from typing import List
|
from typing import Tuple
|
import logging
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import torch
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import torch.nn as nn
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import numpy as np
|
|
from funasr.modules.streaming_utils import utils as myutils
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from funasr.models.decoder.transformer_decoder import BaseTransformerDecoder
|
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from funasr.modules.attention import MultiHeadedAttentionSANMDecoder, MultiHeadedAttentionCrossAtt
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from funasr.modules.embedding import PositionalEncoding
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from funasr.modules.layer_norm import LayerNorm
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from funasr.modules.positionwise_feed_forward import PositionwiseFeedForwardDecoderSANM
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from funasr.modules.repeat import repeat
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from funasr.models.decoder.sanm_decoder import DecoderLayerSANM, ParaformerSANMDecoder
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|
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class ContextualDecoderLayer(nn.Module):
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def __init__(
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self,
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size,
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self_attn,
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src_attn,
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feed_forward,
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dropout_rate,
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normalize_before=True,
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concat_after=False,
|
):
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"""Construct an DecoderLayer object."""
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super(ContextualDecoderLayer, self).__init__()
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self.size = size
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self.self_attn = self_attn
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self.src_attn = src_attn
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self.feed_forward = feed_forward
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self.norm1 = LayerNorm(size)
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if self_attn is not None:
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self.norm2 = LayerNorm(size)
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if src_attn is not None:
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self.norm3 = LayerNorm(size)
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self.dropout = nn.Dropout(dropout_rate)
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self.normalize_before = normalize_before
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self.concat_after = concat_after
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if self.concat_after:
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self.concat_linear1 = nn.Linear(size + size, size)
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self.concat_linear2 = nn.Linear(size + size, size)
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def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None,):
|
# tgt = self.dropout(tgt)
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if isinstance(tgt, Tuple):
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tgt, _ = tgt
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residual = tgt
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if self.normalize_before:
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tgt = self.norm1(tgt)
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tgt = self.feed_forward(tgt)
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x = tgt
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if self.normalize_before:
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tgt = self.norm2(tgt)
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if self.training:
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cache = None
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x, cache = self.self_attn(tgt, tgt_mask, cache=cache)
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x = residual + self.dropout(x)
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x_self_attn = x
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residual = x
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if self.normalize_before:
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x = self.norm3(x)
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x = self.src_attn(x, memory, memory_mask)
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x_src_attn = x
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x = residual + self.dropout(x)
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return x, tgt_mask, x_self_attn, x_src_attn
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|
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class ContextualBiasDecoder(nn.Module):
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def __init__(
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self,
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size,
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src_attn,
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dropout_rate,
|
normalize_before=True,
|
):
|
"""Construct an DecoderLayer object."""
|
super(ContextualBiasDecoder, self).__init__()
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self.size = size
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self.src_attn = src_attn
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if src_attn is not None:
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self.norm3 = LayerNorm(size)
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self.dropout = nn.Dropout(dropout_rate)
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self.normalize_before = normalize_before
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def forward(self, tgt, tgt_mask, memory, memory_mask=None, cache=None):
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x = tgt
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if self.src_attn is not None:
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if self.normalize_before:
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x = self.norm3(x)
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x = self.dropout(self.src_attn(x, memory, memory_mask))
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return x, tgt_mask, memory, memory_mask, cache
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|
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class ContextualParaformerDecoder(ParaformerSANMDecoder):
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"""
|
Author: Speech Lab of DAMO Academy, Alibaba Group
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2006.01713
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"""
|
def __init__(
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self,
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vocab_size: int,
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encoder_output_size: int,
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attention_heads: int = 4,
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linear_units: int = 2048,
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num_blocks: int = 6,
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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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self_attention_dropout_rate: float = 0.0,
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src_attention_dropout_rate: float = 0.0,
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input_layer: str = "embed",
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use_output_layer: bool = True,
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pos_enc_class=PositionalEncoding,
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normalize_before: bool = True,
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concat_after: bool = False,
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att_layer_num: int = 6,
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kernel_size: int = 21,
|
sanm_shfit: int = 0,
|
):
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super().__init__(
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vocab_size=vocab_size,
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encoder_output_size=encoder_output_size,
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dropout_rate=dropout_rate,
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positional_dropout_rate=positional_dropout_rate,
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input_layer=input_layer,
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use_output_layer=use_output_layer,
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pos_enc_class=pos_enc_class,
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normalize_before=normalize_before,
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)
|
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attention_dim = encoder_output_size
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if input_layer == 'none':
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self.embed = None
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if input_layer == "embed":
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self.embed = torch.nn.Sequential(
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torch.nn.Embedding(vocab_size, attention_dim),
