from typing import List
|
from typing import Tuple
|
import logging
|
import torch
|
import torch.nn as nn
|
import numpy as np
|
|
from funasr.models.scama import utils as myutils
|
from funasr.models.transformer.decoder import BaseTransformerDecoder
|
|
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.register import tables
|
|
class DecoderLayerSANM(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 = 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)
|
|
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)
|
|
x = residual + self.dropout(self.src_attn(x, memory, memory_mask))
|
|
return x, tgt_mask, memory, memory_mask, cache
|
|
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", "FsmnDecoderSCAMAOpt")
|
class FsmnDecoderSCAMAOpt(BaseTransformerDecoder):
|
"""
|
Author: Speech Lab of DAMO Academy, Alibaba Group
|
SCAMA: Streaming chunk-aware multihead attention for online 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,
|
att_layer_num: int = 6,
|
kernel_size: int = 21,
|
sanm_shfit: int = None,
|
concat_embeds: bool = False,
|
attention_dim: int = None,
|
tf2torch_tensor_name_prefix_torch: str = "decoder",
|
tf2torch_tensor_name_prefix_tf: str = "seq2seq/decoder",
|
embed_tensor_name_prefix_tf: str = None,
|
):
|
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,
|
)
|
if attention_dim is None:
|
attention_dim = encoder_output_size
|
|
if input_layer == "embed":
|
self.embed = torch.nn.Sequential(
|
torch.nn.Embedding(vocab_size, attention_dim),
|
)
|
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, encoder_output_size=encoder_output_size
|
),
|
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=sanm_shfit
|
),
|
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,
|
),
|
)
|
if concat_embeds:
|
self.embed_concat_ffn = repeat(
|
1,
|
lambda lnum: DecoderLayerSANM(
|
attention_dim + encoder_output_size,
|
None,
|
None,
|
PositionwiseFeedForwardDecoderSANM(attention_dim + encoder_output_size, linear_units, dropout_rate,
|
adim=attention_dim),
|
dropout_rate,
|
normalize_before,
|
concat_after,
|
),
|
)
|
else:
|
self.embed_concat_ffn = None
|
self.concat_embeds = concat_embeds
|
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
|
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
|
self.embed_tensor_name_prefix_tf = embed_tensor_name_prefix_tf
|
|
def forward(
|
self,
|
hs_pad: torch.Tensor,
|
hlens: torch.Tensor,
|
ys_in_pad: torch.Tensor,
|
ys_in_lens: torch.Tensor,
|
chunk_mask: torch.Tensor = None,
|
pre_acoustic_embeds: 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 = self.embed(tgt)
|
|
if pre_acoustic_embeds is not None and self.concat_embeds:
|
x = torch.cat((x, pre_acoustic_embeds), dim=-1)
|
x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)
|
|
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:
|
x = self.after_norm(x)
|
if self.output_layer is not None:
|
x = self.output_layer(x)
|
|
olens = tgt_mask.sum(1)
|
return x, olens
|
|
def score(self, ys, state, x, x_mask=None, pre_acoustic_embeds: torch.Tensor = None, ):
|
"""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), memory_mask=x_mask, pre_acoustic_embeds=pre_acoustic_embeds,
|
cache=state
|
)
|
return logp.squeeze(0), state
|
|
def forward_one_step(
|
self,
|
tgt: torch.Tensor,
|
tgt_mask: torch.Tensor,
|
memory: torch.Tensor,
|
memory_mask: torch.Tensor = None,
|
pre_acoustic_embeds: torch.Tensor = None,
|
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 = tgt[:, -1:]
|
tgt_mask = None
|
x = self.embed(x)
|
|
if pre_acoustic_embeds is not None and self.concat_embeds:
|
x = torch.cat((x, pre_acoustic_embeds), dim=-1)
|
x, _, _, _, _ = self.embed_concat_ffn(x, None, None, None, None)
|
|
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, memory_mask, 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, memory_mask, 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 = self.output_layer(y)
|
y = torch.log_softmax(y, dim=-1)
|
|
return y, new_cache
|
|
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
|
embed_tensor_name_prefix_tf = self.embed_tensor_name_prefix_tf if self.embed_tensor_name_prefix_tf is not None else tensor_name_prefix_tf
|
map_dict_local = {
|
|
## 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)
|
|
# 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)
|
|
# 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)
|
|
# 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,)
|
|
# in embed
|
"{}.embed.0.weight".format(tensor_name_prefix_torch):
|
{"name": "{}/w_embs".format(embed_tensor_name_prefix_tf),
|
"squeeze": None,
|
"transpose": None,
|
}, # (4235,256),(4235,256)
|
|
# out layer
|
"{}.output_layer.weight".format(tensor_name_prefix_torch):
|
{"name": ["{}/dense/kernel".format(tensor_name_prefix_tf),
|
"{}/w_embs".format(embed_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,)
|
|
}
|
return map_dict_local
|
|
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] == "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)
|
|
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] == "decoders3":
|
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))
|
|
elif names[1] == "embed" or names[1] == "output_layer":
|
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))
|
|
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))
|
|
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
|
