82 lines
2.7 KiB
Python
82 lines
2.7 KiB
Python
# Copyright (c) 2025 ByteDance Ltd. and/or its affiliates
|
|
#
|
|
# 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.
|
|
|
|
|
|
from typing import Callable
|
|
from torch import Tensor, nn
|
|
|
|
from .attention import Attention, LayerScale, Mlp
|
|
|
|
|
|
class Block(nn.Module):
|
|
def __init__(
|
|
self,
|
|
dim: int,
|
|
num_heads: int,
|
|
mlp_ratio: float = 4.0,
|
|
qkv_bias: bool = True,
|
|
proj_bias: bool = True,
|
|
ffn_bias: bool = True,
|
|
drop: float = 0.0,
|
|
attn_drop: float = 0.0,
|
|
init_values=None,
|
|
drop_path: float = 0.0,
|
|
act_layer: Callable[..., nn.Module] = nn.GELU,
|
|
norm_layer: Callable[..., nn.Module] = nn.LayerNorm,
|
|
attn_class: Callable[..., nn.Module] = Attention,
|
|
ffn_layer: Callable[..., nn.Module] = Mlp,
|
|
qk_norm: bool = False,
|
|
rope=None,
|
|
) -> None:
|
|
super().__init__()
|
|
|
|
self.norm1 = norm_layer(dim)
|
|
|
|
self.attn = attn_class(
|
|
dim,
|
|
num_heads=num_heads,
|
|
qkv_bias=qkv_bias,
|
|
proj_bias=proj_bias,
|
|
attn_drop=attn_drop,
|
|
proj_drop=drop,
|
|
qk_norm=qk_norm,
|
|
rope=rope,
|
|
)
|
|
|
|
self.ls1 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
|
|
self.norm2 = norm_layer(dim)
|
|
mlp_hidden_dim = int(dim * mlp_ratio)
|
|
self.mlp = ffn_layer(
|
|
in_features=dim,
|
|
hidden_features=mlp_hidden_dim,
|
|
act_layer=act_layer,
|
|
drop=drop,
|
|
bias=ffn_bias,
|
|
)
|
|
self.ls2 = LayerScale(dim, init_values=init_values) if init_values else nn.Identity()
|
|
|
|
self.sample_drop_ratio = 0.0 # Equivalent to always having drop_path=0
|
|
|
|
def forward(self, x: Tensor, pos=None, attn_mask=None) -> Tensor:
|
|
def attn_residual_func(x: Tensor, pos=None, attn_mask=None) -> Tensor:
|
|
return self.ls1(self.attn(self.norm1(x), pos=pos, attn_mask=attn_mask))
|
|
|
|
def ffn_residual_func(x: Tensor) -> Tensor:
|
|
return self.ls2(self.mlp(self.norm2(x)))
|
|
|
|
# drop_path is always 0, so always take the else branch
|
|
x = x + attn_residual_func(x, pos=pos, attn_mask=attn_mask)
|
|
x = x + ffn_residual_func(x)
|
|
return x
|