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# pos_enc_class(attention_dim, positional_dropout_rate),
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)
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elif input_layer == "linear":
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self.embed = torch.nn.Sequential(
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torch.nn.Linear(vocab_size, attention_dim),
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torch.nn.LayerNorm(attention_dim),
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torch.nn.Dropout(dropout_rate),
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torch.nn.ReLU(),
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pos_enc_class(attention_dim, positional_dropout_rate),
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)
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else:
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raise ValueError(f"only 'embed' or 'linear' is supported: {input_layer}")
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self.normalize_before = normalize_before
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if self.normalize_before:
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self.after_norm = LayerNorm(attention_dim)
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if use_output_layer:
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self.output_layer = torch.nn.Linear(attention_dim, vocab_size)
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else:
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self.output_layer = None
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self.att_layer_num = att_layer_num
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self.num_blocks = num_blocks
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if sanm_shfit is None:
|
sanm_shfit = (kernel_size - 1) // 2
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self.decoders = repeat(
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att_layer_num - 1,
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lambda lnum: DecoderLayerSANM(
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attention_dim,
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MultiHeadedAttentionSANMDecoder(
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attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
|
),
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MultiHeadedAttentionCrossAtt(
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attention_heads, attention_dim, src_attention_dropout_rate
|
),
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PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
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dropout_rate,
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normalize_before,
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concat_after,
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),
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)
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self.dropout = nn.Dropout(dropout_rate)
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self.bias_decoder = ContextualBiasDecoder(
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size=attention_dim,
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src_attn=MultiHeadedAttentionCrossAtt(
|
attention_heads, attention_dim, src_attention_dropout_rate
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),
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dropout_rate=dropout_rate,
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normalize_before=True,
|
)
|
self.bias_output = torch.nn.Conv1d(attention_dim*2, attention_dim, 1, bias=False)
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self.last_decoder = ContextualDecoderLayer(
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attention_dim,
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MultiHeadedAttentionSANMDecoder(
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attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=sanm_shfit
|
),
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MultiHeadedAttentionCrossAtt(
|
attention_heads, attention_dim, src_attention_dropout_rate
|
),
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PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
|
dropout_rate,
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normalize_before,
|
concat_after,
|
)
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if num_blocks - att_layer_num <= 0:
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self.decoders2 = None
|
else:
|
self.decoders2 = repeat(
|
num_blocks - att_layer_num,
|
lambda lnum: DecoderLayerSANM(
|
attention_dim,
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MultiHeadedAttentionSANMDecoder(
|
attention_dim, self_attention_dropout_rate, kernel_size, sanm_shfit=0
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),
|
None,
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PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
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dropout_rate,
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normalize_before,
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concat_after,
|
),
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)
|
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self.decoders3 = repeat(
|
1,
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lambda lnum: DecoderLayerSANM(
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attention_dim,
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None,
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None,
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PositionwiseFeedForwardDecoderSANM(attention_dim, linear_units, dropout_rate),
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dropout_rate,
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normalize_before,
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concat_after,
|
),
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)
|
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def forward(
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self,
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hs_pad: torch.Tensor,
|
hlens: torch.Tensor,
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ys_in_pad: torch.Tensor,
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ys_in_lens: torch.Tensor,
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contextual_info: torch.Tensor,
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return_hidden: bool = False,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Forward decoder.
|
|
Args:
|
hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
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hlens: (batch)
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ys_in_pad:
|
input token ids, int64 (batch, maxlen_out)
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if input_layer == "embed"
|
input tensor (batch, maxlen_out, #mels) in the other cases
|
ys_in_lens: (batch)
|
Returns:
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(tuple): tuple containing:
|
|
x: decoded token score before softmax (batch, maxlen_out, token)
|
if use_output_layer is True,
|
olens: (batch, )
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"""
|
tgt = ys_in_pad
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tgt_mask = myutils.sequence_mask(ys_in_lens, device=tgt.device)[:, :, None]
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memory = hs_pad
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memory_mask = myutils.sequence_mask(hlens, device=memory.device)[:, None, :]
|
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x = tgt
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x, tgt_mask, memory, memory_mask, _ = self.decoders(
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x, tgt_mask, memory, memory_mask
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)
|
_, _, x_self_attn, x_src_attn = self.last_decoder(
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x, tgt_mask, memory, memory_mask
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)
|
|
# contextual paraformer related
|
contextual_length = torch.Tensor([contextual_info.shape[1]]).int().repeat(hs_pad.shape[0])
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contextual_mask = myutils.sequence_mask(contextual_length, device=memory.device)[:, None, :]
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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], dim=2)
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x = self.bias_output(x.transpose(1, 2)).transpose(1, 2) # 2D -> D
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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.decoders3(
|
x, tgt_mask, memory, memory_mask
|
)
|
if self.normalize_before:
|
x = self.after_norm(x)
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olens = tgt_mask.sum(1)
|
if self.output_layer is not None and return_hidden is False:
|
x = self.output_layer(x)
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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 = {
|
|
## decoder
|
# ffn
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"{}.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(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(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,)
|
|
## 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)
|
|
}
|
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] == "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))
|
|
|
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] == "bias_decoder":
|
name_q = name
|
|
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))
|
|
|
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))
|
|
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
|
