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# Copyright 2024 EPFL and Apple Inc.
#
# 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 Optional
from torch import nn
from einops import rearrange
class BottleneckBlock(nn.Module):
def __init__(self, thin, wide):
super(BottleneckBlock, self).__init__()
self.block = nn.Sequential(
nn.Linear(thin, wide),
nn.GELU(),
nn.Linear(wide, thin)
)
def forward(self, x):
out = self.block(x)
return out
class StandardMLP(nn.Module):
def __init__(self, dim_in, dim_out, widths):
super(StandardMLP, self).__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.widths = widths
self.linear_in = nn.Linear(self.dim_in, self.widths[0])
self.linear_out = nn.Linear(self.widths[-1], self.dim_out)
self.layers = []
self.layer_norms = []
for i in range(len(self.widths) - 1):
self.layers.append(nn.Linear(self.widths[i], self.widths[i + 1]))
self.layer_norms.append(nn.LayerNorm(widths[i + 1]))
self.layers = nn.ModuleList(self.layers)
self.layernorms = nn.ModuleList(self.layer_norms)
def forward(self, x):
# If x is an image, apply MLP point-wise to each token/pixel
if x.ndim == 4:
_, _, H, W = x.shape
x = rearrange(x, 'b d h w -> b (h w) d')
x_is_image = True
else:
x_is_image = False
z = self.linear_in(x)
for layer, norm in zip(self.layers, self.layer_norms):
z = norm(z)
z = layer(z)
out = self.linear_out(z)
# If x was an image, rearrange back to image
if x_is_image:
out = rearrange(out, 'b (h w) d -> b d h w', h=H, w=W)
return out
class BottleneckMLP(nn.Module):
def __init__(self, dim_in, dim_out, block_dims):
super(BottleneckMLP, self).__init__()
self.dim_in = dim_in
self.dim_out = dim_out
self.block_dims = block_dims
self.linear_in = nn.Linear(self.dim_in, self.block_dims[0][1])
self.linear_out = nn.Linear(self.block_dims[-1][1], self.dim_out)
blocks = []
layernorms = []
for block_dim in self.block_dims:
wide, thin = block_dim
blocks.append(BottleneckBlock(thin=thin, wide=wide))
layernorms.append(nn.LayerNorm(thin))
self.blocks = nn.ModuleList(blocks)
self.layernorms = nn.ModuleList(layernorms)
def forward(self, x):
# If x is an image, apply MLP point-wise to each token/pixel
if x.ndim == 4:
_, _, H, W = x.shape
x = rearrange(x, 'b d h w -> b (h w) d')
x_is_image = True
else:
x_is_image = False
x = self.linear_in(x)
for block, norm in zip(self.blocks, self.layernorms):
x = x + block(norm(x))
out = self.linear_out(x)
# If x was an image, rearrange back to image
if x_is_image:
out = rearrange(out, 'b (h w) d -> b d h w', h=H, w=W)
return out
def build_mlp(model_id: str = "BottleneckMLP/B_6-Wi_1024",
dim_in: Optional[int] = None,
dim_out: Optional[int] = None,
**kwargs) -> nn.Module:
"""Constructs an MLP model from a model ID string, see
"Scaling MLPs: A Tale of Inductive Bias" (https://arxiv.org/abs/2306.13575).
Args:
model_id: Model ID string. E.g. "BottleneckMLP/B_6-Wi_1024".
See https://arxiv.org/abs/2306.13575 for options and details.
dim_in: Input dimensionality. If None, defaults to MLP dimension.
dim_out: Output dimensionality. If None, defaults to MLP dimension.
Returns:
MLP model.
"""
model, architecture = model_id.split("/")
assert model in ["BottleneckMLP", "MLP"], f"Model {model} not supported."
sep = architecture.split("-")
num_blocks = int(sep[0].split("_")[1])
thin = int(sep[1].split("_")[1])
# If dim_in and dim_out are not specified, use MLP dim
dim_in = dim_in or thin
dim_out = dim_out or thin
if len(sep) == 3:
expansion_factor = int(sep[2].split("_")[1])
else:
expansion_factor = 4
if model == "BottleneckMLP":
blocks = [[expansion_factor * thin, thin] for _ in range(num_blocks)]
return BottleneckMLP(
dim_in=dim_in,
dim_out=dim_out,
block_dims=blocks,
)
elif model == "MLP":
blocks = [thin for _ in range(num_blocks)]
return StandardMLP(
dim_in=dim_in,
dim_out=dim_out,
widths=blocks,
) |