#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import torch
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from typing import List, Optional, Tuple
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class FeedForward(torch.nn.Module):
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"""FeedForward module definition.
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Args:
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size: Input/Output size.
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hidden_size: Hidden size.
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block_id: Block index.
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num_blocks: Number of blocks in the architecture.
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"""
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def __init__(
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self, size: int, hidden_size: int, block_id: int, dropout_rate: float, num_blocks: int
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) -> None:
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"""Construct a FeedForward object."""
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super().__init__()
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self.time_shift = torch.nn.ZeroPad2d((0, 0, 1, -1))
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self.time_mix_key = torch.nn.Parameter(torch.empty(1, 1, size))
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self.time_mix_receptance = torch.nn.Parameter(torch.empty(1, 1, size))
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self.proj_key = torch.nn.Linear(size, hidden_size, bias=True)
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self.proj_value = torch.nn.Linear(hidden_size, size, bias=True)
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self.proj_receptance = torch.nn.Linear(size, size, bias=True)
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self.block_id = block_id
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self.reset_parameters(size, block_id, num_blocks)
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self.dropout = torch.nn.Dropout(p=dropout_rate)
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def reset_parameters(self, size: int, block_id: int, num_blocks: int) -> None:
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"""Reset module parameters.
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Args:
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size: Block size.
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block_id: Block index.
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num_blocks: Number of blocks in the architecture.
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"""
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ratio_1_to_almost0 = 1.0 - (block_id / num_blocks)
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time_weight = torch.ones(1, 1, size)
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for i in range(size):
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time_weight[0, 0, i] = i / size
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with torch.no_grad():
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self.time_mix_key.data = torch.pow(time_weight, ratio_1_to_almost0)
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self.time_mix_receptance.data = torch.pow(time_weight, ratio_1_to_almost0)
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def forward(
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self, x: torch.Tensor, state: Optional[List[torch.Tensor]] = None
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) -> Tuple[torch.Tensor, Optional[List[torch.Tensor]]]:
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"""Compute channel mixing.
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Args:
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x: FeedForward input sequences. (B, U, size)
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state: Decoder hidden state. [5 x (B, 1, size, N)]
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Returns:
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x: FeedForward output sequences. (B, U, size)
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state: Decoder hidden state. [5 x (B, 1, size, N)]
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"""
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shifted_x = (
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self.time_shift(x) if state is None else state[0][..., self.block_id]
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)
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key = x * self.time_mix_key + shifted_x * (1 - self.time_mix_key)
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receptance = x * self.time_mix_receptance + shifted_x * (
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1 - self.time_mix_receptance
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)
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key = torch.square(torch.relu(self.proj_key(key)))
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value = self.proj_value(self.dropout(key))
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receptance = torch.sigmoid(self.proj_receptance(receptance))
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if state is not None:
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state[0][..., self.block_id] = x
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x = receptance * value
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return x, state
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