From 28ccfbfc51068a663a80764e14074df5edf2b5ba Mon Sep 17 00:00:00 2001
From: kongdeqiang <kongdeqiang960204@163.com>
Date: 星期五, 13 三月 2026 17:41:41 +0800
Subject: [PATCH] 提交
---
funasr/models/contextual_paraformer/decoder.py | 670 ++++++++++++++-----------------------------------------
1 files changed, 170 insertions(+), 500 deletions(-)
diff --git a/funasr/models/contextual_paraformer/decoder.py b/funasr/models/contextual_paraformer/decoder.py
index 5ec2756..ba2ce9a 100644
--- a/funasr/models/contextual_paraformer/decoder.py
+++ b/funasr/models/contextual_paraformer/decoder.py
@@ -1,22 +1,27 @@
-from typing import List
-from typing import Tuple
-import logging
+#!/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
-import torch.nn as nn
+import logging
import numpy as np
-
-from funasr.models.scama import utils as myutils
-
-from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
-from funasr.models.transformer.embedding import PositionalEncoding
-from funasr.models.transformer.layer_norm import LayerNorm
-from funasr.models.sanm.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
-from funasr.models.transformer.utils.repeat import repeat
-from funasr.models.paraformer.decoder import DecoderLayerSANM, ParaformerSANMDecoder
+from typing import 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.layer_norm import LayerNorm
+from funasr.models.transformer.embedding import PositionalEncoding
+from funasr.models.paraformer.decoder import DecoderLayerSANM, ParaformerSANMDecoder
+from funasr.models.sanm.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
+from funasr.models.sanm.attention import (
+ MultiHeadedAttentionSANMDecoder,
+ MultiHeadedAttentionCrossAtt,
+)
-class ContextualDecoderLayer(nn.Module):
+
+class ContextualDecoderLayer(torch.nn.Module):
def __init__(
self,
size,
@@ -38,14 +43,21 @@
self.norm2 = LayerNorm(size)
if src_attn is not None:
self.norm3 = LayerNorm(size)
- self.dropout = nn.Dropout(dropout_rate)
+ self.dropout = torch.nn.Dropout(dropout_rate)
self.normalize_before = normalize_before
self.concat_after = concat_after
if self.concat_after:
- self.concat_linear1 = nn.Linear(size + size, size)
- self.concat_linear2 = nn.Linear(size + size, size)
+ self.concat_linear1 = torch.nn.Linear(size + size, size)
+ self.concat_linear2 = torch.nn.Linear(size + size, size)
- def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None,):
+ def forward(
+ self,
+ tgt,
+ tgt_mask,
+ memory,
+ memory_mask,
+ cache=None,
+ ):
# tgt = self.dropout(tgt)
if isinstance(tgt, Tuple):
tgt, _ = tgt
@@ -73,7 +85,7 @@
return x, tgt_mask, x_self_attn, x_src_attn
-class ContextualBiasDecoder(nn.Module):
+class ContextualBiasDecoder(torch.nn.Module):
def __init__(
self,
size,
@@ -87,7 +99,7 @@
self.src_attn = src_attn
if src_attn is not None:
self.norm3 = LayerNorm(size)
- self.dropout = nn.Dropout(dropout_rate)
+ self.dropout = torch.nn.Dropout(dropout_rate)
self.normalize_before = normalize_before
def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
@@ -95,8 +107,9 @@
if self.src_attn is not None:
if self.normalize_before:
x = self.norm3(x)
- x = self.dropout(self.src_attn(x, memory, memory_mask))
+ x = self.dropout(self.src_attn(x, memory, memory_mask))
return x, tgt_mask, memory, memory_mask, cache
+
@tables.register("decoder_classes", "ContextualParaformerDecoder")
class ContextualParaformerDecoder(ParaformerSANMDecoder):
@@ -105,6 +118,7 @@
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,
@@ -137,7 +151,7 @@
)
attention_dim = encoder_output_size
- if input_layer == 'none':
+ if input_layer == "none":
self.embed = None
if input_layer == "embed":
self.embed = torch.nn.Sequential(
@@ -183,7 +197,7 @@
concat_after,
),
)
- self.dropout = nn.Dropout(dropout_rate)
+ self.dropout = torch.nn.Dropout(dropout_rate)
self.bias_decoder = ContextualBiasDecoder(
size=attention_dim,
src_attn=MultiHeadedAttentionCrossAtt(
@@ -192,20 +206,20 @@
dropout_rate=dropout_rate,
normalize_before=True,
)
- self.bias_output = torch.nn.Conv1d(attention_dim*2, attention_dim, 1, bias=False)
+ self.bias_output = torch.nn.Conv1d(attention_dim * 2, attention_dim, 1, bias=False)
self.last_decoder = ContextualDecoderLayer(
- attention_dim,
- MultiHeadedAttentionSANMDecoder(
- attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
- ),
- MultiHeadedAttentionCrossAtt(
- attention_heads, attention_dim, src_attention_dropout_rate
- ),
- PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
- dropout_rate,
- normalize_before,
- concat_after,
- )
+ attention_dim,
+ MultiHeadedAttentionSANMDecoder(
+ attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
+ ),
+ MultiHeadedAttentionCrossAtt(
+ attention_heads, attention_dim, src_attention_dropout_rate
+ ),
+ PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
+ dropout_rate,
+ normalize_before,
+ concat_after,
+ )
if num_blocks - att_layer_num <= 0:
self.decoders2 = None
else:
@@ -271,31 +285,25 @@
memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
x = tgt
- x, tgt_mask, memory, memory_mask, _ = self.decoders(
- x, tgt_mask, memory, memory_mask
- )
- _, _, x_self_attn, x_src_attn = self.last_decoder(
- x, tgt_mask, memory, memory_mask
- )
+ x, tgt_mask, memory, memory_mask, _ = self.decoders(x, tgt_mask, memory, memory_mask)
+ _, _, x_self_attn, x_src_attn = self.last_decoder(x, tgt_mask, memory, memory_mask)
# contextual paraformer related
contextual_length = torch.Tensor([contextual_info.shape[1]]).int().repeat(hs_pad.shape[0])
contextual_mask = myutils.sequence_mask(contextual_length, device=memory.device)[:, None, :]
- cx, tgt_mask, _, _, _ = self.bias_decoder(x_self_attn, tgt_mask, contextual_info, memory_mask=contextual_mask)
+ cx, tgt_mask, _, _, _ = self.bias_decoder(
+ x_self_attn, tgt_mask, contextual_info, memory_mask=contextual_mask
+ )
if self.bias_output is not None:
- x = torch.cat([x_src_attn, cx*clas_scale], dim=2)
+ x = torch.cat([x_src_attn, cx * clas_scale], dim=2)
x = self.bias_output(x.transpose(1, 2)).transpose(1, 2) # 2D -> D
x = x_self_attn + self.dropout(x)
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.decoders2(x, tgt_mask, memory, memory_mask)
- x, tgt_mask, memory, memory_mask, _ = self.decoders3(
- x, tgt_mask, memory, memory_mask
- )
+ x, tgt_mask, memory, memory_mask, _ = self.decoders3(x, tgt_mask, memory, memory_mask)
if self.normalize_before:
x = self.after_norm(x)
olens = tgt_mask.sum(1)
@@ -303,473 +311,135 @@
x = self.output_layer(x)
return x, olens
- def gen_tf2torch_map_dict(self):
- tensor_name_prefix_torch = self.tf2torch_tensor_name_prefix_torch
- tensor_name_prefix_tf = self.tf2torch_tensor_name_prefix_tf
- map_dict_local = {
+@tables.register("decoder_classes", "ContextualParaformerDecoderExport")
+class ContextualParaformerDecoderExport(torch.nn.Module):
+ def __init__(
+ self,
+ model,
+ max_seq_len=512,
+ model_name="decoder",
+ onnx: bool = True,
+ **kwargs,
+ ):
+ super().__init__()
+ from funasr.utils.torch_function import sequence_mask
- ## decoder
- # ffn
- "{}.decoders.layeridx.norm1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.norm1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.decoders.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders.layeridx.feed_forward.norm.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders.layeridx.feed_forward.norm.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/LayerNorm_1/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_ffn/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,1024),(1,1024,256)
+ self.model = model
+ self.make_pad_mask = sequence_mask(max_seq_len, flip=False)
- # fsmn
- "{}.decoders.layeridx.norm2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/gamma".format(
- tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.norm2.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/LayerNorm/beta".format(
- tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.self_attn.fsmn_block.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/decoder_memory_block/depth_conv_w".format(
- tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 2, 0),
- }, # (256,1,31),(1,31,256,1)
- # src att
- "{}.decoders.layeridx.norm3.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.norm3.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.src_attn.linear_q.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,256),(1,256,256)
- "{}.decoders.layeridx.src_attn.linear_q.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders.layeridx.src_attn.linear_k_v.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.decoders.layeridx.src_attn.linear_k_v.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_1/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders.layeridx.src_attn.linear_out.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,256),(1,256,256)
