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# Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Positionwise feed forward layer definition."""
import torch
class PositionwiseFeedForward(torch.nn.Module):
"""Positionwise feed forward layer.
FeedForward are appied on each position of the sequence.
The output dim is same with the input dim.
Args:
idim (int): Input dimenstion.
hidden_units (int): The number of hidden units.
dropout_rate (float): Dropout rate.
activation (torch.nn.Module): Activation function
"""
def __init__(
self,
idim: int,
hidden_units: int,
dropout_rate: float,
activation: torch.nn.Module = torch.nn.ReLU(),
bias: bool = True,
*dummy_args,
**dummy_kwargs,
):
"""Construct a PositionwiseFeedForward object."""
super(PositionwiseFeedForward, self).__init__()
self.w_1 = torch.nn.Linear(idim, hidden_units, bias=bias)
self.activation = activation
self.dropout = torch.nn.Dropout(dropout_rate)
self.w_2 = torch.nn.Linear(hidden_units, idim, bias=bias)
def forward(self, xs: torch.Tensor) -> torch.Tensor:
"""Forward function.
Args:
xs: input tensor (B, L, D)
Returns:
output tensor, (B, L, D)
"""
return self.w_2(self.dropout(self.activation(self.w_1(xs))))
class MoEFFNLayer(torch.nn.Module):
"""
Mixture of expert with Positionwise feed forward layer
See also figure 1 in https://arxiv.org/pdf/2305.15663.pdf
The output dim is same with the input dim.
Modified from https://github.com/Lightning-AI/lit-gpt/pull/823
https://github.com/mistralai/mistral-src/blob/b46d6/moe_one_file_ref.py#L203-L219
Args:
n_expert: number of expert.
n_expert_activated: The actual number of experts used for each frame
idim (int): Input dimenstion.
hidden_units (int): The number of hidden units.
dropout_rate (float): Dropout rate.
activation (torch.nn.Module): Activation function
"""
def __init__(
self,
idim: int,
hidden_units: int,
dropout_rate: float,
activation: torch.nn.Module = torch.nn.ReLU(),
bias: bool = False,
n_expert: int = 8,
n_expert_activated: int = 2,
):
super(MoEFFNLayer, self).__init__()
self.gate = torch.nn.Linear(idim, n_expert, bias=False)
self.experts = torch.nn.ModuleList(
PositionwiseFeedForward(
idim, hidden_units, dropout_rate, activation, bias=bias)
for _ in range(n_expert))
self.n_expert = n_expert
self.n_expert_activated = n_expert_activated
def forward(self, xs: torch.Tensor) -> torch.Tensor:
"""Foward function.
Args:
xs: input tensor (B, L, D)
Returns:
output tensor, (B, L, D)
"""
B, L, D = xs.size(
) # batch size, sequence length, embedding dimension (idim)
xs = xs.view(-1, D) # (B*L, D)
router = self.gate(xs) # (B*L, n_expert)
logits, selected_experts = torch.topk(
router, self.n_expert_activated
) # probs:(B*L, n_expert_activated), selected_exp: (B*L, n_expert_activated)
weights = torch.nn.functional.softmax(
logits, dim=1,
dtype=torch.float).to(dtype=xs.dtype) # (B*L, n_expert_activated)
output = torch.zeros_like(xs) # (B*L, D)
for i, expert in enumerate(self.experts):
mask = selected_experts == i
token_ids, ith_expert = torch.where(mask)
output[token_ids] += weights[token_ids, ith_expert, None] * expert(
xs[token_ids])
return output.view(B, L, D)
class GatedVariantsMLP(torch.nn.Module):
""" https://arxiv.org/pdf/2002.05202.pdf
"""
def __init__(
self,
idim: int,
hidden_units: int,
dropout_rate: float,
activation: torch.nn.Module = torch.nn.GELU(),
bias: bool = True,
*dummy_args,
**dummy_kwargs,
):
"""Construct a PositionwiseFeedForward object."""
super(GatedVariantsMLP, self).__init__()
self.gate = torch.nn.Linear(idim, hidden_units, bias=False)
self.activation = activation
# w_1 as up proj
self.w_1 = torch.nn.Linear(idim, hidden_units, bias=bias)
self.dropout = torch.nn.Dropout(dropout_rate)
# w_2 as down proj
self.w_2 = torch.nn.Linear(hidden_units, idim, bias=bias)
def forward(self, x) -> torch.Tensor:
"""Foward function.
Args:
xs: input tensor (B, L, D)
Returns:
output tensor, (B, L, D)
"""
gate = self.activation(self.gate(x))
up = self.w_1(x)
fuse = gate * up
return self.w_2(self.dropout(fuse))
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