518 lines
19 KiB
Python
518 lines
19 KiB
Python
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# Copyright 2019 Shigeki Karita
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Decoder definition."""
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from typing import Any
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from typing import List
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from typing import Sequence
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from typing import Tuple
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import torch
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from torch import nn
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from funasr.models.transformer.attention import MultiHeadedAttention
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from funasr.models.transformer.utils.dynamic_conv import DynamicConvolution
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from funasr.models.transformer.utils.dynamic_conv2d import DynamicConvolution2D
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from funasr.models.transformer.embedding import PositionalEncoding
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from funasr.models.transformer.layer_norm import LayerNorm
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from funasr.models.transformer.utils.lightconv import LightweightConvolution
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from funasr.models.transformer.utils.lightconv2d import LightweightConvolution2D
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from funasr.models.transformer.utils.mask import subsequent_mask
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from funasr.models.transformer.utils.nets_utils import make_pad_mask
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from funasr.models.transformer.positionwise_feed_forward import (
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PositionwiseFeedForward, # noqa: H301
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)
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from funasr.models.transformer.utils.repeat import repeat
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from funasr.models.transformer.scorers.scorer_interface import BatchScorerInterface
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from omegaconf import OmegaConf
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from funasr.register import tables
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class LayerNorm(nn.LayerNorm):
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def forward(self, x):
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return super().forward(x.float()).type(x.dtype)
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class DecoderLayer(nn.Module):
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"""Single decoder layer module.
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Args:
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size (int): Input dimension.
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self_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` instance can be used as the argument.
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src_attn (torch.nn.Module): Self-attention module instance.
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`MultiHeadedAttention` instance can be used as the argument.
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feed_forward (torch.nn.Module): Feed-forward module instance.
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`PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
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can be used as the argument.
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dropout_rate (float): Dropout rate.
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normalize_before (bool): Whether to use layer_norm before the first block.
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concat_after (bool): Whether to concat attention layer's input and output.
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if True, additional linear will be applied.
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i.e. x -> x + linear(concat(x, att(x)))
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if False, no additional linear will be applied. i.e. x -> x + att(x)
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"""
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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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layer_id=None,
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args={},
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**kwargs,
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):
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"""Construct an DecoderLayer object."""
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super(DecoderLayer, self).__init__()
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self.size = size
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# self.self_attn = self_attn.to(torch.bfloat16)
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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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self.norm2 = LayerNorm(size)
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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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self.layer_id = layer_id
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if args.get("version", "v4") == "v4":
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from funasr.models.sense_voice.rwkv_v4 import RWKVLayer
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from funasr.models.sense_voice.rwkv_v4 import RWKV_TimeMix as RWKV_Tmix
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elif args.get("version", "v5") == "v5":
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from funasr.models.sense_voice.rwkv_v5 import RWKVLayer
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from funasr.models.sense_voice.rwkv_v5 import RWKV_Tmix_x052 as RWKV_Tmix
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else:
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from funasr.models.sense_voice.rwkv_v6 import RWKVLayer
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from funasr.models.sense_voice.rwkv_v6 import RWKV_Tmix_x060 as RWKV_Tmix
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# self.attn = RWKVLayer(args=args, layer_id=layer_id)
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self.self_attn = RWKV_Tmix(args, layer_id=layer_id)
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self.args = args
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self.ln0 = None
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if self.layer_id == 0 and not args.get("ln0", True):
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self.ln0 = LayerNorm(args.n_embd)
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if args.get("init_rwkv", True):
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print("init_rwkv")
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layer_id = 0
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scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
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nn.init.constant_(self.ln0.weight, scale)
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# init
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if args.get("init_rwkv", True):
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print("init_rwkv")
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scale = ((1 + layer_id) / args.get("n_layer")) ** 0.7
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nn.init.constant_(self.norm1.weight, scale)
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# nn.init.constant_(self.self_attn.ln2.weight, scale)
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if args.get("init_rwkv", True):
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print("init_rwkv")
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nn.init.orthogonal_(self.self_attn.receptance.weight, gain=1)
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nn.init.orthogonal_(self.self_attn.key.weight, gain=0.1)
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nn.init.orthogonal_(self.self_attn.value.weight, gain=1)
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nn.init.orthogonal_(self.self_attn.gate.weight, gain=0.1)
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nn.init.zeros_(self.self_attn.output.weight)
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if args.get("datatype", "bf16") == "bf16":
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self.self_attn.to(torch.bfloat16)
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# self.norm1.to(torch.bfloat16)
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def forward(self, tgt, tgt_mask, memory, memory_mask, cache=None):
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"""Compute decoded features.