- "{}.decoders.layeridx.src_attn.linear_out.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_layeridx/multi_head/conv1d_2/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- # dnn
- "{}.decoders3.layeridx.norm1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders3.layeridx.norm1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.decoders3.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.decoders3.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders3.layeridx.feed_forward.norm.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders3.layeridx.feed_forward.norm.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/LayerNorm_1/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.decoders3.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_dnn_layer_layeridx/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,1024),(1,1024,256)
+ from funasr.models.sanm.attention import MultiHeadedAttentionSANMDecoderExport
+ from funasr.models.sanm.attention import MultiHeadedAttentionCrossAttExport
+ from funasr.models.paraformer.decoder import DecoderLayerSANMExport
+ from funasr.models.transformer.positionwise_feed_forward import (
+ PositionwiseFeedForwardDecoderSANMExport,
+ )
- # embed_concat_ffn
- "{}.embed_concat_ffn.layeridx.norm1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.embed_concat_ffn.layeridx.norm1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.embed_concat_ffn.layeridx.feed_forward.w_1.weight".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.embed_concat_ffn.layeridx.feed_forward.w_1.bias".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.embed_concat_ffn.layeridx.feed_forward.norm.weight".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/LayerNorm_1/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.embed_concat_ffn.layeridx.feed_forward.norm.bias".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/LayerNorm_1/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.embed_concat_ffn.layeridx.feed_forward.w_2.weight".format(tensor_name_prefix_torch):
- {"name": "{}/cif_concat/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,1024),(1,1024,256)
+ for i, d in enumerate(self.model.decoders):
+ if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
+ d.feed_forward = PositionwiseFeedForwardDecoderSANMExport(d.feed_forward)
+ 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)
- # out norm
- "{}.after_norm.weight".format(tensor_name_prefix_torch):
- {"name": "{}/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.after_norm.bias".format(tensor_name_prefix_torch):
- {"name": "{}/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
+ if self.model.decoders2 is not None:
+ for i, d in enumerate(self.model.decoders2):
+ if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
+ d.feed_forward = PositionwiseFeedForwardDecoderSANMExport(d.feed_forward)
+ if isinstance(d.self_attn, MultiHeadedAttentionSANMDecoder):
+ d.self_attn = MultiHeadedAttentionSANMDecoderExport(d.self_attn)
+ self.model.decoders2[i] = DecoderLayerSANMExport(d)
- # in embed
- "{}.embed.0.weight".format(tensor_name_prefix_torch):
- {"name": "{}/w_embs".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (4235,256),(4235,256)
+ for i, d in enumerate(self.model.decoders3):
+ if isinstance(d.feed_forward, PositionwiseFeedForwardDecoderSANM):
+ d.feed_forward = PositionwiseFeedForwardDecoderSANMExport(d.feed_forward)
+ self.model.decoders3[i] = DecoderLayerSANMExport(d)
- # out layer
- "{}.output_layer.weight".format(tensor_name_prefix_torch):
- {"name": ["{}/dense/kernel".format(tensor_name_prefix_tf), "{}/w_embs".format(tensor_name_prefix_tf)],
- "squeeze": [None, None],
- "transpose": [(1, 0), None],
- }, # (4235,256),(256,4235)
- "{}.output_layer.bias".format(tensor_name_prefix_torch):
- {"name": ["{}/dense/bias".format(tensor_name_prefix_tf),
- "seq2seq/2bias" if tensor_name_prefix_tf == "seq2seq/decoder/inputter_1" else "seq2seq/bias"],
- "squeeze": [None, None],
- "transpose": [None, None],
- }, # (4235,),(4235,)
+ self.output_layer = model.output_layer
+ self.after_norm = model.after_norm
+ self.model_name = model_name
- ## clas decoder
- # src att
- "{}.bias_decoder.norm3.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_15/multi_head_1/LayerNorm/gamma".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.bias_decoder.norm3.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_15/multi_head_1/LayerNorm/beta".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.bias_decoder.src_attn.linear_q.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,256),(1,256,256)
