Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,811 Bytes
44d8da2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 |
# Copyright 2024 Alibaba DAMO Academy
#
# 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.
import math
import os
import re
import einops
import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.models.layers import LayerNorm, LayerNorm2d
from timm.models.regnet import RegStage
from transformers import TRANSFORMERS_CACHE
def parse_snapshot_folder(repo_id, cache_dir=None, repo_type="model"):
revision = "main"
# 1. parse the downloaded cache folder
if cache_dir is None:
cache_dir = TRANSFORMERS_CACHE
else:
cache_dir = cache_dir
object_id = repo_id.replace("/", "--")
repo_cache = os.path.join(cache_dir, f"{repo_type}s--{object_id}")
# 2. resolve refs (for instance to convert main to the associated commit sha)
refs_dir = os.path.join(repo_cache, "refs")
if os.path.isdir(refs_dir):
revision_file = os.path.join(refs_dir, revision)
if os.path.isfile(revision_file):
with open(revision_file) as f:
revision = f.read()
# 3. acquire the snapshot folder
folder = os.path.join(repo_cache, "snapshots", revision)
return folder
def load_mm_projector(model_path, cache_dir=None, token=None):
if os.path.exists(os.path.join(model_path, 'mm_projector.bin')):
is_local = True
folder = model_path
else:
is_local = False
folder = parse_snapshot_folder(model_path, cache_dir=cache_dir, repo_type="model")
if not os.path.exists(os.path.join(folder, 'mm_projector.bin')):
# downloading from remote repo
from huggingface_hub import snapshot_download
snapshot_download(repo_id=model_path, cache_dir=cache_dir, token=token)
mm_projector_weights = torch.load(os.path.join(folder, 'mm_projector.bin'), map_location='cpu')
mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
return mm_projector_weights
class IdentityMap(nn.Module):
def __init__(self):
super().__init__()
def forward(self, x, *args, **kwargs):
return x
@property
def config(self):
return {"mm_projector_type": 'identity'}
def build_mlp(depth, hidden_size, output_hidden_size):
modules = [nn.Linear(hidden_size, output_hidden_size)]
for _ in range(1, depth):
modules.append(nn.GELU())
modules.append(nn.Linear(output_hidden_size, output_hidden_size))
return nn.Sequential(*modules)
class SimSpatialConv(nn.Module):
def __init__(self, config, downsample=(2, 2), padding=1, depth=1, mlp_depth=2):
super().__init__()
self.encoder_hidden_size = encoder_hidden_size = config.mm_hidden_size
self.output_hidden_size = output_hidden_size = config.hidden_size
self.downsample = downsample
self.padding = padding
self.sampler = nn.Sequential(
nn.Conv2d(
in_channels=self.encoder_hidden_size,
out_channels=4 * self.encoder_hidden_size,
kernel_size=self.downsample,
stride=self.downsample,
padding=self.padding,
bias=True
),
nn.SiLU(),
)
self.readout = build_mlp(mlp_depth, 4 * self.encoder_hidden_size, self.output_hidden_size)
def forward(self, x):
hw = int(x.size(1) ** 0.5)
x = einops.rearrange(x, "b (h w) d -> b d h w", h=hw, w=hw)
x = self.sampler(x)
x = einops.rearrange(x, "b d h w -> b (h w) d")
x = self.readout(x)
return x
def cal_proj_size(self, input_size):
if isinstance(input_size, int):
input_size = (input_size, input_size)
height = math.ceil((input_size[0] + self.padding) / self.downsample[0])
width = math.ceil((input_size[1] + self.padding) / self.downsample[1])
return height * width
class MlpGeluProjector(nn.Module):
def __init__(self, config, projector_type):
super().__init__()
mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type)
mlp_depth = int(mlp_gelu_match.group(1))
self.readout = build_mlp(mlp_depth, config.mm_hidden_size, config.hidden_size)
def forward(self, x):
x = self.readout(x)
return x
def cal_proj_size(self, input_size):
if isinstance(input_size, int):
input_size = (input_size, input_size)
height = input_size[0]
width = input_size[1]
return height * width
def build_vision_projector(config, delay_load=False, **kwargs):
# videollama3 projector only support image-wise operation now, i.e., prohibit the temporal aggregation
projector_type = getattr(config, 'mm_projector_type', 'linear')
if projector_type == "linear":
# NOTE: for both linear and mlp2x_gelu projector type, mean pooling is adopted to aggreate video features
return nn.Linear(config.mm_hidden_size, config.hidden_size)
elif projector_type == "simp_spatial_conv":
return SimSpatialConv(config)
elif projector_type.startswith("mlp"):
return MlpGeluProjector(config, projector_type)
if projector_type == 'identity':
return IdentityMap()
raise ValueError(f'Unknown projector type: {projector_type}')
|