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Args:
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tgt (torch.Tensor): Input tensor (#batch, maxlen_out, size).
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tgt_mask (torch.Tensor): Mask for input tensor (#batch, maxlen_out).
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memory (torch.Tensor): Encoded memory, float32 (#batch, maxlen_in, size).
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memory_mask (torch.Tensor): Encoded memory mask (#batch, maxlen_in).
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cache (List[torch.Tensor]): List of cached tensors.
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Each tensor shape should be (#batch, maxlen_out - 1, size).
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Returns:
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torch.Tensor: Output tensor(#batch, maxlen_out, size).
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torch.Tensor: Mask for output tensor (#batch, maxlen_out).
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torch.Tensor: Encoded memory (#batch, maxlen_in, size).
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torch.Tensor: Encoded memory mask (#batch, maxlen_in).
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"""
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if self.layer_id == 0 and self.ln0 is not None:
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tgt = self.ln0(tgt)
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if self.args.get("datatype", "bf16") == "bf16":
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tgt = tgt.bfloat16()
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residual = tgt
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tgt = self.norm1(tgt)
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if cache is None:
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x = residual + self.dropout(self.self_attn(tgt, mask=tgt_mask))
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else:
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# tgt_q = tgt[:, -1:, :]
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# residual_q = residual[:, -1:, :]
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tgt_q_mask = None
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x = residual + self.dropout(self.self_attn(tgt, mask=tgt_q_mask))
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x = x[:, -1, :]
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if self.args.get("datatype", "bf16") == "bf16":
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x = x.to(torch.float32)
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# x = residual + self.dropout(self.self_attn(tgt_q, tgt, tgt, tgt_q_mask))
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residual = x
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x = self.norm2(x)
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x = residual + self.dropout(self.src_attn(x, memory, memory, memory_mask))
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residual = x
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x = self.norm3(x)
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x = residual + self.dropout(self.feed_forward(x))
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if cache is not None:
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x = torch.cat([cache, x], dim=1)
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return x, tgt_mask, memory, memory_mask
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class BaseTransformerDecoder(nn.Module, BatchScorerInterface):
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"""Base class of Transfomer decoder module.
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Args:
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vocab_size: output dim
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encoder_output_size: dimension of attention
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attention_heads: the number of heads of multi head attention
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linear_units: the number of units of position-wise feed forward
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num_blocks: the number of decoder blocks
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dropout_rate: dropout rate
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self_attention_dropout_rate: dropout rate for attention
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input_layer: input layer type
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use_output_layer: whether to use output layer
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pos_enc_class: PositionalEncoding or ScaledPositionalEncoding
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normalize_before: whether to use layer_norm before the first block
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concat_after: whether to concat attention layer's input and output
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if True, additional linear will be applied.
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i.e. x -> x + linear(concat(x, att(x)))
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if False, no additional linear will be applied.
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i.e. x -> x + att(x)
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"""
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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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dropout_rate: float = 0.1,
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positional_dropout_rate: float = 0.1,
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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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):
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super().__init__()
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attention_dim = encoder_output_size
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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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# Must set by the inheritance
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self.decoders = None
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def forward(
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self,
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hs_pad: torch.Tensor,
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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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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Forward decoder.
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Args:
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hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
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hlens: (batch)
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ys_in_pad:
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input token ids, int64 (batch, maxlen_out)
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if input_layer == "embed"
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input tensor (batch, maxlen_out, #mels) in the other cases
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ys_in_lens: (batch)
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Returns:
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(tuple): tuple containing:
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x: decoded token score before softmax (batch, maxlen_out, token)
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if use_output_layer is True,
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olens: (batch, )
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"""
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tgt = ys_in_pad
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# tgt_mask: (B, 1, L)
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tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device)
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# m: (1, L, L)
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m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0)
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# tgt_mask: (B, L, L)
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tgt_mask = tgt_mask & m
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memory = hs_pad
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memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to(memory.device)
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# Padding for Longformer
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if memory_mask.shape[-1] != memory.shape[1]:
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padlen = memory.shape[1] - memory_mask.shape[-1]
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memory_mask = torch.nn.functional.pad(memory_mask, (0, padlen), "constant", False)
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x = self.embed(tgt)
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x, tgt_mask, memory, memory_mask = self.decoders(x, tgt_mask, memory, memory_mask)
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if self.normalize_before:
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x = self.after_norm(x)
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if self.output_layer is not None:
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x = self.output_layer(x)
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olens = tgt_mask.sum(1)
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return x, olens
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def forward_one_step(
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self,
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tgt: torch.Tensor,
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tgt_mask: torch.Tensor,
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memory: torch.Tensor,
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cache: List[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, List[torch.Tensor]]:
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"""Forward one step.