- "{}.bias_decoder.src_attn.linear_q.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- "{}.bias_decoder.src_attn.linear_k_v.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_1/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (1024,256),(1,256,1024)
- "{}.bias_decoder.src_attn.linear_k_v.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_1/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (1024,),(1024,)
- "{}.bias_decoder.src_attn.linear_out.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_2/kernel".format(tensor_name_prefix_tf),
- "squeeze": 0,
- "transpose": (1, 0),
- }, # (256,256),(1,256,256)
- "{}.bias_decoder.src_attn.linear_out.bias".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_15/multi_head_1/conv1d_2/bias".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": None,
- }, # (256,),(256,)
- # dnn
- "{}.bias_output.weight".format(tensor_name_prefix_torch):
- {"name": "{}/decoder_fsmn_layer_15/conv1d/kernel".format(tensor_name_prefix_tf),
- "squeeze": None,
- "transpose": (2, 1, 0),
- }, # (1024,256),(1,256,1024)
+ # bias decoder
+ if isinstance(self.model.bias_decoder.src_attn, MultiHeadedAttentionCrossAtt):
+ self.model.bias_decoder.src_attn = MultiHeadedAttentionCrossAttExport(
+ self.model.bias_decoder.src_attn
+ )
+ self.bias_decoder = self.model.bias_decoder
- }
- return map_dict_local
+ # last decoder
+ if isinstance(self.model.last_decoder.src_attn, MultiHeadedAttentionCrossAtt):
+ self.model.last_decoder.src_attn = MultiHeadedAttentionCrossAttExport(
+ self.model.last_decoder.src_attn
+ )
+ if isinstance(self.model.last_decoder.self_attn, MultiHeadedAttentionSANMDecoder):
+ self.model.last_decoder.self_attn = MultiHeadedAttentionSANMDecoderExport(
+ self.model.last_decoder.self_attn
+ )
+ if isinstance(self.model.last_decoder.feed_forward, PositionwiseFeedForwardDecoderSANM):
+ self.model.last_decoder.feed_forward = PositionwiseFeedForwardDecoderSANMExport(
+ self.model.last_decoder.feed_forward
+ )
+ self.last_decoder = self.model.last_decoder
+ self.bias_output = self.model.bias_output
+ self.dropout = self.model.dropout
- def convert_tf2torch(self,
- var_dict_tf,
- var_dict_torch,
- ):
- map_dict = self.gen_tf2torch_map_dict()
- var_dict_torch_update = dict()
- decoder_layeridx_sets = set()
- for name in sorted(var_dict_torch.keys(), reverse=False):
- names = name.split('.')
- if names[0] == self.tf2torch_tensor_name_prefix_torch:
- if names[1] == "decoders":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
- layeridx_bias = 0
- layeridx += layeridx_bias
- decoder_layeridx_sets.add(layeridx)
- if name_q in map_dict.keys():
- name_v = map_dict[name_q]["name"]
- name_tf = name_v.replace("layeridx", "{}".format(layeridx))
- data_tf = var_dict_tf[name_tf]
- if map_dict[name_q]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
- if map_dict[name_q]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
- var_dict_tf[name_tf].shape))
- elif names[1] == "last_decoder":
- layeridx = 15
- name_q = name.replace("last_decoder", "decoders.layeridx")
- layeridx_bias = 0
- layeridx += layeridx_bias
- decoder_layeridx_sets.add(layeridx)
- if name_q in map_dict.keys():
- name_v = map_dict[name_q]["name"]
- name_tf = name_v.replace("layeridx", "{}".format(layeridx))
- data_tf = var_dict_tf[name_tf]
- if map_dict[name_q]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
- if map_dict[name_q]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
- var_dict_tf[name_tf].shape))
+ 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
- elif names[1] == "decoders2":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
- name_q = name_q.replace("decoders2", "decoders")
- layeridx_bias = len(decoder_layeridx_sets)
+ def forward(
+ self,
+ hs_pad: torch.Tensor,
+ hlens: torch.Tensor,
+ ys_in_pad: torch.Tensor,
+ ys_in_lens: torch.Tensor,
+ bias_embed: torch.Tensor,
+ ):
- layeridx += layeridx_bias
- if "decoders." in name:
- decoder_layeridx_sets.add(layeridx)
- if name_q in map_dict.keys():
- name_v = map_dict[name_q]["name"]
- name_tf = name_v.replace("layeridx", "{}".format(layeridx))
- data_tf = var_dict_tf[name_tf]
- if map_dict[name_q]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
