
self.embeds_id = embeds_id
|
self.attention_dim = attention_dim
|
|
def forward(
|
self,
|
hs_pad: torch.Tensor,
|
hlens: torch.Tensor,
|
ys_in_pad: torch.Tensor,
|
ys_in_lens: torch.Tensor,
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
"""Forward decoder.
|
|
Args:
|
hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
|
hlens: (batch)
|
ys_in_pad:
|
input token ids, int64 (batch, maxlen_out)
|
if input_layer == "embed"
|
input tensor (batch, maxlen_out, #mels) in the other cases
|
ys_in_lens: (batch)
|
Returns:
|
(tuple): tuple containing:
|
|
x: decoded token score before softmax (batch, maxlen_out, token)
|
if use_output_layer is True,
|
olens: (batch, )
|
"""
|
tgt = ys_in_pad
|
tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device)
|
|
memory = hs_pad
|
memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to(
|
memory.device
|
)
|
# Padding for Longformer
|
if memory_mask.shape[-1] != memory.shape[1]:
|
padlen = memory.shape[1] - memory_mask.shape[-1]
|
memory_mask = torch.nn.functional.pad(
|
memory_mask, (0, padlen), "constant", False
|
)
|
|
# x = self.embed(tgt)
|
x = tgt
|
embeds_outputs = None
|
for layer_id, decoder in enumerate(self.decoders):
|
x, tgt_mask, memory, memory_mask = decoder(
|
x, tgt_mask, memory, memory_mask
|
)
|
if layer_id == self.embeds_id:
|
embeds_outputs = x
|
if self.normalize_before:
|
x = self.after_norm(x)
|
if self.output_layer is not None:
|
x = self.output_layer(x)
|
|
olens = tgt_mask.sum(1)
|
if embeds_outputs is not None:
|
return x, olens, embeds_outputs
|
else:
|
return x, olens
|