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Args:
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tgt: input token ids, int64 (batch, maxlen_out)
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tgt_mask: input token mask, (batch, maxlen_out)
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dtype=torch.uint8 in PyTorch 1.2-
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dtype=torch.bool in PyTorch 1.2+ (include 1.2)
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memory: encoded memory, float32 (batch, maxlen_in, feat)
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cache: cached output list of (batch, max_time_out-1, size)
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Returns:
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y, cache: NN output value and cache per `self.decoders`.
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y.shape` is (batch, maxlen_out, token)
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"""
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x = self.embed(tgt)
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if cache is None:
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cache = [None] * len(self.decoders)
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new_cache = []
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for c, decoder in zip(cache, self.decoders):
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x, tgt_mask, memory, memory_mask = decoder(x, tgt_mask, memory, None, cache=c)
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new_cache.append(x)
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if self.normalize_before:
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y = self.after_norm(x[:, -1])
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else:
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y = x[:, -1]
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if self.output_layer is not None:
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y = torch.log_softmax(self.output_layer(y), dim=-1)
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return y, new_cache
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def score(self, ys, state, x):
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"""Score."""
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ys_mask = subsequent_mask(len(ys), device=x.device).unsqueeze(0)
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logp, state = self.forward_one_step(ys.unsqueeze(0), ys_mask, x.unsqueeze(0), cache=state)
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return logp.squeeze(0), state
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|
|
def batch_score(
|
||
|
|
self, ys: torch.Tensor, states: List[Any], xs: torch.Tensor
|
||
|
|
) -> Tuple[torch.Tensor, List[Any]]:
|
||
|
|
"""Score new token batch.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen).
|
||
|
|
states (List[Any]): Scorer states for prefix tokens.
|
||
|
|
xs (torch.Tensor):
|
||
|
|
The encoder feature that generates ys (n_batch, xlen, n_feat).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
tuple[torch.Tensor, List[Any]]: Tuple of
|
||
|
|
batchfied scores for next token with shape of `(n_batch, n_vocab)`
|
||
|
|
and next state list for ys.
|
||
|
|
|
||
|
|
"""
|
||
|
|
# merge states
|
||
|
|
n_batch = len(ys)
|
||
|
|
n_layers = len(self.decoders)
|
||
|
|
if states[0] is None:
|
||
|
|
batch_state = None
|
||
|
|
else:
|
||
|
|
# transpose state of [batch, layer] into [layer, batch]
|
||
|
|
batch_state = [
|
||
|
|
torch.stack([states[b][i] for b in range(n_batch)]) for i in range(n_layers)
|
||
|
|
]
|
||
|
|
|
||
|
|
# batch decoding
|
||
|
|
ys_mask = subsequent_mask(ys.size(-1), device=xs.device).unsqueeze(0)
|
||
|
|
logp, states = self.forward_one_step(ys, ys_mask, xs, cache=batch_state)
|
||
|
|
|
||
|
|
# transpose state of [layer, batch] into [batch, layer]
|
||
|
|
state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)]
|
||
|
|
return logp, state_list
|
||
|
|
|
||
|
|
|
||
|
|