- if map_dict[name_q]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
- var_dict_tf[name_tf].shape))
+ 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]
- elif names[1] == "decoders3":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
+ 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, :]
- layeridx_bias = 0
- layeridx += layeridx_bias
- if "decoders." in name:
- decoder_layeridx_sets.add(layeridx)
- if name_q in map_dict.keys():
- name_v = map_dict[name_q]["name"]
- name_tf = name_v.replace("layeridx", "{}".format(layeridx))
- data_tf = var_dict_tf[name_tf]
- if map_dict[name_q]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
- if map_dict[name_q]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
- var_dict_tf[name_tf].shape))
- elif names[1] == "bias_decoder":
- name_q = name
+ x = tgt
+ x, tgt_mask, memory, memory_mask, _ = self.model.decoders(x, tgt_mask, memory, memory_mask)
- if name_q in map_dict.keys():
- name_v = map_dict[name_q]["name"]
- name_tf = name_v
- data_tf = var_dict_tf[name_tf]
- if map_dict[name_q]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
- if map_dict[name_q]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
- var_dict_tf[name_tf].shape))
+ _, _, x_self_attn, x_src_attn = self.last_decoder(x, tgt_mask, memory, memory_mask)
+ # contextual paraformer related
+ contextual_length = torch.Tensor([bias_embed.shape[1]]).int().repeat(hs_pad.shape[0])
+ # contextual_mask = myutils.sequence_mask(contextual_length, device=memory.device)[:, None, :]
+ contextual_mask = self.make_pad_mask(contextual_length)
+ contextual_mask, _ = self.prepare_mask(contextual_mask)
+ contextual_mask = contextual_mask.transpose(2, 1).unsqueeze(1)
+ cx, tgt_mask, _, _, _ = self.bias_decoder(
+ x_self_attn, tgt_mask, bias_embed, memory_mask=contextual_mask
+ )
- elif names[1] == "embed" or names[1] == "output_layer" or names[1] == "bias_output":
- name_tf = map_dict[name]["name"]
- if isinstance(name_tf, list):
- idx_list = 0
- if name_tf[idx_list] in var_dict_tf.keys():
- pass
- else:
- idx_list = 1
- data_tf = var_dict_tf[name_tf[idx_list]]
- if map_dict[name]["squeeze"][idx_list] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"][idx_list])
- if map_dict[name]["transpose"][idx_list] is not None:
- data_tf = np.transpose(data_tf, map_dict[name]["transpose"][idx_list])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info("torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(),
- name_tf[idx_list],
- var_dict_tf[name_tf[
- idx_list]].shape))
+ if self.bias_output is not None:
+ x = torch.cat([x_src_attn, cx], dim=2)
+ x = self.bias_output(x.transpose(1, 2)).transpose(1, 2) # 2D -> D
+ x = x_self_attn + self.dropout(x)
- else:
- data_tf = var_dict_tf[name_tf]
- if map_dict[name]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name]["squeeze"])
- if map_dict[name]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name]["transpose"])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
- var_dict_tf[name_tf].shape))
+ 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)
- elif names[1] == "after_norm":
- name_tf = map_dict[name]["name"]
- data_tf = var_dict_tf[name_tf]
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_tf,
- var_dict_tf[name_tf].shape))
-
- elif names[1] == "embed_concat_ffn":
- layeridx = int(names[2])
- name_q = name.replace(".{}.".format(layeridx), ".layeridx.")
-
- layeridx_bias = 0
- layeridx += layeridx_bias
- if "decoders." in name:
- decoder_layeridx_sets.add(layeridx)
- if name_q in map_dict.keys():
- name_v = map_dict[name_q]["name"]
- name_tf = name_v.replace("layeridx", "{}".format(layeridx))
- data_tf = var_dict_tf[name_tf]
- if map_dict[name_q]["squeeze"] is not None:
- data_tf = np.squeeze(data_tf, axis=map_dict[name_q]["squeeze"])
- if map_dict[name_q]["transpose"] is not None:
- data_tf = np.transpose(data_tf, map_dict[name_q]["transpose"])
- data_tf = torch.from_numpy(data_tf).type(torch.float32).to("cpu")
- assert var_dict_torch[name].size() == data_tf.size(), "{}, {}, {} != {}".format(name, name_tf,
- var_dict_torch[
- name].size(),
- data_tf.size())
- var_dict_torch_update[name] = data_tf
- logging.info(
- "torch tensor: {}, {}, loading from tf tensor: {}, {}".format(name, data_tf.size(), name_v,
- var_dict_tf[name_tf].shape))
-
- return var_dict_torch_update
+ return x, ys_in_lens
--
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