@tables.register("decoder_classes", "TransformerRWKVDecoder")
|
||
|
|
class TransformerRWKVDecoder(BaseTransformerDecoder):
|
||
|
|
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,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
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,
|
||
|
|
)
|
||
|
|
# from funasr.models.sense_voice.rwkv_v6 import RWKVLayer
|
||
|
|
|
||
|
|
rwkv_cfg = kwargs.get("rwkv_cfg", {})
|
||
|
|
args = OmegaConf.create(rwkv_cfg)
|
||
|
|
|
||
|
|
attention_dim = encoder_output_size
|
||
|
|
self.decoders = repeat(
|
||
|
|
num_blocks,
|
||
|
|
lambda lnum: DecoderLayer(
|
||
|
|
attention_dim,
|
||
|
|
MultiHeadedAttention(attention_heads, attention_dim, src_attention_dropout_rate),
|
||
|
|
PositionwiseFeedForward(attention_dim, linear_units, dropout_rate),
|
||
|
|
dropout_rate,
|
||
|
|
normalize_before,
|
||
|
|
concat_after,
|
||
|
|
lnum,
|
||
|
|
args=args,
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
# init
|
||
|
|
if args.get("init_rwkv", True):
|
||
|
|
print("init_rwkv")
|
||
|
|
nn.init.uniform_(self.embed[0].weight, a=-1e-4, b=1e-4)
|
||
|
|
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
hs_pad: torch.Tensor,
|
||
|
|
hlens: torch.Tensor,
|
||
|
|
ys_in_pad: torch.Tensor,
|
||
|
|
ys_in_lens: torch.Tensor,
|
||
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||
|
|
"""Forward decoder.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
hs_pad: encoded memory, float32 (batch, maxlen_in, feat)
|
||
|
|
hlens: (batch)
|
||
|
|
ys_in_pad:
|
||
|
|
input token ids, int64 (batch, maxlen_out)
|
||
|
|
if input_layer == "embed"
|
||
|
|
input tensor (batch, maxlen_out, #mels) in the other cases
|
||
|
|
ys_in_lens: (batch)
|
||
|
|
Returns:
|
||
|
|
(tuple): tuple containing:
|
||
|
|
|
||
|
|
x: decoded token score before softmax (batch, maxlen_out, token)
|
||
|
|
if use_output_layer is True,
|
||
|
|
olens: (batch, )
|
||
|
|
"""
|
||
|
|
tgt = ys_in_pad
|
||
|
|
# tgt_mask: (B, 1, L)
|
||
|
|
tgt_mask = (~make_pad_mask(ys_in_lens)[:, None, :]).to(tgt.device)
|
||
|
|
# m: (1, L, L)
|
||
|
|
m = subsequent_mask(tgt_mask.size(-1), device=tgt_mask.device).unsqueeze(0)
|
||
|
|
# tgt_mask: (B, L, L)
|
||
|
|
tgt_mask = tgt_mask & m
|
||
|
|
|
||
|
|
memory = hs_pad
|
||
|
|
memory_mask = (~make_pad_mask(hlens, maxlen=memory.size(1)))[:, None, :].to(memory.device)
|
||
|
|
# Padding for Longformer
|
||
|
|
if memory_mask.shape[-1] != memory.shape[1]:
|
||
|
|
padlen = memory.shape[1] - memory_mask.shape[-1]
|
||
|
|
memory_mask = torch.nn.functional.pad(memory_mask, (0, padlen), "constant", False)
|
||
|
|
|
||
|
|
x = self.embed(tgt)
|
||
|
|
x, tgt_mask, memory, memory_mask = self.decoders(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 forward_one_step(
|
||
|
|
self,
|
||
|
|
tgt: torch.Tensor,
|
||
|
|
tgt_mask: torch.Tensor,
|
||
|
|
memory: torch.Tensor,
|
||
|
|
cache: List[torch.Tensor] = None,
|
||
|
|
) -> Tuple[torch.Tensor, List[torch.Tensor]]:
|
||
|
|
"""Forward one step.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
tgt: input token ids, int64 (batch, maxlen_out)
|
||
|
|
tgt_mask: input token mask, (batch, maxlen_out)
|
||
|
|
dtype=torch.uint8 in PyTorch 1.2-
|
||
|
|
dtype=torch.bool in PyTorch 1.2+ (include 1.2)
|
||
|
|
memory: encoded memory, float32 (batch, maxlen_in, feat)
|
||
|
|
cache: cached output list of (batch, max_time_out-1, size)
|
||
|
|
Returns:
|
||
|
|
y, cache: NN output value and cache per `self.decoders`.
|
||
|
|
y.shape` is (batch, maxlen_out, token)
|
||
|
|
"""
|
||
|
|
x = self.embed(tgt)
|
||
|
|
if cache is None:
|
||
|
|
cache = [None] * len(self.decoders)
|
||
|
|
new_cache = []
|
||
|
|
for c, decoder in zip(cache, self.decoders):
|
||
|
|
x, tgt_mask, memory, memory_mask = decoder(x, tgt_mask, memory, None, cache=c)
|
||
|
|
new_cache.append(x)
|
||
|
|
|
||
|
|
if self.normalize_before:
|
||
|
|
y = self.after_norm(x[:, -1])
|
||
|
|
else:
|
||
|
|
y = x[:, -1]
|
||
|
|
if self.output_layer is not None:
|
||
|
|
y = torch.log_softmax(self.output_layer(y), dim=-1)
|
||
|
|
|
||
|
|
return y, new_cache
|